/d — Jev Router

Routes a request in four parts. A deterministic pre-route.py guard keeps git/PR and security work on their quality gates. You, the session model, pick the agent, skill, and pipeline from the full routing manifest with /do's rule text. Jev answers the attachment questions that add extra skills, and validates that the restated outcome survives the route. A script validates your pick and computes the attachments.

Design rationale: ${CLAUDE_SKILL_DIR}/references/jev-classifier-design.md.

Phase Banners

Every phase: /d > Phase N: PHASE_NAME — description... After intent alignment resolves: === routing banner. Both required.

---

Phase 1: CLASSIFY

Resolve $SDIR: ${HOME}/.claude/scripts, falling back through .hermes/.factory/.codex/.reasonix, or the repo's scripts/ directory.

Step 1 — JEV_RESULT. When JEV_RESULT is already in context (the jev-route-injector hook ran the script before your first token), use it and skip the command below.

REQUEST_FILE=$(mktemp); printf '%s' "{user_request}" > "$REQUEST_FILE"
python3 "$SDIR/jev-route.py" --request-file "$REQUEST_FILE" --json-compact

Fields: available, jev_called, source, forced, stack, project, signals, signal_scores, domain_scores, error, latency_ms, usage. forced is the safety force route (pr-workflow, pr-pipeline, security) or null. project holds the request repository's languages, frameworks, and datastores (names only) or null. error is set when Jev was unavailable or its answers were unusable. Routing continues, and attach then carries only the pre-route stack and the agent domain floor.

Step 2 — Pick. Set the complexity from /do Phase 1 (Trivial, Simple, Medium, Complex). Trivial: go to Phase 1T.

For Simple and above, load /do's routing rules and the manifest. Read the rule text in full and apply every rule:

python3 "$SDIR/print-route-rules.py"
bash "$SDIR/get-routing-manifest.sh" --request-file "$REQUEST_FILE"

The manifest is large. If Bash truncates it or saves it to a file, read the whole file before you pick. Use JEV_RESULT.project when the request names no language or framework.

Return the decision as JSON. Do not print it:

{"agent": "<AGENTS: name or null>", "agents": ["<extra fan-out agents>"],
 "skill": "<SKILLS: name or null>", "pipeline": "<PIPELINES: name or null>",
 "complexity": "simple|medium|complex", "confidence": "high|medium|low",
 "reasoning": "<one sentence>",
 "fallback_reason": "general-purpose: <why no listed agent covers this>"}

fallback_reason is required when agent is general-purpose, and only then.

When JEV_RESULT.forced is set, its skill and pipeline are final: step 3 overrides your values for them. Pick the agent and agents that fit, as /do Phase 2 Step 1(a) does.

Step 3 — Finalize. Write JEV_RESULT and the decision to temporary files and run:

python3 "$SDIR/jev-route.py" --finalize --result-file "$RESULT_FILE" --decision-file "$DECISION_FILE" --json-compact
rm -f "$REQUEST_FILE" "$RESULT_FILE" "$DECISION_FILE"

Hold the output as ROUTE: agent, agent_source, skill, skill_source, pipeline, agents, attach, stack, complexity, confidence, reasoning, fallback_reason, source, signals, domain_scores, and problems. The script checks every name against the live manifest, applies the safety force route, fills an empty skill with workflow, and gives a null or general-purpose agent the owning agent of the skill. attach is the list of extra skills that ride with skill (for example programming for Go work, testing when tests are part of the work). It is the whole Phase 4 stack.

Gate: problems empty → Phase 2. problems set → fix the decision, run step 3 once more, and carry any problem that remains into task_spec.gaps. The output has an error key and no route fields → use your decision as ROUTE with attach set to [].

---

Phase 1T: TRIVIAL (complexity == Trivial)

Direct-handling path. It still goes through Phase 2 so the user outcome is restated and Jev validates it; use {"complexity": "trivial"} as the route file. After an aligned Phase 2 result, show Classification: Trivial, then answer or do the one-line action directly. Do not run Phases 3–5 or call build-dispatch.py. Stop there.

---

Phase 2: ALIGN INTENT (required for every /d route)

MANDATORY STOP: For every /d invocation, write PROPOSED_INTENT and run the validator on that exact text before any routing banner, dispatch, answer, edit, or other action. This requirement has no exception for force routes or trivial routes.

Before selecting the work method, write PROPOSED_INTENT: a concise one- or two-sentence restatement of what the user wants accomplished. State the outcome and each deliverable the request names or directly requires. Copy every explicit constraint in the user's words: limits ("only", "at most"), exclusions ("don't touch", "do not deploy"), required methods, and authorization boundaries. Do not add deliverables the user did not ask for, such as extra tests, docs, cleanup, refactors, or verification steps. Do not add notes about missing inputs or preconditions; the validator decides whether clarification is needed. Do not describe the selected agent, skill, or implementation mechanics as the outcome. Preserve the user's words where precision matters.

Put the request, the ROUTE JSON, and the proposed intent in temporary files rather than shell-splicing user text, then call:

python3 "$SDIR/jev-intent-align.py" \
  --request-file "$REQUEST_FILE" \
  --route-file "$ROUTE_FILE" \
  --proposed-intent-file "$INTENT_FILE" \
  --json-compact

The validator sends one bounded state and all independent questions together through the selected Jev transport. It checks whether the outcome and constraints are preserved, the route can cover the material scope, the restatement is too narrow, it introduces unrequested work, and essential clarification is needed. It returns aligned, clarification_needed, issues, and raw scores.

Show this before the routing banner:

Intent alignment (/d):
  -> Restated outcome: [PROPOSED_INTENT]
  -> Jev: [aligned|review|unavailable] [issues, if any]

Gate:

• clarification_needed == true → ask one concise question that names the essential ambiguity; do not dispatch until answered.
• alignment == aligned and complexity is Trivial → direct handling in Phase 1T; otherwise → Phase 3.
• alignment == review because scope is lost, work was added, or the route cannot cover the request → correct PROPOSED_INTENT or the route and run this validator once more. Carry unresolved issues into task_spec.gaps; do not silently proceed as though Jev approved it.
• alignment == unavailable or error → state that validation was unavailable, preserve the verbatim request and proposed intent in the task spec, then continue under the ROUTE result. Gateway outage must not become a false request rejection.

This is an instruction gate enforced by the /d contract, not a hook-enforced technical boundary. The user remains the final backstop if an agent violates it.

---

Phase 3: DECIDE (after aligned intent)

Use ROUTE's agent, skill, pipeline, and complexity as-is. The script validated them against the live manifest. agent_source: skill-default means the pick named no domain agent and the script used the skill's owning agent. A general-purpose agent carries ROUTE.fallback_reason. When complexity is null, use medium, except a single one-line trivial fix → simple.

Routing banner (Phase 2 intent block + routing block, both required, printed together):

===================================================================
 ROUTING (/d): [brief summary]
===================================================================

 Intent (/d):
   -> Restated: [PROPOSED_INTENT]
   -> Alignment: [aligned|review|unavailable] [— issues, if any]

 Selected:
   -> Agent: [ROUTE.agent] - [ROUTE.reasoning]
   -> Skill: [ROUTE.skill] - [ROUTE.reasoning]
   -> Attached: [ROUTE.attach, comma-separated, or "none"]
   -> Pipeline: [ROUTE.pipeline, if set]
   -> Source: [ROUTE.source] (confidence: [ROUTE.confidence])

 Invoking...
===================================================================

The Intent block must be populated from the Phase 2 validator run. Printing the banner with a placeholder or omitting the Intent block is a Phase 2 skip and is not allowed.

Gate: Agent+skill set, banner shown. Phase 4.

---

Phase 4: ENHANCE (attach skills)

stack = ROUTE.attach, in order, plus anti-rationalization-core. Copy the names exactly. Do not add, rename, or drop skills: the script built attach from these rules, and build-dispatch.py rejects any name absent from skills/INDEX.json.

Source	Attaches
JEV_RESULT.stack (pre-route, e.g. a .go file with PR or security work)	its entries, first
Agent domain floor	programming for Go, Kotlin, PHP, and Swift agents; kubernetes for kubernetes-helm-engineer; frontend for ui-design-engineer
domain_scores at 0.6 or higher	programming, frontend, kubernetes, testing, building-with-jev, research
tests_requested / comprehensive_review / objective_loop_worthy	testing / review / workflow
local_only	local-only shared pattern

At most three skills are attached beyond skill. Two adjustments stay with you:

• comprehensive_review attached review and a real multi-file diff exists: right-size-review.py outranks it, so drop review from stack.
• signals.research_needed is true: add research-coordinator-engineer to the fan-out agents.

Fan-out agents: union ROUTE.agents (your MULTI-AGENT RULE picks, each validated against the manifest) into the research_needed agent list, deduped. Dispatch fan-out agents as separate parallel Agent tool calls alongside the primary build-dispatch.py dispatch.

Gate: Stack applied. Phase 5.

---

Phase 5: EXECUTE

Build the task spec with request_verbatim unchanged and intent exactly PROPOSED_INTENT; include any unresolved alignment issue in gaps, then invoke build-dispatch.py:

python3 "$SDIR/build-dispatch.py" --json '{
  "agent": "<ROUTE.agent>", "skill": "<ROUTE.skill; omit when agent-only>",
  "pipeline": "<ROUTE.pipeline; omit when null>",
  "complexity": "<ROUTE.complexity>",
  "model": "<haiku|sonnet|opus from Model choice>",
  "context_mode": "summary",
  "provider": "<anthropic|openai|other>",
  "manual_model_override": false,
  "health": "-",
  "fallback_reason": "<REQUIRED when agent=general-purpose; omit otherwise>",
  "stack": ["<ROUTE.attach, in order>", "anti-rationalization-core"],
  "task_spec": {"request_verbatim": "<user message, unchanged>", "intent": "...",
                "constraints": "<applicable rules, limits, and authorization>",
                "decisions": "...",
                "gaps": "...",
                "acceptance": "<command> -> <expected>",
                "files": "<owned paths; optional line ranges>", "ownership": "<worker scope>",
                "operator_context": "..."},
  "flags": {"worktree": false, "local_only": false, "thinking_override": null},
  "token_remaining": 480000
}'

Model choice

Set model from the task type, not inherit. Pass the same model on every Agent call, including Explore and Plan.

Task type	Model	Cost O/S/H per run (USD)	Deciding Jev evidence
locate, lookup, cite lines	haiku	0.43 / 0.19 / 0.02	haiku 0.91
mechanical edit (rename, 11 files)	haiku	0.26 / 0.11 / 0.007	haiku 0.93 with scope evidence
review, find planted bugs	haiku	0.4 / 0.15 / 0.01	haiku 0.95 (n=1)
debug: root cause + regression test	sonnet	0.39 / 0.20 / 0.015	sonnet 0.88; haiku test 2.00 vs 2.86
feature in an existing pattern (7 files)	sonnet	1.30 / 0.32 / 0.088	sonnet 0.64; Opus edited unrequested docs
long lane (9-10 files, UI text)	sonnet	1.66 / 0.46 / 0.25	sonnet 0.77
no row fits	sonnet	n/a	sonnet 0.54 / haiku 0.45 (split)

Opus won no measured task.

Tiers (model_policy): low-risk = haiku, standard = sonnet, high-risk (unmeasured, costly miss) = opus. max-power stays opus/xhigh and needs manual_model_override=true. Use inherit only when no row or tier fits and the session model is acceptable.

Escalate on a miss: haiku, then sonnet, then opus. Retry once on the next model when an objective check fails (tests, tsc, ruff, acceptance command) or a Jev blocker Noul is at least 0.6.

Evidence limits: one repo, one run per cell; the review row is n=1. Full record, Jev questions, probabilities, and the re-measure method: skills/meta/d/references/model-task-fit.md.

The builder validates each name against its index, then emits the dispatch action. For Complex or creation requests, apply creation detection, plan-file gating, quality-loop, workflow dispatch, fan-out, and auto-pipeline fallback.

Gate: Agent invoked, results delivered.

---

Post-execute: GRILL-JEV (plan/spec/design output)

After any execution that produces a plan, spec, or design artifact, run grill-jev automatically before declaring the work complete. This applies whenever the agent's output contains phases, steps, checklists, or a structured implementation plan.

Detection: the agent wrote task_plan.md, a spec file, a design document, or the response itself is a structured plan with numbered steps or phases.

# File artifact
python3 scripts/grill-jev.py --file task_plan.md --mode plan

# Inline plan (write to temp file first, then grill)
python3 scripts/grill-jev.py --file /tmp/plan_output.md --mode plan

Print the findings report. If high-signal findings exist (exit code 1):

• Show findings to the user
• Ask whether to address findings before proceeding or accept and move on

If no high-signal findings (exit code 0): proceed, note "grill-jev: clean".

Skip grill-jev when:

• The output is code only (no plan structure) — use --mode code instead
• The output is a pure research response with no actionable steps
• grill-jev is itself the requested action (avoid recursion)

---

Error handling

Errors inside jev-route.py resolve to an error key and an empty evidence set. Routing continues; Phase 2 reports an unavailable validation.

When changing how the router or validator builds or sends Jev requests, apply skills/shared-patterns/jev-production-lessons.md: stage requests at or under the reliable size, send each stage's requests together, and retry by status code.

References

• ${CLAUDE_SKILL_DIR}/references/jev-classifier-design.md — request/response contract, what Jev still decides, design decisions
• ${CLAUDE_SKILL_DIR}/references/model-task-fit.md — model per task type, measured cost and Jev scores, re-measure method
• ${CLAUDE_SKILL_DIR}/SPEC.md, ${CLAUDE_SKILL_DIR}/EVAL.md — maintenance contract and regression cases (load only when creating, evaluating, or redesigning this skill)
• scripts/jev-route.py, scripts/print-route-rules.py, scripts/jev-intent-align.py, scripts/jev_transport.py, scripts/jev_vercel.py, scripts/jev_gateway/jev_vercel_gateway.mjs, scripts/pre-route.py, scripts/routing-manifest.py, scripts/build-dispatch.py
• Rule text: skills/meta/do/SKILL.md Phase 2 (printed by print-route-rules.py)
• Jev hook: hooks/jev-route-injector-userprompt.py (UserPromptSubmit) precomputes JEV_RESULT