Migrate to Bedrock (Assess + Execute)

Single-command AI migration: OpenAI / Gemini / Anthropic → Amazon Bedrock.

Requires the gcp-to-aws OR the azure-to-aws skill installed alongside this one. This skill has no standalone Assess implementation — Phase A below runs Assess by directly reading and executing whichever of those two skills is present's own phase instruction files in this same session (the same inline-execution pattern agent-advisor uses for its migration-plan phase: no cross-skill tool call, no turn boundary, and no dependency on your agent supporting a Skill/subagent dispatch mechanism — reading a file works on any agent). There is no fallback path that performs Assess itself if neither sibling is present. If you installed this skill on its own (e.g. a single-skill npx skills add), install gcp-to-aws or azure-to-aws too before using it.

The skill base directory is given in the "Base directory for this skill: X" line the harness emits at load time. Call it <SKILL_BASE>. Derived paths:

• $SCRIPTS = <SKILL_BASE>/scripts
• $HELPERS = <SKILL_BASE>/references/helpers (the former helper skills, now references)
• $ASSESS_SKILL / $ASSESS_BASE — resolved once, in Step 0b: $ASSESS_SKILL is whichever of gcp-to-aws / azure-to-aws is installed alongside this skill (gcp-to-aws preferred when both are present), and $ASSESS_BASE = <SKILL_BASE>/../$ASSESS_SKILL — that sibling skill's own directory. Phase A reads its instruction files directly off this path; every relative reference inside an $ASSESS_SKILL file (references/shared/..., references/phases/..., etc.) resolves under $ASSESS_BASE, exactly as it would if $ASSESS_SKILL were running standalone.

---

Optional usage telemetry

Before starting or resuming, load references/vendored/telemetry/PROTOCOL.md and run its read-only status check. Use the returned reporting mode rather than the model's identity. Complete the existing notice exchange only when that protocol requires it; unavailable or declined telemetry never blocks this skill.

Step 0 — Check prerequisites

0a. Check that uv is available

uv --version 2>/dev/null || echo "MISSING"

If missing: "Install uv first — see the official install guide: https://docs.astral.sh/uv/getting-started/installation/ (e.g. brew install uv or pipx install uv)". Stop.

0a-bis. Route: full migration vs. access-only

This skill has two modes. Decide which the user wants before the Assess-sibling check (0b) — the access-only mode does not use gcp-to-aws/azure-to-aws at all, so requiring one of them there would block a user who only wants access enablement.

Route to Access-only mode (jump to the "## Access-only mode" section below, skip 0b and Steps 1+) when either is true:

• $ARGUMENTS contains an explicit access-request phrase — model access, request access, enable model access, just access, or preflight (and no source-code path). Do not route on the bare words enable or access alone — "enable the Bedrock rewrite" is a full migration, not an access request. When in doubt, use the AskUserQuestion below rather than the keyword. Or
• the user's request is about getting Bedrock model access enabled rather than rewriting code — e.g. "help me request access to Claude on Bedrock", "enable GPT models on Bedrock", "which models do I need to turn on", "I just want access, my engineers will do the migration".

If it is ambiguous (the user mentions both a codebase and access), AskUserQuestion:

"Two things I can do — which do you want? [Full migration] Rewrite your OpenAI/Gemini/Anthropic calls for Bedrock end-to-end. [Just model access] Check and walk you through enabling Bedrock access for specific models, no code changes."

[Just model access] → Access-only mode. [Full migration] → continue to 0b.

Otherwise (a code path / clear rewrite intent) → continue to 0b for the full migration.

0b. Check that a gcp-to-aws or azure-to-aws sibling skill is installed

Phase A below runs Assess by directly reading and executing $ASSESS_SKILL's own phase instruction files (Discover → Clarify → Design → Estimate, AI-only path) in this same session — there is no Assess logic in this skill to fall back to. gcp-to-aws and azure-to-aws are separate skills, not bundled inside this one — a single-skill install (e.g. npx skills add ... --skill llm-to-bedrock) does not bring either along automatically. Check for them now, before promising the user an Assess phase this deployment cannot run:

if [ -f "<SKILL_BASE>/../gcp-to-aws/SKILL.md" ]; then
  ASSESS_SKILL=gcp-to-aws
elif [ -f "<SKILL_BASE>/../azure-to-aws/SKILL.md" ]; then
  ASSESS_SKILL=azure-to-aws
else
  echo ASSESS_SIBLING_MISSING
fi

$ASSESS_BASE = <SKILL_BASE>/../$ASSESS_SKILL. This checks the sibling directories relative to <SKILL_BASE> (defined above), which works under any install path — native plugin install and npx skills add --skill '*' both place gcp-to-aws/azure-to-aws as siblings of this skill's own directory. It does not depend on ${CLAUDE_PLUGIN_ROOT}. When both are installed, gcp-to-aws wins — it is this skill's original, more deeply tested integration, so this preserves today's default behavior for every existing user with both skills installed.

• $ASSESS_SKILL resolved (either value) → proceed to Step 1.

• ASSESS_SIBLING_MISSING → stop here — do not proceed. Tell the user:

  "This migration needs either the gcp-to-aws or the azure-to-aws skill installed alongside this one — it handles code scanning, AI-workload detection, and Bedrock model design; I can't do that part myself without it. Both ship in the same aws-startup-advisor plugin as this skill, so if you installed the whole plugin one of them should already be next to me — it looks like only some of the plugin's skills were installed. Install the pair together with: npx skills add aws/agent-toolkit-for-aws/plugins/aws-startup-advisor/skills --skill llm-to-bedrock --skill gcp-to-aws (or substitute azure-to-aws if your source infra runs on Azure; use the same --agent and --global/project scope you used for this skill), or run that same npx skills add command with --skill '*' instead of naming individual skills to get every skill at once. Then restart your agent and ask me to migrate again."

  Do not perform the Assess phase yourself as a workaround — Phase A below is explicit that Assess logic lives only in $ASSESS_SKILL; re-implementing it here would drift out of sync with that skill's Discover/Clarify/Design logic over time. There is no standalone Assess for this skill — this check exists to fail fast and clearly, not to unlock alternate behavior.

Phase A reads $ASSESS_SKILL's files directly off disk rather than invoking it as a skill, so there is no separate agent-capability requirement here — any agent that can read a file and run Bash can execute this. (An earlier version of this skill invoked gcp-to-aws via a cross-skill Skill-tool call; that mechanism is Claude-Code-specific and unverified elsewhere, which is exactly why Phase A no longer uses it.)

---

Step 1 — Collect source code path

If $ARGUMENTS contains a path, use it as $REPO. Otherwise use AskUserQuestion: "Where is your source code? Enter a local path or GitHub URL."

If a GitHub URL, git clone it to a temp dir; use that path as $REPO.

Checks on $REPO:

1. Git-root check (compare resolved paths — on macOS /tmp resolves to /private/tmp, so a raw string comparison false-positives):

   [ "$(git -C <REPO> rev-parse --show-toplevel 2>/dev/null)" = "$(cd <REPO> && pwd -P)" ] && echo GIT_ROOT_OK || echo GIT_ROOT_MISMATCH
   

   • GIT_ROOT_OK → proceed.
   • GIT_ROOT_MISMATCH and the command errored (not a git repo at all) → tell the user the path must be a git repository (the deliverable is a git branch); re-ask.
   • GIT_ROOT_MISMATCH but inside a repo (user pointed at a subdirectory) → AskUserQuestion: "Use the repo root instead" (recommended) / "Continue with this subdirectory" / "Abort".

2. Dirty-tree check:

   git -C <REPO> status --porcelain
   

   If uncommitted changes exist, show them and AskUserQuestion: "Continue anyway" or "Let me clean up first".

Record $REPO for all subsequent steps.

---

Step 1.5 — Check for an existing usage profile

Before Phase A begins, check whether a prior Discover run (from either gcp-to-aws or azure-to-aws) already captured real OpenAI/OpenRouter/Anthropic usage-API spend for this repo — if so, reuse it as the cost baseline instead of later falling back to golden-dataset extrapolation. Phase A's own $MIGRATION_DIR resolution and resume logic start fresh each time Phase A is entered, so this step must run strictly before that to have a chance to short-circuit it.

1. Glob every .migration/<run-dir>/<provider>-usage-profile.json under $REPO, excluding this skill's own output directory:

   for p in anthropic openai openrouter; do
     find "$REPO/.migration" -mindepth 2 -maxdepth 2 -name "${p}-usage-profile.json" \
       -not -path "*/.bedrock-*/*" 2>/dev/null
   done
   

   -mindepth 2 -maxdepth 2 matches exactly .migration/<run-dir>/<provider>-usage-profile.json. -not -path "*/.bedrock-*/*" excludes llm-to-bedrock's own $BEDROCK_RUN_DIR (.migration/.bedrock-<id>/) — that directory is this skill's OWN output, never a gcp/azure Discover output. This glob is provider-named, not skill-named — it finds a profile regardless of whether gcp-to-aws's or azure-to-aws's Discover wrote it, since both write to the same filename convention under the same $REPO/.migration/ tree.

2. Zero matches across all three → no change to current behavior; proceed to Phase A exactly as today.

3. Determine which run's artifact to use (deterministic tiebreak, source-run status surfaced): for each distinct run directory with at least one match, read that directory's .phase-status.json → last_updated (a full ISO-8601 timestamp, which DOES carry the year) and use it as the primary sort key — the greatest last_updated wins. Do not treat the directory's own <MMDD-HHMM> name as chronological on its own: a plain lexicographic comparison of <MMDD-HHMM> is only chronological WITHIN a single calendar year — 1231-1200 (Dec 31) lexicographically sorts after 0102-1200 (Jan 2) of the FOLLOWING, objectively later year, so using the name alone can pick a stale run. Fall back to the <MMDD-HHMM> directory name ONLY when a candidate's last_updated is missing or unparseable, and when you do, say so explicitly — that fallback is a same-year assumption, not a general chronological guarantee, and must never be presented to the user as equivalent to a real timestamp comparison. Use .phase-status.json → current_phase as a secondary piece of information to SURFACE to the user (not to break ties) — e.g. a run whose current_phase shows Discover never advanced past Clarify tells the user this was likely an abandoned/declined session, even though its usage-profile file is still the most recent one. State both pieces to the user explicitly before adopting the figures: "Found an existing [provider] usage profile from your <MMDD-HHMM> run (status: <current_phase>) — using that one." If a run directory's .phase-status.json is missing/unparseable entirely, still allow it as a candidate via the <MMDD-HHMM> fallback, but surface status: unknown instead of a phase name. Genuine tie or ambiguity (two or more candidates all lack a usable last_updated, so only the same-year-limited <MMDD-HHMM> fallback is available for them, or their last_updated values are otherwise ambiguous): do NOT silently guess — surface every ambiguous candidate to the user (directory name + whatever status is known for each) and ask which one is actually the one to use, reusing the "state both pieces to the user explicitly" pattern above rather than picking one automatically.

4. For the winning run directory, read every usage-profile file present in it (a run can have multiple providers' profiles simultaneously — including a mix of gcp-produced and azure-produced profiles — B1 does not need to distinguish which skill wrote which file, since the profile schema itself is identical regardless of producer).

5. For each profile: check metadata.capture_warnings first — non-empty means some endpoint failed and that category's volume is UNKNOWN, not zero; propagate the caveat forward.

6. Compute summary.monthly_cost_usd SUMMED across every profile in the winning run (never max/pick-one, reusing estimate-ai.md's documented SUM discipline from both skills — gcp's and azure's estimate-ai.md apply the identical rule), combining usage_by_model[] with the same normalization rules (OpenRouter prompt_tokens/completion_tokens → input_tokens/output_tokens; Anthropic cache_read_tokens/cache_creation_tokens INCLUDED in volume sums — these are additional token pools the Admin API reports separately from input_tokens, not a sub-accounting of it; see estimate-ai.md's Prerequisites section in either sibling skill for the full uncached_input_tokens + cache_read_input_tokens + cache_creation_input_tokens contract). Apply the metadata.partial_window exception: a partial-window profile's figures are a reference figure labeled with active_days, never blended into the monthly baseline (schema consequence specified below). Second, parallel exception — metadata.cost_status (Anthropic profile only, currently the only profile that writes this field): a profile whose cost_status is "cost_unavailable" has null for summary.monthly_cost_usd — EXCLUDE it from this dollar SUM entirely (never add null, never substitute 0), but STILL include its usage_by_model[] token totals in usage_by_model[] below — usage succeeded for that profile, only its cost side failed. This mirrors the partial_window exception's shape (one disqualifies dollars only, the other disqualifies dollars only) but is a distinct, independent condition — a profile can be partial_window: false and still cost_status: "cost_unavailable". Third exception — OpenRouter BYOK/overlap de-duplication: when BOTH an openrouter-usage-profile.json and a direct provider profile (anthropic-usage-profile.json or openai-usage-profile.json) exist in the winning run directory AND the OpenRouter profile's metadata.cost_provenance is "byok_passthrough" or "mixed", do NOT sum both profiles' dollar figures for the overlapping model(s) — the same underlying provider spend would otherwise be counted twice (once via the direct provider's own usage API, once via OpenRouter routing the same traffic). Prefer the direct provider's own figure for the overlapping model(s) as ground truth, and treat the OpenRouter profile's corresponding usage_by_model[] dollar entries as router-fee-only for that overlap (exclude them from this SUM). When cost_provenance is "unknown" (the only value this flow currently writes — see discover-openrouter-api.md's own documented detection limitation), there is no reliable signal to resolve the overlap automatically: match models across the two profiles — OpenRouter's usage_by_model[].model uses a provider/model-name form (e.g. "openai/gpt-4.1"); a model is "the same" as the direct profile's bare model name (e.g. "claude-sonnet-5") if the direct name equals the OpenRouter model name's substring after its last /. For every OpenRouter usage_by_model[] row whose model matches a model present on the direct profile, EXCLUDE that row's dollar AND token figures from this SUM entirely (not dollars alone — the direct profile already counts that traffic's tokens, so including OpenRouter's figures too would double-count both). Keep every OpenRouter-only model (no match on the direct profile) in the sum as before. Flag the exclusion in the summary presented to the user as "OpenRouter and <provider> usage profiles both present; N overlapping model(s) excluded from OpenRouter's totals to avoid double-counting BYOK traffic — provenance is unknown, so this exclusion assumes full overlap for those models, which may undercount if OpenRouter is only partially BYOK-routing them."

7. Hold these computed figures — do not write them yet. $BEDROCK_RUN_DIR does not exist at this point in the document (it is only established in "### A3 — Locate Assess output, then establish this skill's own run state" below, after Phase A runs); writing to it here would write to a directory variable that is not yet assigned. Carry the computed source_run_dir, source_profiles, captured_at, capture_warnings, windows, summary, and usage_by_model[] values forward (the shape is specified in "New artifact" below) and present the short summary now: "Found existing usage data: $X/month across N models (source: [provider list], captured <date>). I'll use this as your current-cost baseline instead of estimating from sampled golden-dataset traffic." The actual file write happens in A3, immediately after $BEDROCK_RUN_DIR is set — see the cross-reference there.

8. This step never prompts for consent — it only reads a file a prior, already-consented run already wrote. No new attack surface; no new consent gate needed.

9. Idempotency: this step is safe to re-run on every Phase-B start, including a resumed session (A2's resume path). It performs read-only file operations only — the actual write of usage-baseline.json happens later, in A3 (see step 7 above), and that write is itself idempotent — overwriting usage-baseline.json on a rerun with the same inputs produces the same output, and a rerun with DIFFERENT inputs (e.g. a newer profile appeared since the last run) correctly re-resolves and overwrites. Re-running this step on a resume where it already ran is therefore harmless; it is not gated behind any "already ran" check.

New artifact: $BEDROCK_RUN_DIR/usage-baseline.json

Written to $BEDROCK_RUN_DIR ($REPO/.migration/.bedrock-<id>/) in "### A3 — Locate Assess output, then establish this skill's own run state" below, immediately after $BEDROCK_RUN_DIR is set — not at Step 1.5 itself, since $BEDROCK_RUN_DIR does not exist yet when Step 1.5 runs (Step 1.5's steps 1–6 above still run here, before Phase A, so Phase A1's own fresh-Discover fallback decision can short-circuit correctly; only the file WRITE is deferred). This gives Phase C's report-generator a stable, already-resolved place to read from without re-globbing .migration/*/ itself (that glob belongs only to this step).

Schema (per-provider window-status map, so a mixed partial/full combination is representable):

{
  "source_run_dir": ".migration/0315-1030",
  "source_profiles": ["anthropic-usage-profile.json", "openai-usage-profile.json"],
  "captured_at": "<ISO 8601, max across source profiles>",
  "capture_warnings": [],
  "windows": {
    "anthropic": { "window_days": 30, "active_days": 12, "partial_window": true, "cost_status": "partial_window" },
    "openai": { "window_days": 30, "active_days": 30, "partial_window": false, "cost_status": "complete" }
  },
  "summary": {
    "monthly_cost_usd": 0.0,
    "monthly_cost_usd_is_blended_estimate": true,
    "currency": "USD",
    "models_seen": 0
  },
  "usage_by_model": [
    {
      "model": "string",
      "provider": "openai|anthropic|openrouter",
      "partial_window": true,
      "input_tokens": 0,
      "output_tokens": 0,
      "num_model_requests": 0
    }
  ]
}

Validation rules:

• source_profiles is non-empty (the file is only written when this step found at least one profile — absence of the file, not an empty array, signals "no existing profile found").
• windows is keyed by every provider contributing to usage_by_model[]; a provider absent from windows must not appear in usage_by_model[] either.
• usage_by_model[].provider disambiguates source; usage_by_model[].partial_window is a direct copy of that row's provider's windows.<provider>.partial_window — carried per-row (not only per-provider) so a consumer reading usage_by_model[] alone, without cross-referencing windows, can still tell which rows are reference-only.
• summary.monthly_cost_usd_is_blended_estimate: true whenever windows contains ANY provider with partial_window: true — when true, the consumer (llm2bedrock-report-generator.md §6) MUST present monthly_cost_usd as a labeled reference figure (with the partial provider's active_days shown), never as an unqualified monthly baseline. When every contributing provider is full-window, this flag is false and monthly_cost_usd is a normal blended monthly baseline. This satisfies step 6 above's discipline WITHOUT splitting summary into two separate top-level figures — the one monthly_cost_usd number remains the SUM step 6 computes, and the boolean tells the consumer how to present it.
• windows.<provider>.cost_status: "complete" | "partial_window" | "cost_unavailable", copied straight from that provider's source usage-profile metadata.cost_status (currently only the Anthropic usage profile writes this field — see discover-anthropic-api.md Step 3 in either sibling skill; OpenAI/OpenRouter profiles that don't yet write it default to "complete" when partial_window is false and "partial_window" when it is true, since for those providers window-completeness and cost-completeness haven't been split apart yet). When ANY windows.<provider>.cost_status is "cost_unavailable", that provider's dollars were already excluded from summary.monthly_cost_usd in step 6 above — a consumer reading windows to explain a dollar total that is lower than the token volume would suggest finds the reason here, per-provider, without re-opening the source profile file.
• source_run_dir path resolution, stated explicitly: source_run_dir is POSIX, relative to $REPO (the same $REPO passed in every subagent's context block as the Repository: line) — never an absolute path, and never relative to $BEDROCK_RUN_DIR. This keeps the artifact portable across a cloned/moved repo. Both the writer (step 7 above, which MUST strip the $REPO/ prefix from the winning run directory's absolute find-produced path before writing it here) and the reader (llm2bedrock-report-generator.md §6) resolve it the same way: join $REPO + source_run_dir to get the absolute path, when either needs to re-open a source profile file directly rather than trusting usage-baseline.json's own already-summed figures.

Standalone invocation with no existing profile

When this step finds zero matches, rely entirely on Phase A's existing inline execution of $ASSESS_BASE's own discover.md (gcp-to-aws's or azure-to-aws's, whichever Step 0b resolved $ASSESS_SKILL to for this run), which already offers OpenAI/OpenRouter/Anthropic usage discovery via its own consent/trigger logic — gcp-to-aws has this today in its Step 1e–1g; azure-to-aws has the identical capability. No new "offer live discovery" entry point is designed here — re-implementing it in this step would duplicate the ~400-line security-contract capture logic AND the offer/consent flow a third/fourth/fifth time (once per cloud), and risk drift between call sites. Instead, Phase A's prose already names $ASSESS_BASE/references/phases/discover/discover.md by path and tells the reader to execute it in full — that is the "reference by path, don't reimplement" mechanism, already in place, covering both clouds.

One adjustment: when Phase A's inline Discover captures a FRESH profile in a NEW $MIGRATION_DIR, re-run steps 4–7 above against that run's own directory (no cross-run search needed) at the end of Phase A's Discover sub-step, and hold the resulting figures the same way (step 7's deferred-write discipline applies here too — $BEDROCK_RUN_DIR still does not exist at this point, since A3 has not run yet) — so Phase C always reads from one consistent place regardless of whether the profile came from this step (reuse) or Phase A's fresh capture, and regardless of which cloud's Assess skill produced it.

Additive, optional — existing flow unaffected: usage-baseline.json absent means no behavior change — the existing behavior-delta comparison and golden-dataset-sample extrapolated cost table (llm2bedrock-report-generator.md §6.3) run exactly as today.

---

Phase A — Assess (runs $ASSESS_SKILL's own AI-path files, inline)

Do NOT read source code, detect AI SDKs, or ask Clarify questions yourself from scratch. This phase reads $ASSESS_SKILL's own phase instruction files off disk and follows them exactly as if $ASSESS_SKILL were running standalone — the same content, the same state file, the same artifacts. The only difference from invoking $ASSESS_SKILL as a separate skill is that there is no tool call and no turn boundary: everything below runs inline, in this session. Step 1.5 above already checked for and, when found, computed the figures for an existing usage-API profile (from either gcp-to-aws's or azure-to-aws's Discover) as the Bedrock cost baseline — held in memory until "### A3 — Locate Assess output, then establish this skill's own run state" below writes them to $BEDROCK_RUN_DIR/usage-baseline.json once $BEDROCK_RUN_DIR exists; Phase A below still runs fully regardless — it only falls back to Discover's own fresh usage-API capture or golden-dataset extrapolation when no existing profile was found.

Path resolution. $ASSESS_SKILL instruction files use relative references (references/phases/..., references/shared/..., references/vendored/..., references/design-refs/..., shared/..., design-refs/..., phases/..., data/... — including the short forms). Resolve every one of them under $ASSESS_BASE (defined above), exactly the prefix it's written with, e.g. shared/pricing-cache.md → $ASSESS_BASE/references/shared/pricing-cache.md. $MIGRATION_DIR is the one path that does not resolve under $ASSESS_BASE — it stays under $REPO per $ASSESS_SKILL's own convention (A1 below). $ASSESS_SKILL's files are read-only here — this phase never edits them.

A1 — Run Discover, Clarify, Design, and Estimate

Tell the user, before starting:

"I'm now running the Discover → Clarify → Design → Estimate assessment (the same logic $ASSESS_SKILL uses standalone) to detect your AI workloads and design the Bedrock migration. It'll ask you some questions — please answer them."

Then, in order:

1. Resolve $MIGRATION_DIR. Check for an existing .migration/ directory at $REPO exactly as $ASSESS_BASE/references/phases/discover/discover.md Step 0 describes (list existing runs and offer Resume/Fresh/Cancel if any exist; otherwise create $REPO/.migration/<MMDD-HHMM>/ with the current timestamp and set $MIGRATION_DIR to it). A directory that exists but has no .phase-status.json yet is NOT an existing run — treat it as fresh and skip discover.md's Resume/Fresh/Cancel prompt for it (same exception agent-advisor's migration-plan.md Phase A documents for its own inline gcp-to-aws delegation). This matters here because llm-to-bedrock does not pre-create $MIGRATION_DIR before this step — but a run interrupted after this step creates the directory and before discover.md's Step 0 writes .phase-status.json would otherwise leave exactly this directory (present, but state-less) for a later resume, and discover.md's Resume branch would then try to read a .phase-status.json that doesn't exist.

2. Read and execute $ASSESS_BASE/references/phases/discover/discover.md in full, exactly as written, including its own Step 0 state-file initialization (skip Step 0 if resuming — $MIGRATION_DIR already has a .phase-status.json) and its Step 1 sub-discovery gates (1a–1e). Those gates already key off what's actually present in $REPO — IaC discovery only runs if Terraform files exist there, billing discovery only if billing exports exist, and so on; you do not need to steer it. discover.md's own usage-API consent/capture actions (OpenAI, OpenRouter, and — for azure-to-aws — Anthropic) will offer the matching Admin-API usage discovery when applicable; accepting any of them gives Estimate real spend and token volumes without manual exports. (Anthropic's Admin key has NO selectable scopes — it is all-or-nothing; the old sentence's "Usage set to Read" phrasing was already wrong for OpenAI's own key in a narrower way worth not perpetuating into this generalized sentence — note this as an adjacent, pre-existing inaccuracy fixed at its source rather than carried forward.)

   When Discover's Step 0 writes the run's .phase-status.json, it must record "initiated_by": "LLM_TO_BEDROCK" beside owning_skill (which is set to whichever of GCP_TO_AWS / AZURE_TO_AWS actually ran — i.e. $ASSESS_SKILL's own identifier — per discover.md's own instruction for a run started by another skill): this run was started by llm-to-bedrock, and that is how telemetry attributes it.

   On HANDOFF_OK: at least one of ai-workload-profile.json, gcp-resource-inventory.json / azure-resource-inventory.json, or billing-profile.json is present in $MIGRATION_DIR.

3. Read and execute $ASSESS_BASE/references/phases/clarify/clarify.md in full. It routes itself — when ai-workload-profile.json is the only discovery artifact, it reads clarify-ai-only.md and runs that standalone flow; if infra artifacts also exist, it runs the fragment/assembler split instead. Either way, follow what it loads exactly. On HANDOFF_OK: preferences.json is present in $MIGRATION_DIR.

4. Read and execute $ASSESS_BASE/references/phases/design/design.md in full. It routes to design-ai.md when ai-workload-profile.json exists — that is the file this skill's Execute phase depends on. On HANDOFF_OK: aws-design-ai.json is present in $MIGRATION_DIR (plus aws-design.json/aws-design-billing.json too, if an infra or billing route also ran).

5. Read and execute $ASSESS_BASE/references/phases/estimate/estimate.md in full. You do not need to continue past Estimate — this skill only needs the Assess artifacts (aws-design-ai.json, ai-workload-profile.json, preferences.json), so once estimate.md reaches its post-Estimate decision gate, choosing not to generate infra (or simply stopping at the decision pack) is enough; you never read anything generate.md would produce (Terraform, MIGRATION_GUIDE.md, migration-report.html).

Each file above manages $MIGRATION_DIR/.phase-status.json itself, per its own protocol — do not write to that file yourself, other than the initiated_by field named in step 2 above. If any file's own _postconditions/handoff checks fail (GATE_FAIL), stop and show the user exactly what that file reported; do not patch an artifact to force a gate to pass.

A2 — Confirm Assess is complete

Since Phase A above ran every step through Estimate inline (not across separate invocations), Assess is complete once step 5 of A1 finishes without a GATE_FAIL. This check exists as a backstop in case the session was interrupted partway through A1 (e.g. the user stopped mid-Clarify and is resuming later) — re-read $MIGRATION_DIR/.phase-status.json and resume A1 at whichever step in phases is not yet "completed", rather than restarting from Discover.

Before trusting that re-read, apply $ASSESS_BASE/SKILL.md § State Validation (the same contract A1's own phase files rely on when THEY read this state, so this wrapper-level backstop re-read must not be held to a looser standard). In particular: if .phase-status.json fails to parse (an interrupted write left it invalid — e.g. the session was interrupted mid-write, not just mid-phase), § State Validation check 2's reconstruction procedure is the one and only sanctioned way to recover it — infer completed phases from the artifacts actually present in $MIGRATION_DIR, present the inferred status to the user for confirmation, and rewrite .phase-status.json only on that confirmation. This is a narrow, explicit exception to A1's "do not write to that file yourself" rule: .phase-status.json recovery per this contract is not the same act as a phase file's own state management, and proceeding here without it would mean this wrapper resumes on state it never validated, unlike every phase file it delegates to.

Workshop guard: if phases.workshop == "in_progress", design.md's inner-workshop path is actively repricing (workshop-refresh.md is rewriting aws-design-ai.json between an old and a new mapping) — finish that loop (it resolves phases.workshop back to "completed" on exit or decline) before treating Design as done.

A3 — Locate Assess output, then establish this skill's own run state

Use the SAME $MIGRATION_DIR A1/A2 already resolved and confirmed — do NOT re-select a directory here. $MIGRATION_DIR is already set from A1 step 1 (and re-confirmed, not replaced, by A2's re-read on a resumed session); re-running a "newest directory" lookup (ls -td "$REPO/.migration"/*/ | head -1) at this point can select a DIFFERENT, newer run than the one A2 just verified was complete — e.g. a sibling run whose own Design is still in progress. B1 would then read that sibling's unfinished model map instead of the run this phase actually assessed, with no error raised (the newer directory can genuinely contain all three files below, just from a different, incomplete assessment). If $MIGRATION_DIR is somehow unset here (it should never be, given A1/A2 above), that is a bug in this phase's own state tracking — stop and report it rather than guessing a directory from ls -td.

Verify all three of these files exist in $MIGRATION_DIR (Phase B reads every one):

• aws-design-ai.json (model mapping + architecture)
• ai-workload-profile.json (detected workloads)
• preferences.json (user preferences from Clarify)

If any of the three is missing, Assess did not complete the AI path correctly — name the missing file(s), show the error, and stop. A1 running to completion without a GATE_FAIL should already guarantee this; this step is the backstop that confirms it before Execute reads them.

Establish this skill's own run state (read-merge-write). This skill keeps a thin run-state file of its own, separate from the delegated run's, so the AI migration appears in the usage funnel under its own name. It declares its own state shape (phases assess, execute); the shared DSL's read-merge-write rule applies. Leave the delegated run's .phase-status.json untouched here: a run created by this invocation already carries initiated_by (A1), and rewriting an older run's file would make its history look like new work to the telemetry hooks.

1. Set $BEDROCK_RUN_DIR = $REPO/.migration/.bedrock-<id>/, where <id> is the basename of $MIGRATION_DIR (e.g. .migration/.bedrock-0910-1100/). The leading dot keeps it out of the ls -td "$REPO/.migration"/*/ lookup above, so it can never be mistaken for the Assess run directory; keying it to the delegated run means a resumed migration reuses it. $BEDROCK_RUN_DIR also holds usage-baseline.json when Step 1.5 (or Phase A's own fresh Discover capture) found an existing usage-API profile — written in step 2 below, once this directory exists (see Step 1.5's "New artifact" section above for the schema).

   • If $BEDROCK_RUN_DIR/.phase-status.json already exists, this migration is being resumed: keep the file (including its run_id) and continue.

   • Otherwise create the directory and write .phase-status.json:

     {
       "migration_id": ".bedrock-<id>",
       "last_updated": "<ISO 8601 now>",
       "current_phase": "execute",
       "run_id": "<fresh UUID from uuidgen>",
       "owning_skill": "LLM_TO_BEDROCK",
       "phases": { "assess": "completed", "execute": "in_progress" }
     }
     

   The assess/execute phase names are not in the telemetry API model yet, so only the run-level events (RUN_STARTED, RUN_COMPLETED) leave the machine for this run; the AI journey's entry point is already visible through the delegated run's events, which carry initiatingSkill.

2. Write usage-baseline.json, now that $BEDROCK_RUN_DIR exists. If Step 1.5 (or Phase A's own fresh Discover capture, per Step 1.5's "Standalone invocation" note) held computed figures in step 7, write them to $BEDROCK_RUN_DIR/usage-baseline.json now, using the schema in Step 1.5's "New artifact" section above. If no figures were held (zero matches in Step 1.5, and no fresh profile from Phase A's Discover either), skip this write entirely — usage-baseline.json's absence is itself the "no baseline" signal llm2bedrock-report-generator.md §6.3 checks for.

3. After creating or validating the resumed Bedrock run, reconcile in cli reporting mode per references/vendored/telemetry/PROTOCOL.md. In hook mode, leave reporting to the host hooks.

---

Phase B — Execute Prep

B1 — Read Assess outputs

Read $MIGRATION_DIR/aws-design-ai.json and extract:

• ai_architecture.bedrock_models[] → array of {source_model, aws_model_id, use_case}
• Collect all aws_model_id values into $TARGET_MODELS (array). Keep the use_case of each: the preflight script probes each model by the right API automatically (Converse for chat, InvokeModel for embeddings), but the evaluator's quality scoring only applies to chat models — embedding targets get format/dimension validation only.

Read $MIGRATION_DIR/ai-workload-profile.json and extract:

• summary.ai_source → source provider

Read $MIGRATION_DIR/preferences.json and extract:

• design_constraints.target_region → $REGION (default us-east-1 if absent)

Validation: If aws-design-ai.json has no ai_architecture.bedrock_models[] array, or the array is empty, STOP: "Assess output incomplete — model mapping missing."

B2 — AWS identity confirmation

aws sts get-caller-identity 2>&1

If the command fails (no credentials, expired SSO token): show the error and tell the user to run aws configure or aws sso login (suggest typing ! aws sso login to run it in this session), then re-run B2. Do not proceed without a confirmed identity.

On success, show Account, Arn, UserId via AskUserQuestion: "This AWS identity will be used for Bedrock calls. Is this correct?"

Options:

• Yes, use this identity → proceed
• Use a different AWS profile → ask which profile, record it as $AWS_PROFILE_CHOICE, re-run B2 as aws sts get-caller-identity --profile $AWS_PROFILE_CHOICE, and re-confirm. Do NOT rely on exporting AWS_PROFILE — env vars do not persist across Bash tool calls or into workflow subagents (see B3). Instead pass the choice explicitly everywhere: --profile on every aws CLI call, and prepend AWS_PROFILE=$AWS_PROFILE_CHOICE inline on the B4 preflight command and inside the workflow args (awsProfile field) so subagents can do the same.

Also confirm region: "Bedrock region will be $REGION. OK or override?"

B3 — Source API key (optional)

First, create the artifact directory and make it self-ignoring IMMEDIATELY — before any key exists, so the secret is never sitting in an unignored working tree (even if the user aborts before the rewriter runs):

mkdir -p "$REPO/.saws-migrate" && printf '*\n' > "$REPO/.saws-migrate/.gitignore"

Determine $KEY_ENV_VAR from B1's source provider (this is the env-var name the baseline skill's parser expects — a bare key without the NAME= prefix will NOT be parsed):

• openai → OPENAI_API_KEY
• anthropic → ANTHROPIC_API_KEY
• google / gemini → GEMINI_API_KEY

The key must never enter this conversation (HARD RULE). Do not ask the user to paste the key in chat, and never echo, cat, or interpolate its VALUE into any command, question, or output — the agent only ever handles the file path. If the user pastes a key into the chat unprompted, do not use it: tell them it is now part of the transcript, recommend rotating it, and continue with one of the paths below.

First check whether the key is already present in the shell environment (each Bash call initializes from the user's profile, so a profile-exported key is visible to every call). POSIX-safe — printenv works in bash and zsh alike (${!VAR} indirection is bash-only and zsh errors on it). This prints presence only, never the value:

[ -n "$(printenv "$KEY_ENV_VAR")" ] && echo ENV_KEY_PRESENT || echo ENV_KEY_ABSENT

AskUserQuestion: "Do you have an API key for the source model (e.g. OpenAI key for GPT-4o)? Providing it enables side-by-side quality comparison. Without it, evaluation uses absolute scoring only."

Options (offer the first only on ENV_KEY_PRESENT):

• Use the $KEY_ENV_VAR already in my environment → materialize env var to file in one command — the value never appears in the transcript:

  printf '%s=%s\n' "$KEY_ENV_VAR" "$(printenv "$KEY_ENV_VAR")" > "$REPO/.saws-migrate/.source-provider-env" && chmod 600 "$REPO/.saws-migrate/.source-provider-env"
  

  Then run the format check below and set sourceBaselineAvailable = true, sourceKeyRef = "$REPO/.saws-migrate/.source-provider-env".

• I'll write it to a file myself → give the user this command to run in THEIR OWN terminal (not through the agent; in Claude Code an ! prefix runs it in-session) — read -rs collects the key without echoing it:

  read -rs k && printf '%s=%s\n' "<KEY_ENV_VAR>" "$k" > "<REPO>/.saws-migrate/.source-provider-env" && chmod 600 "<REPO>/.saws-migrate/.source-provider-env" && unset k
  

  Substitute the literal env-var name and repo path when presenting it (those are not secrets). Then run the format check below and set the same flags as above.

• Skip → sourceBaselineAvailable = false, sourceKeyRef = "".

Whichever path wrote the file, verify the format (never prints the key; catches a value written without the NAME= prefix, which the baseline parser would silently miss):

grep -qE '^(OPENAI|ANTHROPIC|GEMINI)_API_KEY=.+' "$REPO/.saws-migrate/.source-provider-env" && echo KEY_FORMAT_OK || echo KEY_FORMAT_BAD

On KEY_FORMAT_BAD, have the same path that wrote the file rewrite it (do not echo its contents).

(.saws-migrate/ is already self-ignoring from the first command above; the rewriter re-asserts this before any commit as a second layer.)

IMPORTANT: The file is the handoff mechanism — do NOT rely on export to carry the key into later steps. Shell state set in one Bash call does not persist into other calls or into workflow subagents; only a profile-exported variable (the ENV_KEY_PRESENT path above) is reliably visible, and even that must be materialized to the file for the baseline runner.

B4 — Bedrock preflight

uv run --project $SCRIPTS python $SCRIPTS/preflight_bedrock.py --region $REGION --models <comma-separated $TARGET_MODELS> --dataset-size 200

(--dataset-size 200 matches the golden-dataset cap, so the quota warning reflects the worst case. Prefix with AWS_PROFILE=$AWS_PROFILE_CHOICE if B2 chose a non-default profile.)

Parse the JSON output. On failure the TOP LEVEL carries reason/detail (lifted from the first failing model) plus failing_models (all failing ids); per-model verdicts are in models[]:

• ok == false + reason: credentials → show the detail (configure/refresh credentials), stop; user re-runs after fixing.
• ok == false + reason: model_access → model access not enabled in the Bedrock console (NOT an IAM problem): point the user at the console Model access page for the failing models, stop; re-run B4 after they enable it.
• ok == false + reason: authz → IAM denies inference. For a Converse/InvokeModel target the action to grant is bedrock:InvokeModel; for a mantle-only openai.gpt-5* target it is the bedrock-mantle:* set (see B4a). The detail names which. Tell the user the action to grant; stop.
• ok == false + reason: mantle_deps_missing → the pinned scripts environment lacks openai / aws-bedrock-token-generator, so a mantle-only target could not be probed at all. This is an environment fault, not a Bedrock verdict: tell the user to re-sync (uv sync --project $SCRIPTS) and stop. Do NOT proceed — access was never verified.
• ok == false + reason: model_unavailable → Read the resolve-bedrock-model-id reference at $HELPERS/resolve-bedrock-model-id/resolve-bedrock-model-id.md and follow its procedure with each ID from failing_models + region. AskUserQuestion with the candidates: "Use <candidate> (cross-region inference profile)" / "Paste a different model ID" / "Abort". On a choice, replace the ID in $TARGET_MODELS and re-run B4.
• ok == false + any other reason → show detail and stop.
• ok == true → proceed. Surface any quota_warning, and any model whose reason is embedding_unprobed (embedding family the preflight can't probe — remind the user to confirm model access in the console).

---

Phase C — Execute

Phase C dispatches the five plugin agents sequentially via the Agent tool (subagent types aws-startup-advisor:llm2bedrock-code-analyzer, aws-startup-advisor:llm2bedrock-log-ingestor, aws-startup-advisor:llm2bedrock-prompt-evaluator, aws-startup-advisor:llm2bedrock-code-rewriter, aws-startup-advisor:llm2bedrock-report-generator). Each agent writes its result to a file under $PHASE_DIR = $REPO/.saws-migrate/phase-results/; you validate every file with the bundled validator before moving on. There is no workflow runtime — the files ARE the state.

The validator (used at every step)

uv run --project $SCRIPTS python $SCRIPTS/validate_result.py --schema <analysis|ingestion|eval|rewrite|delta-decisions> <file>

• Exit 0 + RESULT=valid CONTROL=ok → phase completed; proceed.
• Exit 0 + CONTROL=blocked REASON=<r> → blocked flow (below).
• Exit 0 + CONTROL=partial COMPLETED=<n> TOTAL=<m> → partial flow (eval only).
• Exit 1 (RESULT=invalid + error lines) or exit 2 (file missing) → stateless fixer retry: dispatch a FRESH agent of the same type whose prompt is the original context block + the file path + the validator's verbatim error output + the instruction "fix ONLY the output file at <path> so it validates; do not redo the phase's work unless a required field is genuinely missing from it". Cap 2 retries per phase; then stop and show the errors.

The context block (instantiated at every dispatch)

Build this exact line format (agents parse the labels). Omit lines marked optional when empty:

Repository: <$REPO>
AWS region: <$REGION>
AWS profile (pass as --profile / AWS_PROFILE= inline on every aws/boto3 invocation): <$AWS_PROFILE_CHOICE — omit line if default>
Target Bedrock model(s): <comma-joined $TARGET_MODELS, with any resolved overrides already applied>
Migration plan dir: <$MIGRATION_DIR>
Resolved target model id: <override for the primary chat model — omit if none>
Scripts directory (pinned uv toolchain): <$SCRIPTS>
Report date suffix: <saved suffix from run-context — C5/C6 dispatches only>
Usage baseline path: <$BEDROCK_RUN_DIR/usage-baseline.json — C5/C6 dispatches only, omit line if the file is absent>
Source baseline available: <true|false>
Source provider env file: <path — omit if none>
User-supplied log files: <comma-joined — omit if none>
Golden dataset cap (max cases the ingestor may emit): 200
Phase results directory: <$PHASE_DIR>
Prior phase results (Read these files): <paths of already-validated phase JSONs>
Confirmed behavior-delta decisions file (Read it): <$PHASE_DIR/delta-decisions.json — C5 only>
<helper-reference lines — inject ONLY the ones this agent needs, per the table below>

Prior-phase results are passed as FILE PATHS — never inline their JSON into the prompt.

Helper references (the former helper skills, now under $HELPERS). Agents no longer load skills by name; instead the agent Reads a helper reference at an absolute path you inject. For each dispatch, add ONLY the helper lines that agent uses (per its # 4 section):

Agent (dispatch)	Helper-reference lines to add
C1 llm2bedrock-code-analyzer	behavior-delta-detection reference: $HELPERS/behavior-delta-detection/behavior-delta-detection.md; resolve-bedrock-model-id reference: $HELPERS/resolve-bedrock-model-id/resolve-bedrock-model-id.md
C5 llm2bedrock-code-rewriter	bedrock-known-fixes reference: $HELPERS/bedrock-known-fixes/bedrock-known-fixes.md; behavior-delta-detection reference: $HELPERS/behavior-delta-detection/behavior-delta-detection.md; dependency-conflict-resolution reference: $HELPERS/dependency-conflict-resolution/dependency-conflict-resolution.md
C3 llm2bedrock-prompt-evaluator	bedrock-known-fixes reference: $HELPERS/bedrock-known-fixes/bedrock-known-fixes.md; resolve-bedrock-model-id reference: $HELPERS/resolve-bedrock-model-id/resolve-bedrock-model-id.md; run-source-model-baseline reference: $HELPERS/run-source-model-baseline/run-source-model-baseline.md
C2 llm2bedrock-log-ingestor, C6 llm2bedrock-report-generator	(none — these agents load no helpers)

Expand $HELPERS to its absolute path (you have <SKILL_BASE>) so the subagent — where ${CLAUDE_PLUGIN_ROOT} is empty — receives a resolvable absolute path.

C0 — Run-context gate (resume safety)

The Eval phase makes one paid Bedrock call per golden case (and, with a source key, one paid source-provider call per case). Before any dispatch, tell the user evaluation will invoke Bedrock at their expense, capped at 200 cases.

1. mkdir -p $PHASE_DIR. Build $PHASE_DIR/current-context.json with exactly these fields (hashes via shasum -a 256; key hash is a fingerprint — never store the key value):

{
  "repo_root": "<cd $REPO && pwd -P>",
  "migration_dir": "<$MIGRATION_DIR>",
  "region": "<$REGION>",
  "aws_profile": "<$AWS_PROFILE_CHOICE or \"\">",
  "aws_account": "<Account from B2>",
  "repo_head_sha": "<git -C $REPO rev-parse HEAD>",
  "repo_branch": "<git -C $REPO rev-parse --abbrev-ref HEAD>",
  "repo_dirty_sha256": "<sha256 of: git status --porcelain + git diff + git diff --cached, EACH with pathspecs -- . ':(exclude).saws-migrate' ':(exclude).migration' ':(exclude)MIGRATION_REPORT_*.md'; \"\" when all three are empty>",
  "target_models": [{"source_model": "...", "aws_model_id": "...", "use_case": "..."}],
  "resolved_model_overrides": {},
  "source_provider": "<from B1>",
  "source_baseline_available": <true|false from B3>,
  "source_key_sha256": "<sha256 of .source-provider-env contents, \"\" when absent>",
  "usage_baseline_sha256": "<sha256 of $BEDROCK_RUN_DIR/usage-baseline.json contents, \"\" when absent>",
  "log_files": [{"path": "...", "sha256": "..."}],
  "max_golden_cases": 200,
  "assess_design_sha256": "<sha256 of $MIGRATION_DIR/aws-design-ai.json>",
  "report_date_suffix": "<date +%Y-%m-%d>",
  "schema_version": 1,
  "plugin_version": "<version from <plugin>/.claude-plugin/plugin.json>"
}

1. Stage 0 (post-C5 normalization). If $PHASE_DIR/rewrite.json exists and validates as a payload (CONTROL=ok), do NOT use live repo_* values. Run three integrity checks: (1) rewrite.baseline_parent_sha equals the SAVED repo_head_sha; (2) git rev-parse <rewrite.branch_name> equals rewrite.branch_tip_sha; (3) git status --porcelain (with the artifact exclusions) is empty. All pass → copy the saved repo_* values into current-context verbatim, continue to step 3. Check 2 fails (tip moved) → STOP and AskUserQuestion: "Keep your commits (regenerate report only, with a mixed-authorship note)" / "Reset the branch to the rewriter's tip and regenerate from C6" / "Abort". Check 3 fails (dirty tree) → STOP and ask: commit/stash (then re-check) or discard the edits. Check 1 fails → treat as a full repo_* mismatch in step 3.

2. If $PHASE_DIR/run-context.json exists, compare:

uv run --project $SCRIPTS python $SCRIPTS/validate_result.py --check-run-context $PHASE_DIR/run-context.json --current $PHASE_DIR/current-context.json

• RUN_CONTEXT=match → resume: walk C1→C2→C3→(C4: delta-decisions.json)→C5→(C6: report file) in order; a phase counts completed iff its file validates with CONTROL=ok (C6: iff MIGRATION_REPORT_<saved suffix>.md exists while rewrite.json is payload-valid). STOP the walk at the first missing/invalid/control-state file — blocked/partial files route to their flows below, NEVER count as completed. Offer the user "skip completed phases X..Y, resume at Z". Files after an unexplained gap: archive them with the gap.
• RUN_CONTEXT=mismatch → scoped invalidation. Map each MISMATCH line through this table, archive the named units to $REPO/.saws-migrate/phase-results-archive/<saved suffix>-$(date +%H%M%S)/ (a SIBLING of phase-results/ — never nest it inside), then immediately overwrite run-context.json with current-context.json (carrying forward the saved report_date_suffix unless REPORT itself is being invalidated), then re-run the invalidated phases in order. Tell the user which fields differed and what re-runs.

Mismatched field(s)	Archive (units)	Keep
repo_root, migration_dir, region, aws_profile, aws_account, source_provider, assess_design_sha256, schema_version, plugin_version	everything	—
repo_head_sha / repo_branch / repo_dirty_sha256	everything	—
target_models / resolved_model_overrides	ANALYSIS, EVAL, REWRITE, REPORT	INGESTION
log_files / max_golden_cases	everything	—
source_key_sha256 / source_baseline_available	ANALYSIS, EVAL, REWRITE, REPORT	INGESTION
usage_baseline_sha256	EVAL, REWRITE, REPORT	ANALYSIS, INGESTION

Units: ANALYSIS = analysis.json · INGESTION = ingestion.json + .saws-migrate/golden-dataset/ · EVAL = eval.json + .saws-migrate/eval-results/ (minus cost_compare.py) · REWRITE = rewrite.json + delta-decisions.json · REPORT = MIGRATION_REPORT_<saved suffix>.md.

Post-C5 reruns of C1–C3 need the pre-migration tree. If rewrite.json was payload-valid and the table invalidates ANALYSIS/INGESTION/EVAL: confirm with the user that the old migration branch will be discarded (keep-or-reset flow first if the tip moved), then git checkout <saved repo_branch>, delete the old branch and the saws-migrate-baseline tag, and re-run from C1. If the user declines, stop — re-analyzing a tree that contains the rewrite produces garbage.

1. No saved run-context → fresh run: write current-context.json as run-context.json, dispatch C1.

C1 — Analyzer · C2 — Ingestor · C3 — Evaluator

For each phase in order, dispatch the agent with the context block (listing all prior-phase file paths), then validate its output file:

Step	agentType	Output file	Schema
C1	aws-startup-advisor:llm2bedrock-code-analyzer	$PHASE_DIR/analysis.json	analysis
C2	aws-startup-advisor:llm2bedrock-log-ingestor	$PHASE_DIR/ingestion.json	ingestion
C3	aws-startup-advisor:llm2bedrock-prompt-evaluator	$PHASE_DIR/eval.json	eval

Blocked flow (CONTROL=blocked): resolve with the user per REASON —

• model_access → user enables the model in the Bedrock console (nothing fingerprinted changes; re-dispatch the blocked phase only)
• model_unresolvable → user picks/pastes an ID → record it in resolved_model_overrides, fold it into the Target line
• source_key_auth → user supplies a new key (re-run B3) or sets baseline unavailable
• authz → IAM denies inference. The detail names the action set to grant: bedrock:InvokeModel* for a Converse target, or the bedrock-mantle:* actions (CreateInference, CallWithBearerToken) for a mantle-only openai.gpt-5* target. User fixes IAM; nothing fingerprinted changes, so re-dispatch the blocked phase only. Do NOT route this to model_access — the console Model access page is the wrong fix for an IAM denial and vice versa.
• mantle_deps_missing → the pinned scripts environment lacks openai / aws-bedrock-token-generator, so a mantle target could not be probed at all. User re-syncs (uv sync --project $SCRIPTS); re-dispatch the blocked phase only. Access was never verified, so do not treat a previous pass as still valid.
• assess_output_missing → re-run Phase A, then restart Phase C at C0

After ANY resolution, re-run the C0 recipe (rebuild current-context, apply the invalidation table, overwrite run-context) and re-dispatch from the earliest invalidated phase — the table, not the block location, decides where execution resumes.

Partial flow (eval only, CONTROL=partial): AskUserQuestion —

• Continue remaining cases → re-dispatch the evaluator with the extra context line: Resume: raw_results.jsonl already contains completed cases — evaluate only prompts whose ids are not present in it, then re-score and overwrite eval.json
• Proceed with partial pass rate → re-dispatch the evaluator with: Finalize partial: do NOT call Bedrock again — score the cases already in raw_results.jsonl and emit the FULL eval payload over only those cases, with total_cases = the number scored and a notes prefix line 'partial_coverage: <completed>/<total> cases (throttled)'. Then C4 runs normally.
• Abort → stop; the files stay on disk for a later C0 resume.

C4 — Sidebar (two gates) + persist decisions

Gate (a) — Quality go/no-go. Read $PHASE_DIR/eval.json. The threshold is pass rate >= 0.9 AND source_baseline_quality != 'poor' (with no_golden_cases: true in the notes there is no quality signal — always ask). At or above → proceed silently. Below, AskUserQuestion:

• Proceed anyway → gate (b)
• Change target model → record in resolved_model_overrides, re-run C0 (the table invalidates ANALYSIS/EVAL and execution resumes at C1). Cap: 2 retries.
• Abort → stop, no code touched.

Gate (a.5) — Rewrite strategy (from migration plan). Read migration_path from $MIGRATION_DIR/aws-design-ai.json → ai_architecture.code_migration.migration_path. If the value starts with "mantle" ("mantle", "mantle_openai_responses"), set rewrite_strategy = "mantle". Otherwise (value is "converse", "gpt-oss", or the field is absent), set rewrite_strategy = "converse". No user question needed — the decision was already made during the Assess/Design phase.

Match on the prefix, not on equality: Design writes the more specific "mantle_openai_responses" for a same-model OpenAI migration, and an equality check against "mantle" would silently route those runs down the Converse path — rewriting working same-model code into a boto3 Converse client against a model that has no Converse surface.

Gate (b) — Behavior-delta resolution. For each analysis.behavior_deltas[] with user_visible == true, AskUserQuestion with the options from the behavior-delta-detection reference (Read $HELPERS/behavior-delta-detection/behavior-delta-detection.md, and the source_provider sub-reference under its references/ dir).

Persist: write the decisions array (entries {delta_type, location, resolution_chosen, source}; [] when there were no user-visible deltas) to $PHASE_DIR/delta-decisions.json and validate it (--schema delta-decisions). The file must exist before C5 — it is what makes a C5 retry or a post-crash resume self-sufficient.

C5 — Rewriter · C6 — Report

Step	agentType	Output	Schema
C5	aws-startup-advisor:llm2bedrock-code-rewriter	$PHASE_DIR/rewrite.json	rewrite
C6	aws-startup-advisor:llm2bedrock-report-generator	MIGRATION_REPORT_<saved suffix>.md in repo root	(none — file existence is the completion check)

C5's context block includes the Confirmed behavior-delta decisions file line and the Report date suffix line (from run-context, NOT today's date on a resume). C6's context block lists all four phase-result file paths.

When rewrite_strategy == "mantle", C5's context block ALSO includes:

• Rewrite strategy: mantle (omit this line entirely for Converse — its absence is the signal for the default Converse path)
• Mantle model map: <source-model> -> <bedrock-model-id> — sourced from the plan's ai_architecture.bedrock_models[] entries (each source_model → aws_model_id pair).
• Mantle surface: responses and Mantle base path: /openai/v1 when any mapped aws_model_id is a proprietary GPT model (openai.gpt-5*). These are served only on the /openai/v1 path via the Responses API — distinct from the v1 path other mantle models use — so the rewriter must not emit a /v1 base URL or a Chat Completions call for them. See $ASSESS_BASE/references/shared/openai-on-bedrock.md.
• Same model: true when bedrock_models[].model_change is false. Signals the rewriter to keep model parameters untouched and limit changes to the endpoint, credential, model id, and (if the source used Chat Completions) the surface reshape.

C7 — Render summary

uv run --project $SCRIPTS python $SCRIPTS/render_report.py --phase-results $PHASE_DIR --repo $REPO --date-suffix <saved suffix>

Print the summary. Point the user at rewrite.branch_name (usually bedrock-migration, but a collision-suffixed variant like bedrock-migration-2 when they already had that branch) and the report file. Tell them how to undo — substitute the ACTUAL branch name from rewrite.branch_name, never a hardcoded one (on a collision run, bedrock-migration is the user's own pre-existing branch and deleting it would destroy their work):

To discard: git checkout <your original branch>, git branch -D <rewrite.branch_name>, git tag -d saws-migrate-baseline, and rm -rf .saws-migrate .migration removes all migration artifacts (including the API key file).

Close this skill's run state (read-merge-write on $BEDROCK_RUN_DIR/.phase-status.json): set phases.execute to "completed", current_phase to "complete", and update last_updated. This is what marks the AI migration finished in the usage funnel. After writing this state, reconcile in cli reporting mode per references/vendored/telemetry/PROTOCOL.md; skip the CLI in hook mode.

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Access-only mode

Entered from Step 0a-bis when the user wants Bedrock model access checked/enabled for a named set of models, not a code migration. It reuses the identity check (B2) and the preflight (B4) but takes its model list from the user, and it never touches gcp-to-aws, Assess, the source key (B3), or Phase C. It answers "which models do I need to turn on, and how" — then stops. No git branch, no rewrite.

AC1 — Set expectations (accuracy — say this first)

Tell the user, before collecting models:

"A few things about 'model access' on Bedrock:

• Claude, Llama, Nova, Mistral, etc. are Bedrock foundation models on the bedrock-runtime endpoint — inference uses bedrock:InvokeModel / Converse. In commercial Regions, access is on by default once the caller has the AWS Marketplace permissions (aws-marketplace:Subscribe / Unsubscribe / ViewSubscriptions) — the model auto-subscribes on first invoke. The console Model access page is the explicit enable/catalog step (and the required flow in GovCloud). Anthropic models also need a one-time First-Time-Use form (PutUseCaseForModelAccess) per account before invoke — except when reached via bedrock-mantle.
• OpenAI on Bedrock comes in a few forms, all real — and which endpoint they use is per-ID, not one blanket rule:
  • Open-weight gpt-oss (openai.gpt-oss-20b-1:0, openai.gpt-oss-120b-1:0) → bedrock-runtime via bedrock:InvokeModel / Converse (its Responses API is also offered on bedrock-mantle).
  • Bare proprietary GPT ids (openai.gpt-5*, not gpt-oss) → probed on the bedrock-mantle endpoint (Responses API).
  • A GPT-5.6 CRIS profile id prefixed us. / in. / global. (e.g. global.openai.gpt-5.6-sol) → a bedrock-runtime target where Converse is supported.
  • The bedrock-mantle path uses a separate action set — bedrock-mantle:* (e.g. the AmazonBedrockMantleInferenceAccess managed policy: bedrock-mantle:CreateInference + CallWithBearerToken) — distinct from bedrock:InvokeModel. The preflight decides per model; don't assume.
• What is not on Bedrock is calling OpenAI's own hosted API at api.openai.com — that stays with OpenAI. 'GPT on Bedrock' means the AWS-served models above, reached through AWS endpoints and IAM.

The preflight probes each model by the right API automatically and reports exactly which access to enable per model; I'll relay that."

AC2 — Collect requested models and region (do NOT resolve friendly names yet)

• Models (raw request only): if $ARGUMENTS names model IDs, use them directly — skip resolution, they're already IDs. Otherwise AskUserQuestion: "Which models do you want access to? Give Bedrock model IDs (e.g. anthropic.claude-sonnet-4-5-v1:0, openai.gpt-oss-120b-1:0) or provider + name and I'll resolve the ID." Collect whatever the user gave (IDs and/or friendly names) into $REQUESTED_MODELS — do NOT invoke the friendly-name resolver here. The resolver's own commands (aws bedrock list-foundation-models / list-inference-profiles) require a --region and run under whatever AWS identity is active at call time — resolving before $REGION/AC3 are set means it can run against the wrong region or the default (not yet confirmed) profile and fail for reasons that have nothing to do with the model name.
• Region: AskUserQuestion: "Which AWS region? (default us-east-1)" → $REGION.

AC3 — AWS identity confirmation

Run the B2 identity-confirmation step exactly as written (including the profile-choice handling and $AWS_PROFILE_CHOICE). Do not proceed without a confirmed identity.

AC3.5 — Resolve friendly names, now that region + identity are confirmed

For any entry in $REQUESTED_MODELS that is not already a Bedrock model ID, resolve it now (read $HELPERS/resolve-bedrock-model-id/resolve-bedrock-model-id.md and follow its procedure) — using $REGION from AC2 and, if AC3 chose a non-default profile, prefixing the resolver's own AWS CLI calls with AWS_PROFILE=$AWS_PROFILE_CHOICE (env vars do not persist across Bash calls; without the prefix the resolver queries the DEFAULT identity, not the one just confirmed). Confirm the resolved IDs back to the user. Collect the final IDs (already-ID entries plus newly-resolved ones) into $TARGET_MODELS.

If the user later changes $REGION or the AWS profile (e.g. after an AC4 authz/ credentials failure prompts a re-check): re-run this resolution step for any name-based entry before re-running AC4 — a model ID resolved against the old region/profile may not be the right ID (or may not exist) in the new one.

AC4 — Preflight the named models

Run the B4 preflight against $TARGET_MODELS and interpret its verdicts exactly as B4 does, with these mode-specific differences:

uv run --project $SCRIPTS python $SCRIPTS/preflight_bedrock.py --region $REGION --models <comma-separated $TARGET_MODELS> --dataset-size 0

(--dataset-size 0 — there is no golden dataset in this mode, so no quota-vs-dataset warning is meaningful; a plain quota note is still surfaced if present. Prepend AWS_PROFILE=$AWS_PROFILE_CHOICE inline if AC3/B2 chose a non-default profile — env vars do not persist across Bash calls, so without the prefix this probes the DEFAULT identity, not the one the user just confirmed, and a credentials/authz failure would be about the wrong account.)

Then, per B4's branch table:

• reason: model_access → the model exists but access is not enabled for this account. Name the right prerequisite for the failing model(s):
  • Commercial Regions: access is on by default once the caller has the AWS Marketplace permissions (aws-marketplace:Subscribe / Unsubscribe / ViewSubscriptions) — the model auto-subscribes on first invoke. If those permissions are missing, that is the fix.
  • GovCloud, third-party models (most models — check first): GovCloud accounts are linked one-to-one with a commercial account, and per AWS's own docs, third-party model access must be enabled in both accounts — enabling it only in GovCloud leaves the account blocked. Two steps, in order:
    1. In the linked commercial account, in us-east-1 or us-west-2 (switch AWS identity/profile to that account first), invoke the model once (or enable it via the SDK/CLI as in the commercial-Regions bullet above) — this is the same auto-enable mechanism, just run against the commercial account rather than GovCloud. Note: entitlement can take a few minutes to propagate to the linked GovCloud account after this step.
    2. Switch back to the GovCloud identity, then use the console Model access page — always in us-gov-west-1 specifically (not the inference region — GovCloud's Model access console page only exists in that one region, regardless of what $REGION the user is trying to invoke the model from). Confirm which identity/profile is active before each step; a mismatch here (acting in the wrong account) looks like the enablement "didn't work."
  • GovCloud, Amazon-provided models: only the GovCloud-account step above is needed — no linked commercial-account step, since Amazon models aren't third-party AWS Marketplace listings.
  • Anthropic models additionally need the one-time First-Time-Use form (PutUseCaseForModelAccess) per account before invoke — except when reached via bedrock-mantle. Point the user at the failing model(s) + the applicable prerequisite, and offer to re-run AC4 after they enable it. This is the common, expected outcome for a user who came here to "get access."
• reason: authz → access is enabled but IAM denies inference. Name the action to grant — for a standard model bedrock:InvokeModel; for a mantle-only openai.gpt-5* target (bare proprietary GPT, not gpt-oss) the bedrock-mantle:* set (see B4a). The detail says which — follow it rather than guessing from the ID, since gpt-oss fails on bedrock:InvokeModel, not mantle.
• reason: model_unavailable → the ID isn't offered in $REGION. Use the resolve-bedrock-model-id procedure to suggest a cross-region inference-profile ID or a correct ID, re-confirm, and re-run AC4.
• reason: credentials / mantle_deps_missing / other → surface detail and follow B4's rule (fix and re-run; do not claim access is verified when it isn't).
• ok == true + reason: embedding_unprobed → the model is from an unrecognized embedding family, so the preflight could NOT actually invoke it (see probe_model() in preflight_bedrock.py) — this is ok: true at the JSON level but zero requests were sent, so nothing about actual access was observed either way. Do not report this as "enabled" in any form, verified or not — "enabled" asserts a fact this probe never checked. Tell the user access is unverified for this model (not "enabled — unverified"): ask them to confirm in the Bedrock console or with a manual test invoke before relying on it.
• ok == true + reason: throttled_ok → the probe reached the service and was throttled (ThrottlingException/ServiceQuotaExceededException/a 429 on mantle) — this proves the request was authorized, but it is not the same claim as "an invoke succeeded": no response was produced, so nothing about the model's actual behavior was observed. Tell the user access is confirmed authorized, but the probe itself was throttled — a quota concern to note, not a reason to distrust the access verdict.
• ok == true (any other reason, i.e. an actual invoke or InvokeModel call returned a real response) → tell the user access is confirmed working in $REGION for that ID.

AC5 — Summarize and stop

Give the user a per-model summary: <model_id> → enabled & working (a real invoke succeeded) / access unverified — no request made (embedding_unprobed; recommend a manual console/test check) / authorized (probe throttled) (throttled_ok; access is confirmed, but no response was observed) / enable access (naming the applicable prerequisite from the model_access branch — Marketplace permissions in commercial Regions; for GovCloud, the linked-commercial-account step (third-party models) plus the us-gov-west-1 console Model access page; plus the Anthropic FTU form where it applies) / grant <action> / not available in <region>, use <candidate>. Do NOT collapse embedding_unprobed or throttled_ok into enabled & working — each means something distinct to the user (no request made / authorized-but-no-response / actually confirmed working), and even a genuinely successful invoke on a first-time third-party model is not a permanent guarantee either: AWS auto-enables access in the background on first invoke (up to ~15 minutes to finalize), and a missing prerequisite during that window can make a later call fail with AccessDeniedException even though an earlier call succeeded — mention this for any model reported as newly working for the first time in this account. Then run the Contextual offers (final step). Do not continue into Assess, rewrite, or any code change — this mode is complete.

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Inline mode (platforms without an Agent/subagent dispatch tool)

If this platform has no subagent dispatch tool, run phases inline ONE AT A TIME, with a mandatory stop between phases:

1. Read exactly ONE agent definition (<plugin>/agents/<name>.md) — never load more than one phase's definition into context at once.
2. Follow it start-to-finish; write and validate the same phase-result file.
3. STOP. Report the phase outcome (validator CONTROL line + one-line summary) and ask the user to confirm before loading the next phase's definition. This sidebar is mandatory: it is the context-pressure release valve, and the phase-result file means nothing is lost if the user continues in a fresh session instead.

Warn the user up front that inline mode is slower and context-heavier than subagent dispatch, and that the rewriter phase performs git operations (branch, commits, worktree) directly in this session.

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Failure handling

• An agent dispatch dies (tool error, terminal failure) → the phase file is missing → validator exit 2 → the stateless fixer-retry path (which, finding no file to fix, re-runs the phase). Do not auto-retry more than the 2-retry cap.
• User aborts at any gate → confirm no SOURCE CODE was modified (C5 never started if aborted before then). Note that .migration/ and .saws-migrate/ artifacts do exist; show the undo commands from C7 if the user wants them gone.
• Assess skill fails → show the error and stop. User can re-run /aws-startup-advisor:llm-to-bedrock.

Contextual offers (final step)

Before you finish: your answer is not complete until you have run the Contextual offers (final step) below.

This is a mandatory closing step of every final recommendation — complete it before you consider your answer done. Run this offer check inline yourself; do not depend on loading another skill. The recommendation is decided on merit first; this step never changes, re-ranks, or re-opens it, and the offer is looked up only after the advice is final.

1. Read ../knowledge-base-for-startups/references/offers.md — the offer index (Offer · Summary · Keywords), one row per offer.
2. Judge relevance against what the founder actually surfaced this session. Surface an offer only if one holds: a named-product match (you recommended a specific partner product by name and it has a row); a direct intent/component match (an offer's Keywords directly serve a concrete workload, a component in the final recommendation, or a need the founder raised). Keep a high bar — if the match is weak, or you would be inferring an unstated need, append nothing. In this migration, only surface an offer that is a credit on a product the migration already kept or selected (e.g. LangSmith credits when the rewrite keeps their LangChain code); otherwise append none — never an offer that competes with Amazon Bedrock or anything else the rewrite or the gcp-to-aws design selects (e.g. Fireworks AI or another model-inference provider vs Bedrock).
3. If (and only if) one offer clears the bar, open only its detail file ../knowledge-base-for-startups/references/offers/<slug>.md and append exactly one quiet, dismissible italic line — stating the value and condensing eligibility faithfully from that file (drop no qualifying criterion; never fabricate), with the redeem link built from the canonical source_url plus a source attribution param — append ?source=ide-startupAdvisor-<host> (or &source=... if the url already has a query string), filling <host> from your execution environment (Claude Code → claude; AWS Startup Advisor IDE extension / VS Code → vscode; Cursor → cursor; Kiro → kiro; Codex → codex), or the generic ide-startupAdvisor when you cannot tell. Only append the query param; never change the destination. Show one or none, never a list. If none clears the bar, append nothing and do not mention offers, partner alternatives, or why none appeared.

Standard line:

***<Offer>** has an exclusive offer via AWS Activate — <value>, <eligibility>. [Redeem →](<source_url>?source=ide-startupAdvisor-<host>)*

Caps and control: at most one offer per response and often none; no more than one per five messages and two per session; show a given offer at most once per session and never one already shown, claimed, or dismissed; if the founder has muted offers, skip this step entirely. These per-five-messages, per-session, and already-shown caps are session-state limits; in a fresh session with no prior offers they are non-binding, so do not withhold an otherwise-qualifying offer merely because you cannot verify session history. See ../contextual-offers-for-startups/SKILL.md for the full rules — but perform the check inline; it must not depend on that skill being loaded.