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gaia-full-pipeline

End-to-end Gaia curation pipeline orchestrator. Routes each phase to its dedicated skill and keeps you on track from first discovery to closed intake. Use when starting a fresh curation run or when you want a single skill to hold the sequence together without duplicating each phase's instructions. Trigger phrases: "/gaia-full-pipeline", "run the full pipeline", "start a curation from scratch", "take this skill through the full pipeline", "curate end-to-end", "pipeline run", "full curation flow". Fused from: gaia-curate + gaia-curate-chain + gaia-curate-dynamic + gaia-curate-trending + gaia-bot-curate + gaia-draft-curate + ev-pipeline + gaia-ingest + gaia-intake-close.

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Have one repo URL and want full automation? Run /gaia-quick-curate — 2 human gates, auto-merge. This document is the manual reference explaining what runs inside each gate.

gaia-full-pipeline

Orchestrates the complete curation lifecycle. Each phase delegates to its canonical skill — this skill is the routing layer, not the implementation. Read the referenced skill for the exact commands at each phase.

Lost at any point? Run /gaia-consult to open the reference document with the exact command for your current phase.


Phase 0 — /ev-scout (pre-curation context, no authority)

Before any Phase 1 strategy, invoke /ev-scout with the declared source, skill(s), or intake batch. For Strategy F, this includes already-submitted gaia push intakes. Hand the resulting scout packet to the mapping/curation agent and the L4 reviewer so they see both promising generic research and named-implementation leads before deciding topology.

Scouting favors breadth: possible papers, peer reviews, benchmarks, rivals, counterevidence, and implementation provenance. Leads remain unverified. Do not derive Trust Magnitude, ranks, or ingestion from the packet. The existing later /ev-discovery Phase 0 and /ev-pipeline verification are separate and remain authoritative. Missing search tooling is non-blocking; carry explicit limitations forward. Reuse/refresh existing scout packets instead of repeating the same searches.

Phase 1 — Choose a discovery strategy

The pipeline starts differently depending on whether you already know what you're curating or need to find it first. Pick exactly one path. All strategies stop at L4 — none touch the registry.

Strategy decision guide

You want to…Use
Curate one specific skill you already have the URL for/gaia-curate
Curate a small batch of up to ~5 where recoverability matters (each step checkpointed, retries on failure)/gaia-curate-chain
Curate a large or broad batch with parallelism across multiple candidates (fan-out to Luna workers)/gaia-curate-dynamic
Discover what's trending — you don't have a specific skill in mind, you want to see what's hot on configured marketplaces/repos/gaia-curate-trending
Process bot crawler output already sitting in bot/* branches/gaia-bot-curate
Review pending gaia push intake batches in registry-for-review/ that someone else already submitted/gaia-draft-curate

Rule of thumb: if you're declaring ("I want to curate this skill at this URL"), use A or B. If you're discovering ("show me what exists"), use D. If you're processing a queue, use E or F.


Strategy A — /gaia-curate · Single declared skill {#strategy-a}

When: You have one specific upstream SKILL.md URL. Lowest overhead.

Depth: One candidate. No parallelism. No checkpointing.

Breadth: 1 skill per run.

gaia dev list --generic --json > /tmp/generic-snapshot.json
gaia dev prefill --source "<blob-url>" --output /tmp/prefill-output.json
# … write discovery-packet-v2 JSON …
python3 scripts/validate_discovery_packet.py \
  --generic-snapshot /tmp/generic-snapshot.json <packet>.json
# → Stop at L4

→ Full commands: /gaia-curate · CURATION-CORE.md


Strategy B — /gaia-curate-chain · Small batch, checkpointed {#strategy-b}

When: You have 2–10 candidates and want recoverability. Each candidate transition is atomically checkpointed; a failed field retries in isolation without restarting the whole batch. Good for higher-risk or unfamiliar sources where you expect some defers.

Depth: One candidate at a time, sequentially. Max 2 transient-fetch retries, 1 Luna High repair per candidate.

Breadth: Small batch. Checkpointed run ledger under generated-output/curate-discovery/<run-id>/run.json.

/gaia-curate-chain <source-or-small-batch>
# Checkpoints each candidate → validates → persists to registry-for-review/discovery-packets/
# On failure: emits DEFER with exact resume instruction (candidate ID, failed field, next command)
# → Stop at L4

→ Full protocol: /gaia-curate-chain


Strategy C — /gaia-curate-dynamic · Large batch, parallel workers {#strategy-c}

When: You have a broad source manifest (many repos, a marketplace page, a topic) and want throughput. A Sol/Terra orchestrator shards work to Luna Light harvesters and mappers. Adversarial review only for risky candidates (NEW_GENERIC, fusion proposals, attribution conflicts).

Depth: Adversarial review for risky cases only (proposer/refuter isolated passes). Safe/straightforward candidates skip adversarial cost.

Breadth: Large batch. Configurable concurrency. Resumable cost log in usage.jsonl. Effective concurrency = min(requested, observed harness capacity, remaining shards, budget).

/gaia-curate-dynamic <broad-source-manifest>
# Preflight → capacity canary → shard → harvest → map → [adversarial for risky] → assemble → L4
# State: generated-output/curate-discovery/<run-id>/
# → Stop at L4

Note: Requires configured harness binary (CLAUDE_BIN, CODEX_BIN, or HERMES_BIN). Runs sequentially if concurrency cannot be established.

→ Full protocol: /gaia-curate-dynamic


Strategy D — /gaia-curate-trending · Discover what's trending {#strategy-d}

When: You don't have a specific skill in mind. You want to snapshot configured external skill sources (marketplaces, GitHub topics, model hubs) and surface the top candidates by trend band. Best for periodic sweeps or identifying what to curate next.

Depth: Trend bands only (HOT, RISING, NEW, STEADY, UNKNOWN) — mechanical, never a model judgment. No evidence scoring, no TM, no trust signal.

Breadth: Up to 5 candidates per source page, all configured sources in the run manifest. Resumable with RESUME <run-id>.

/gaia-curate-trending <source-manifest-or-run-id>
# Snapshot → trend-band → fetch SKILL.md → dedupe → map → L4-REVIEW.md
# State: generated-output/curate-discovery/<run-id>/
# → Stop at L4

Operator controls: NEXT (advance to next candidate), STOP (write resumable checkpoint), RESUME <run-id>.

→ Full protocol: /gaia-curate-trending


Strategy E — /gaia-bot-curate · Process bot crawler branches {#strategy-e}

When: The automated crawler has already run and left bot/* branches on the remote. You are processing crawl output — filtering, accepting, rejecting, integrating — not discovering new skills yourself.

Depth: Human-in-the-loop filtration per candidate. Fusion analysis included (does it combine capabilities in a novel way?). Demerit assignment at 3★+.

Breadth: All pending bot/* branches in one run. Narrows by source type (github, vscode-marketplace, huggingface) for triage.

git fetch --all --prune
git for-each-ref refs/remotes/origin/bot   # list branches
# Triage → accept/reject/needs-evidence → gaia dev add/evidence/calibrate
# Delete consumed bot branches after review branch exists
gaia dev docs && gaia dev validate
gh pr create ...

Key difference from other strategies: this is the only strategy that runs gaia dev add and mutates the registry directly during curation — it is not discovery-only.

→ Full protocol: /gaia-bot-curate


Strategy F — /gaia-draft-curate · Review pending gaia push batches {#strategy-f}

When: Someone has already run gaia push and proposals are sitting in registry-for-review/skill-batches/. You are the intake gate deciding which proposals move forward.

Depth: Read-only triage — accept, rename, duplicate, needs-evidence, or reject. No registry mutation. Hands off to /gaia-curate-chain or /gaia-curate for accepted proposals, or directly to /gaia-intake-close (Phase 6 Path B) for rejected proposals.

Breadth: All pending batches in registry-for-review/skill-batches/*.json.

git status --short --branch
python3 scripts/validate_intake.py
gh issue list --state open --search 'label:intake'
# Review decision table → produce handoff packet
# → Hand accepted to /gaia-curate-chain or /gaia-curate

→ Full protocol: /gaia-draft-curate


Phase 2 — L4 Human Gate 🛑

No skill — human only. All strategies converge here.

Every strategy stops after producing review-ready discovery-packet-v2 files in registry-for-review/discovery-packets/. (Exception: /gaia-bot-curate may go directly to registry mutation — re-read its protocol.)

For strategies A–D and F, L4 means:

  1. Open each packet JSON and its companion advisory assessment receipt (generated-output/curation/<candidate>.assessment.json). Verify mapping decision, blob/ URL, and verbatim description against curation principles.
  2. Ratify topology using gaia dev ratify with explicit human review attestation flags (--assessment, --reviewed-by, --approval-ref, --reason, and --acknowledge-human-review), which atomically attaches l4Resolution with ratified generic, named, upstreamSkillFileUrl, and humanReview metadata. Operator authorization alone is not L4 approval; no manual append of fake resolutions.
  3. Re-validate: python3 scripts/validate_discovery_packet.py --generic-snapshot /tmp/generic-snapshot.json <packet>.json

Checkpointing — Phases 3–6 (#1790)

Phase 1's discovery strategies already checkpoint to generated-output/curate-discovery/<run-id>/run.json (strategies B/C above), but that convention stopped at L4 — a background agent restarting mid-Phase-4 or mid-Phase-5 had nothing to resume from and the orchestrator had to inspect partial output by hand. Extend the same convention through the rest of the pipeline: write generated-output/curate-discovery/<run-id>/status.json after each Phase 3–6 step completes, shaped as:

{
  "phase": 4,
  "state": "evidence-verified",
  "artifacts": ["evidence/by-type/repo-own.md"],
  "nextCommand": "gaia dev evidence contributor/skill \"<url>\" --type repo-own ..."
}
  • phase — the pipeline phase number (3–6) just completed.
  • state — a short label for what that phase produced (e.g. packet-pushed, evidence-verified, ingested, closed).
  • artifacts — paths touched or produced by that step, for a resuming agent to inspect before re-running anything.
  • nextCommand — the exact next command to run, so resume never requires re-deriving pipeline state from prose.

Update status.json in place (not append) — it always reflects the latest completed step, not a history log.


Phase 3 — Branch + Push (packet only — no PR here)

Skills: /pr

There is exactly one canonical PR route for an intake: the workflow-generated review/meta/intake-<N> opened when the maintainer applies intake:evidence-approved (Phase 3's label table below). Do not also open a hand-cut PR from this branch — running both routes produces duplicate PRs for the same intake. review/meta/<handle>--<skill> exists only to carry the intake packet/batch itself so gaia push --from-file has something to commit; it is never the PR that lands the skill.

git checkout -b review/meta/<handle>--<skill>
gaia push --from-file <packet>.json --dry-run   # preview
gaia push --from-file <packet>.json             # opens intake issue (auto-labels: intake + needs-triage)
git add . && git commit -m "feat(intake): ..." && git push -u origin <branch>
# No `gh pr create` here — wait for intake:evidence-approved to open the canonical PR (see below).

intake:* label lifecycle — machine gates, applied in order

LabelApplied toWho appliesWhat it triggers
intakeIssue + PRAuto (gaia push) or youRoutes into the intake queue
needs-triageIssueAuto (gaia push)Marks as awaiting review
intake:topology-approvedIssueMaintainer onlyWorkflow fires: adds intake:evidence-review, removes needs-triage, posts handoff comment
intake:evidence-reviewIssueAuto (workflow)Signals agent to prepare Stage-1 seed + Phase-0 plan
intake:evidence-readyIssueAgent / youSignals evidence plan is ready for human approval
intake:evidence-approvedIssueMaintainer onlyWorkflow fires: opens single draft review/meta/intake-<N> PR
intake:rejectedIssueMaintainer onlyCloses intake via /gaia-intake-close (Phase 6 Path B); posts rejection findings, path to acceptance, and badge status note
human-proposalPRAuto (triage workflow)Routes non-bot PR for human review
needs-reviewPRAuto (triage workflow)Signals PR needs a reviewer

Gate constraint: intake:evidence-approved requires both intake:evidence-review and intake:evidence-ready to already be present — the workflow errors if either is missing. intake:topology-approved and intake:evidence-approved are rejected by the workflow if the actor is not admin/maintain/write.

# Auto-triage usually applies PR labels, but if not:
gh pr edit <PR> --add-label "intake" --add-label "human-proposal" --add-label "needs-review"

# When ready to approve topology (maintainer only):
gh issue edit <ISSUE> --add-label "intake:topology-approved"
# ↑ fires intake-approval.yml — watch it:
gh run list --workflow=intake-approval.yml

Phase 4 — Evidence Verification

Skill: /ev-pipeline

Sub-phaseSkillSkip when
Phase 0 — ev-discovery/ev-discoveryStage-1 intakes; only run for promotion candidates needing benchmark-result, arxiv, or peer-review
Phase 1 — ev-collection/ev-collectionNever — rebuilds the whole-registry lake
Phase 2 — ev-star-verification/ev-star-verificationNo github-stars-own rows
Phase 2B — ev-benchmark-verification/ev-benchmark-verificationNo benchmark-result rows
Phase 3 — ev-adversarial-audit/ev-adversarial-auditNever
Phase 4 — ev-link-validation/ev-link-validationNever

Human gate: Review source report. Approve rows. Remove dead-link, subjective, or type-mismatched rows before ingest.


Phase 5 — Evidence Ingest

Skill: /gaia-ingest (single row) · /gaia-ingest-batch (multiple rows)

GAIA_OPERATOR_OVERRIDE=1 gaia dev evidence contributor/skill "<url>" \
  --type <evidence-type> <numeric-flags> \
  --notes "..." --source-started-at YYYY-MM-DD --no-build
# … repeat per row …
GAIA_OPERATOR_OVERRIDE=1 gaia dev build
PYTHONPATH=src python3 scripts/trust_appraise.py --skill contributor/skill
# → Human approves calibration
GAIA_OPERATOR_OVERRIDE=1 gaia dev calibrate contributor/skill 4★
GAIA_OPERATOR_OVERRIDE=1 gaia dev validate

Trust Magnitude → star grade (per registry/schema/meta.json evidence.gradeThresholds; see src/gaia_cli/grading.py):

GradeTMMax stars
—0–191★ (ungraded)
C≥202★ (badge floor)
B≥503★
A≥1004★
S≥2505★–6★

→ Methodology: /trust-methodology-consult


Suite detour — between Phase 5 and Phase 6 🔀

Run only when the intake is a suite capstone (contributor has 3+ named skills being fused).

Skill: /gaia-fuse-full-suite

ls registry/named/<contributor>/        # confirm all components exist
gaia dev fuse <fusion-id> \
  --name "<Fusion Name>" \
  --description "<one-sentence synthesis>" \
  --prereqs <id-1>,<id-2>,... \
  --named-capstone <contributor>/<capstone-slug> \
  --suite-components <named-id-1>,<named-id-2>,...
GAIA_OPERATOR_OVERRIDE=1 gaia dev validate

Suite rank gates (TM is sole gate):

  • 4★ Extra — Origin Contributor recorded + TM ≥ 100
  • 5★ Ultimate — Origin in suiteComponents + 5 A-graded origins + TM ≥ 250
  • 6★ Apex — full 6-predicate Apex Gate (see META.md)

Phase 6 — Close

Skill: /gaia-intake-close

Path A: Accepted Intake (PR Merged)

When the promotion PR passes CI and is approved:

gh pr ready <PR>
gh pr checks <PR>                                   # wait for green
gh pr merge <PR> --subject "feat(registry): ... [closes #ISSUE]"
gh pr comment <PR> --body-file /tmp/pr-close-comment.md
gh issue comment <ISSUE> --body-file /tmp/issue-close-comment.md
gh issue close <ISSUE>
GAIA_OPERATOR_OVERRIDE=1 gaia dev docs             # regenerate Class S artifacts

Posts full evidence audit findings, /trust-appraise scores, live badge image embeddings (![alt](url)), and the universal Powered by Gaia badge snippet.

Path B: Rejected or Early-Exit Intake (Triage or Review Rejection)

When an intake is rejected at triage (Phase 1 Strategy F / /gaia-draft-curate), L4 review (Phase 2), batch staging (Phase 3), or evidence audit (Phase 4): Do not leave the intake issue hanging without a structured closing record. Route directly to /gaia-intake-close to close the intake cleanly:

# 1. Apply intake:rejected label
gh issue edit <ISSUE> --add-label "intake:rejected"

# 2. Post structured closing comment (findings table, path to acceptance, mandatory badge status note)
gh issue comment <ISSUE> --body-file /tmp/issue-close-comment.md

# 3. Close the intake issue
gh issue close <ISSUE>

# 4. Clean up any staged batch branch if pushed
git push origin --delete review/meta/<handle>--<skill> 2>/dev/null || true

The closing comment must include:

  • Rejection findings table with falsifiable rationale per skill (unattributed, product-coupled, unfalsifiable)
  • Actionable path to acceptance (generalization advice, proper attribution, required evidence)
  • Mandatory badge status note providing the universal Powered by Gaia badge, explaining the 2★ badge floor for named skill badges, and linking to https://gaiaskilltree.com/badges/
  • Gracious acknowledgment to the contributor

Re-entry map — jumping back in mid-pipeline

Where you areInvoke
Single declared skill/gaia-curate
Small batch, need recoverability/gaia-curate-chain
Large batch, need parallelism/gaia-curate-dynamic
Want to discover trending skills/gaia-curate-trending
Processing bot crawler branches/gaia-bot-curate
Reviewing pending gaia push batches/gaia-draft-curate
Post-L4, ready to push/pr
Evidence collection only/ev-collection
Star verification only/ev-star-verification
Adversarial audit only/ev-adversarial-audit
Link validation only/ev-link-validation
Single evidence row/gaia-ingest
Batch evidence rows/gaia-ingest-batch
Suite fusion only/gaia-fuse-full-suite
Closing comments (accepted or rejected)/gaia-intake-close
Regenerate artifacts only/regen or /gaia-docs-sync
Lost / unsure of command/gaia-consult

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Measure CI churn cost for a pull request: how many commits and how many CI-compute seconds were burned on avoidable "fix the CI" push rounds. Use when you want to understand the efficiency cost of a PR's iteration cycle, get actionable pre-push check suggestions, or include a churn summary in a pipeline report. Triggers: "ci churn", "how much CI did we burn", "avoidable commits", "ci efficiency", "wasted CI time", "churn report", "/ci-churn". Called automatically by /fp-drift at pipeline close.

日本語の概要は準備中です。原文の説明を表示しています。

gaia-research/gaia-skill-tree232026年10月13日 更新

Produce the rendered evidence a human-gated frontend PR owes its reviewer: capture each changed surface (plus the before state for anything modified, plus mobile width), then generate one self-contained local HTML page the founder can open and review without checking anything out. Use when a PR is gated by the "Frontend changes are HUMAN-GATED" section of CLAUDE.md, or when the user says "/design-gate", "screenshot evidence for this PR", "prep this for founder review", "build the design evidence page", "what does this look like before and after". The page comes from a template script, not hand-written markup. Output is session-local and never committed. Do NOT use for non-gated changes: typo/grammar/factual fixes in an unchanged block, dead link targets, alt/ARIA fixes with no visual change, cache-bust bumps, or refactors with no rendered diff.

日本語の概要は準備中です。原文の説明を表示しています。

gaia-research/gaia-skill-tree232026年10月13日 更新

One-fix-at-a-time, approval-gated design fix recovery. Given a checklist of fixes (handover doc, CHECKLIST.md, or inline list), applies each individually, presents a diff, and waits for an explicit APPROVE / REJECT / NO CHANGES decision before committing. Trigger phrases: /design-iteration, "go through the checklist one by one", "fix these one at a time", "bucket recovery", "design fix pass".

日本語の概要は準備中です。原文の説明を表示しています。

gaia-research/gaia-skill-tree232026年10月13日 更新

Playbook for correcting a named skill's star level to match its computed Trust Magnitude grade, with an independent post-calibration verification pass and a CLI-logged timeline event (never hand-written). Use when: recalibrating a skill's level, fixing a stale or drifted star rating, running a batch of `gaia dev calibrate` corrections, or confirming a past calibration actually landed. Triggers: "calibrate <skill>", "recalibrate the trust magnitude backlog", "promote/demote <skill> to N stars", "why is <skill> still N stars", "verify a calibration actually landed", "Hall of Heroes ranking is wrong".

日本語の概要は準備中です。原文の説明を表示しています。

gaia-research/gaia-skill-tree232026年10月13日 更新

Close out a completed EPIC / sprint end-to-end: author a fact-verified meta-post announcing what shipped, archive the EPIC's handovers, snapshot orchestrator memory, gate the integration→main merge on CI-green + explicit human go, drive the version bump (merge-commit conventions that auto-sync classifies), and verify the release reached the registry/PyPI. Use when the user says "close out EPIC <n>", "wrap up the sprint", "ship the meta shift", "/epic-close", or asks to finalize an integration branch that is ready to merge to main. Generalizes the EPIC 1002 (Yggdrasil II) close-out. This skill orchestrates — it authors founder docs directly (superadmin) and delegates code/regen to workers; it NEVER merges to main without the human gate.

日本語の概要は準備中です。原文の説明を表示しています。

gaia-research/gaia-skill-tree232026年10月13日 更新

Run this skill for Phase 3 of the evidence verification pipeline — the adversarial audit step. Use when you need to check the evidence data lake for bad data before ingestion: dead links, wrong URL formats (tree/ vs blob/), subjective wording ("elite", "high-quality"), stale migration notes, benchmark catalog misuse/vendor-claim leakage, or skills whose star evidence conflicts with classified evidence level. Triggers on phrases like: "audit the data lake", "adversarial check", "ev-adversarial-audit", "check for noise in evidence", "flag bad evidence", "run the audit phase", "quality check the by-type files", or any reference to Phase 3 of the pipeline.

日本語の概要は準備中です。原文の説明を表示しています。

gaia-research/gaia-skill-tree232026年10月13日 更新

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