do

name	do
promoted_to	native-router
description	Classify user requests and route to the correct agent + skill. Primary entry point for all delegated work.
user-invocable	true
argument-hint	<request>
allowed-tools	["Read","Bash","Grep","Glob","Skill","Task"]
routing	{"triggers":["route task","classify request","which agent","delegate to skill","smart router"],"category":"meta-tooling"}

/do - Smart Router

ROUTER, not worker. Classify → agent+skill → dispatch. All execution goes to agents. Catching yourself reading/writing code or analyzing — pause and route to an agent. Main: Classify→Select→Dispatch→Evaluate→Re-route→Report.

Do the whole thing (tests+docs). Product, not plan. Permanent solve over workaround. Search before building; test before shipping. Decompose into agent-sized tasks. The result reads as "that's done," not "that's a start." Partial → follow-up. Inject Simple+. Confidence in handling directly is a signal to route.

Dense-Complete Writing (build-dispatch.py injects; skills/shared-patterns/dense-complete-writing.md). User: banners+summary. Internal: JSON/reasoning/stacking (Verbose overrides).

Instructions

Phase Banners (MANDATORY)

Every phase: /do > Phase N: PHASE_NAME — description... After Phase 2: === routing banner. Both required.

---

Phase 1: CLASSIFY

Read CLAUDE.md first.

Complexity	Agent	Skill	Direct
Trivial	No	No	ONLY user-named file by path
Simple	Yes	Yes	Route
Medium	Required	Required	Route
Complex	2+	2+	Route

Beyond user-named file = Simple+, MUST route. Uncertain → UP. Depth: references/progressive-depth.md. NOT Trivial: repos/URLs, opinions, git, codebase Qs, retro, comparisons.

Parallel FIRST: 2+ failures / 3+ subtasks → multiple Agent tools. Research→research-coordinator-engineer; coord→project-coordinator-engineer; plan+exec→subagent-driven-development; feature→feature-lifecycle (.feature/→feature-state.py status). Force Direct: OFF.

Creation Detection (MANDATORY): create/scaffold/build/"add new"/"new [component]" targeting agent/skill/pipeline/hook/feature/plugin/workflow/voice. ANY + Simple+ → is_creation=true, Phase 4 Step 0. Not: debug/review/fix/refactor/explain/audit.

Gate: Complexity set. Creation → [CREATION REQUEST DETECTED]. Trivial: direct. Simple+: Phase 2.

---

Phase 2: ROUTE

Semantic intent. Prefer FORCE. Keywords hint, never gate. "send my commits to the server" = "git push".

Pre-route (ONCE, before fast-path)

SDIR="${HOME}/.claude/scripts"; [ -d "$SDIR" ] || SDIR="${HOME}/.hermes/scripts"; [ -d "$SDIR" ] || SDIR="${HOME}/.factory/scripts"; [ -d "$SDIR" ] || SDIR="${HOME}/.codex/scripts"; [ -d "$SDIR" ] || SDIR="${HOME}/.reasonix/scripts"
REQUEST_FILE=$(mktemp); printf '%s' "{user_request}" > "$REQUEST_FILE"
python3 "$SDIR/pre-route.py" --request-file "$REQUEST_FILE" --json-compact
rm -f "$REQUEST_FILE"

→PRE_ROUTE_RESULT (once).

Fast path: PRE_ROUTE_RESULT high-conf force_route + pr-workflow/security → skip 0/1.5/1, dispatch direct. Keep banner+overrides+P3+P4. [do-route] health=-. Agent: pre-route→domain→general-purpose.

Step 0: Self-route

Read the manifest (hash-gated cache or regenerate):

bash "$SDIR/get-routing-manifest.sh"

Use bash explicitly so routing does not depend on the script's executable bit.

Internal JSON; [do-route] = sole trace.

Routing rules (ALL apply):

SECTION-INTEGRITY (HARD — never violate):
agent∈AGENTS|null, skill∈SKILLS|null, pipeline∈PIPELINES|null.
No fit→null→general-purpose. Never skill→agent. FORCE skills: skill slot only.

FORCE-ROUTE — select when domain matches SEMANTICALLY (meaning, not words):
- "push my changes" → pr-workflow (FORCE) ✓ (git push)
- "push back on this design" → NOT pr-workflow (resist/argue)
- "configure my fish shell" → fish-shell-config (FORCE) ✓
- "fish for bugs" → NOT fish-shell-config (search for bugs)
- "quick fix to the login page" → quick (FORCE) ✓
- "quick overview of the architecture" → NOT quick (exploration)

PIPELINE — both: triggers match + multi-phase benefit. Mostly null.
"vexjoy voice article"→voice-writer ✓ | "research+sources"→research-pipeline ✓ | "fix typo"→null
Comprehensive-review outranked by right-size-review when real diff exists.

GENERAL: most specific. Agent=domain, skill=method. GENUINE git/version-control ops (actually pushing code, committing files, opening/merging a PR) → ALWAYS pr-workflow. Metaphorical uses ("commit to a decision", "merge ideas/branches in your head", "push back on a proposal") → NEVER pr-workflow.

Step 0b: Apply the routing decision

Low conf → verify INDEX.

Skill-greediness gate (HARD — non-negotiable for Simple+). Null skill → pick: review→systematic-code-review, debug→workflow (systematic-debugging), refactor→workflow (systematic-refactoring), audit→systematic-code-review (whole-repo→full-repo-review), explain→codebase-overview, compare→decision-helper (agent A/Bs→agent-comparison), plan→planning, loop→objective-loop. Fallback: objective-loop.

Section validator (MANDATORY before dispatch):

agents = tokens(manifest, "AGENTS:", "SKILLS:")
skills = tokens(manifest, "SKILLS:", "PIPELINES:")
if route.agent not in agents:
    if route.agent in skills: route.skill ||= route.agent
    route.agent = None; record_misroute(...)
route.agent ||= "general-purpose"

No pair→general-purpose+objective-loop. [cross-repo]→.claude/agents/. Code→domain agents.

Step 1.5: Health (shadow-only)

python3 "$SDIR/learning-db.py" route-weights --json

health_adjust() → keep|demote|tiebreak. Recorded, never alters route. Activation gated on first negative signal (docs/route-loop-validation.md). Demote: conf<0.30+fail>=3+n>=5. Tiebreak: conf<0.35+healthier alt. Force-route/security→keep. n<5→keep. build-dispatch.py emits marker on DECISION.

Step 1: Safety-net (reads PRE_ROUTE_RESULT)

(a) force_route pr-workflow/security disagrees → override. Git/security MUST hit quality gates. (b) Phrase guards suppress false matches → Step 0.

Step 2: Apply skill override — "review"→systematic-code-review, "debug"→workflow (systematic-debugging pipeline), "refactor"→workflow (systematic-refactoring pipeline), "TDD"→test-driven-development. Full table in INDEX.

Step 3: Routing banner (MANDATORY — first visible output)

===================================================================
 ROUTING: [brief summary]
===================================================================
 Selected:
   -> Agent: [name] - [why]
   -> Skill: [name] - [why]
   -> Pipeline: PHASE1 → PHASE2 → ... (if pipeline; phases from skills/workflow/references/pipeline-index.json)
   -> Extra Rigor: [verification patterns for code/security/testing when needed]
 Invoking...
===================================================================

Trivial: Classification: Trivial - [reason], Handling directly.

Learning: hooks below.

Gate: Agent+skill set, banner shown. Phase 3.

---

Phase 3: ENHANCE

Stack on signals.

Signal	Enhancement
Substantive	Retro knowledge when material
"with tests"/"production ready"	test-driven-development+verification-before-completion
"research needed"/"investigate first"	research-coordinator-engineer
Comprehensive/thorough/full review or 5+ files, no diff	parallel-code-review (Security, BizLogic, Arch)
Multi-file review, real diff	right-size-review.py; T1→3,T2→12,T3→17,T4→27. CRITICAL+1. Outranks comprehensive-review.
Complex implementation	Offer subagent-driven-development
"local only"/"no push"/"keep it local"/"stay local"	Inject shared-patterns/local-only.md
Voice profile (e.g. voice-example-profile)	Stack voice-writer; voice-*=profile
Interview-mode heuristic	planning — depth-first-interview.md
Objective with done-criteria / "loop until done"	Stack objective-loop

Review overlap: real-diff row wins; fallback only without diff.

Interview heuristic. Short, no file/symbol, ambiguous. Spec:

Example	?	Why
"i'm not sure how to approach this complex build"	Y	Vague+no target
"fix the typo on line 42 of foo.py"	N	File+loc
"build a thing that does X"	Y	No file
"add a test for parseConfig in src/config.go"	N	Symbol+file
"where do i even start with this rewrite"	Y	No subject
"rename cfg to config in internal/"	N	Mechanical

Check pairs_with before stacking. Skills with built-in verification gates may suffice.

anti-rationalization-core always + verification-checklist (code/debug) + anti-rationalization-review + anti-rationalization-security + anti-rationalization-testing; external: untrusted-content-handling. Max: load verification-before-completion references/anti-rationalization-enforcement.md.

Gate: Enhancements applied. Phase 4.

---

Phase 4: EXECUTE

Step 0: Creation — ADR at adr/{name}.md, adr-query.py register, plan.

Step 1: Plan (Simple+) — task_plan.md; skip Trivial.

Step 1b: Quality-loop (Medium+ code mod) — references/quality-loop.md 14 phases. P2 agent=implementation. Force-route in loop. Skip non-code/Trivial/Simple.

Step 1c: Workflow — Pipeline pick or Complex no pick or explicit → ${CLAUDE_SKILL_DIR}/references/workflow-dispatch.md. Both 1b+1c → quality-loop OUTER, workflow in IMPLEMENT.

Step 2: Invoke agent

build-dispatch.py (MANDATORY) — source for [do-route], thinking, budget, Task Spec, injections, worktree/local-only. Never hand-assemble.

python3 "$SDIR/build-dispatch.py" --json '{
  "agent": "<agent>", "skill": "<skill; omit when agent-only>",
  "complexity": "<trivial|simple|medium|complex>",
  "model": "<fable|sonnet|opus|codex|gpt-5.6-sol|gpt-5.6-terra|gpt-5.6-luna>",
  "model_policy": "<low-risk|standard|high-risk|max-power>",
  "model_effort": "<low|medium|high|xhigh|max>",
  "provider": "<anthropic|openai|other>",
  "manual_model_override": false,
  "health": {"confidence": 0.72, "n": 6, "failure": 0, "action": "keep", "alts": ["k1","k2"]},
  "stack": ["s1","s2"],
  "task_spec": {"intent": "...", "constraints": "...", "acceptance": "...",
                "files": "...", "operator_context": "..."},
  "flags": {"worktree": false, "local_only": false, "thinking_override": null},
  "token_remaining": 480000
}'

agent/skill/complexity: Phase 2 (null→-). model: required Medium+ (- trivial/simple). Use model_policy for automatic selection — resolves via the harness-native provider lane. model_effort identifies the benchmark point; advisory for Claude lanes (Agent tool has no per-call effort). provider: harness detection (anthropic|openai|other, default anthropic). A manual model change must set both manual_model_override=true and model_effort; never inherit the policy effort silently. health: 1.5 (- if none). stack: Phase 3. task_spec: mandatory Medium+; creation+"match ADR". thinking_override: slow=security/arch/5+files; fast=lookups.

[do-route] = SOLE signal for routing-decision-recorder. Sub-agents excluded.

Fallback: [do-route] agent={a} skill={s|-} complexity={c} health=- model={m|-}, Task Spec inline, dispatch.

Model Selection (ADR model-selection-policy).

Harness-native routing. The SDIR probe (Phase 2 pre-route) identifies the harness: ~/.claude → provider anthropic, ~/.codex → provider openai, ~/.hermes/.factory/.reasonix → provider other. Default when absent: anthropic (Claude Code is primary). Each provider lane has its own automatic policy table; cross-provider dispatch is manual-only (explicit tool invocation, never a silent default).

Run deterministic work with scripts, not an LLM. Two decision axes: (1) DeepSWE Pass@1 / cost / tokens / steps — agentic task completion rate, the quantitative source. (2) Owner-observed felt quality — fable > sol (noticeable gap despite similar Pass@1), opus > gpt-5.5 (marginal). Benchmark ties or near-ties resolve in favor of felt quality. Cells: Pass@1 / cost / output tokens / steps; cost = avg USD per task, written as a plain number — slash-command templating substitutes dollar-digit positional parameters in this injected body, so a literal dollar sign before a digit corrupts on every argful invocation. Higher Pass@1 better, other three lower-is-better.

Start low, escalate on miss. Task-class tables are ceilings by risk class, not starting points. Default = lowest tier whose risk class matches; escalate one tier only when output misses the acceptance bar. High tiers cost 3-6x per Pass@1 point (see pts/$ column) — pre-paying for xhigh/max "to be safe" wastes the 200 USD/month plan budget. Fan-out rule: parallel readers use the lane's low-risk point; one synthesis agent may run one tier higher. User-facing output (docs, prose, reviews the owner reads, design) leans fable even at standard tier; bulk/mechanical/parse-heavy work is where the OpenAI lane's cheaper points earn their keep (under Codex harness or explicit cross-provider call).

Anthropic lane (automatic under Claude Code). Effort is advisory — recorded in marker as model@effort for telemetry; the Agent tool has no per-call effort parameter.

Variant	max	xhigh	high	medium	low
Fable-5	70 / 21.63 / 119k / 88	70 / 13.41 / 80k / 68	69 / 9.18 / 57k / 59	65 / 6.09 / 40k / 48	60 / 3.76 / 25k / 38
Opus-4.8	59 / 13.22 / 135k / 120	54 / 8.01 / 86k / 95	52 / 4.28 / 50k / 73	49 / 3.44 / 41k / 66	41 / 2.29 / 29k / 54
Sonnet-5	54 / 26.40 / 214k / 268	50 / 11.89 / 121k / 186	48 / 7.43 / 87k / 147	40 / 4.08 / 57k / 108	31 / 2.19 / 36k / 77

Task class	Selection	pts/$	Why
deterministic	no LLM	—	Run the script directly.
low-risk	fable / low	16.0	60 Pass@1 at 3.76, 25k tokens, 38 steps.
standard	fable / medium	10.7	65 Pass@1 at 6.09, 40k tokens, 48 steps.
high-risk	fable / high	7.5	69 Pass@1 at 9.18, 57k tokens, 59 steps.
max-power	fable / xhigh	5.2	70 Pass@1 at 13.41, 80k tokens, 68 steps; manual_model_override=true; state justification in task_spec intent.

Fable[max] dominated by [xhigh] (same Pass@1, higher cost) — manual-only. Opus dominated by fable at every tier (opus[max] 59/13.22 vs fable[low] 60/3.76). Sonnet-5 dominated by opus. All opus/sonnet points → manual_model_override=true, kept for context-window, latency, and fan-out breadth constraints the benchmark does not measure. Haiku is retired.

OpenAI lane (automatic under Codex CLI).

Variant	max	xhigh	high	medium	low
GPT-5.6 Sol	73 / 8.39 / 60k / 61	71 / 4.70 / 41k / 44	69 / 3.47 / 28k / 37	61 / 1.86 / 18k / 31	45 / 1.07 / 11k / 23
GPT-5.6 Terra	70 / 4.95 / 72k / 76	60 / 2.13 / 40k / 43	54 / 1.13 / 22k / 34	35 / 0.58 / 12k / 25	24 / 0.43 / 8.6k / 21
GPT-5.6 Luna	67 / 3.03 / 73k / 102	57 / 1.54 / 45k / 71	44 / 0.78 / 26k / 49	11 / 0.22 / 8.2k / 24	2 / 0.07 / 3.1k / 12
GPT-5.5 legacy	n/a	67 / 7.23 / 46k / 82	64 / 5.10 / 31k / 62	54 / 2.75 / 20k / 46	27 / 1.20 / 9.4k / 28

Task class	Selection	pts/$	Why
deterministic	no LLM	—	Run the script directly.
low-risk	gpt-5.6-terra / high	47.8	54 Pass@1 at 1.13, 22k tokens, 34 steps.
standard	gpt-5.6-sol / high	19.9	69 Pass@1 at 3.47, 28k tokens, 37 steps.
high-risk	gpt-5.6-sol / xhigh	15.1	71 Pass@1 at 4.70, 41k tokens, 44 steps.
max-power	gpt-5.6-sol / max	8.7	73 Pass@1 at 8.39, 60k tokens, 61 steps; manual_model_override=true; state justification in task_spec intent.

All GPT-5.5 choices are manual-only. Off-policy GPT-5.6 points (Sol medium/low, Terra max/xhigh/medium/low, all Luna) are manual-only — some are cost trade-offs, not dominated; use with manual_model_override=true for a stated constraint.

Other harnesses (provider=other): model_policy is unavailable — choose the highest non-dominated Pass@1 point among models the harness exposes, applying the same start-low-escalate-on-miss discipline. Set model explicitly.

Cross-provider escalation — manual only, never automatic. Escalating anthropic → sol is a cost/limits lever or independent-second-opinion lever, not a quality upgrade (fable wins on felt quality at comparable Pass@1). Under Claude Code, codex-wrapper dispatches (codex skill, pr-workflow codex second-opinion review) remain valid as EXPLICIT tools — deliberate cross-provider calls, not defaults. Escalation targets: anthropic max-power miss → sol/xhigh or sol/max (second opinion, cheaper per point); openai max-power miss → fable/xhigh (quality upgrade on felt quality axis). Manual-pick ordering among legacy/manual points: opus-4.8 above gpt-5.5 where they otherwise tie.

Coordinator model. The main-thread coordinator routes and evaluates but never executes; its cost is input-dominated (largest context, short outputs), and DeepSWE Pass@1 measures execution it never does. Picks: anthropic harness → sonnet; openai harness → gpt-5.6-terra/high. Safe because deterministic scripts (pre-route, manifest, build-dispatch, health weights) absorb routing complexity and the learning loop bounds misroute cost. Escalate the coordinator (sonnet → opus) only on observed misroutes or repeated lazy-completion acceptance, never preemptively. Session model is set via harness config (/model), not per-turn.

Medium+ MUST set a model or policy. Codex prompts stay read-only and public unless a task requires otherwise.

Complex (3+ sources):

Verbs	Mode
list/count/extract/inventory/search/check/find/grep	Scripts when deterministic; otherwise harness-native low-risk readers → harness-native high-risk synth
review/audit/assess/analyze/debug/investigate/evaluate	Single harness-native high-risk agent

Simple/Medium: direct. Feature-branch; mods commit. isolation:"worktree"→flags.worktree. Non-org: 3 reviews→fix→PR. Org: confirm git.

Step 3: Multi-part — deps sequential; independent parallel (max 10).

Step 4: Auto-Pipeline Fallback (no match, Simple+) — auto-pipeline. None → closest+objective-loop. Never empty skill.

Lazy-completion check. "Done" on enumerable → compare scope; short → reject, re-dispatch (references/lazy-completion-detector.md). Re-dispatch → route failure.

Gate: Agent invoked, results delivered.

---

Learning Capture (automatic)

Hooks capture all. On observed route failure or learning question → load ${CLAUDE_SKILL_DIR}/references/learning-capture.md (hooks table, outcome fidelity, route-failure protocol).

---

Error Handling

On any routing error → load ${CLAUDE_SKILL_DIR}/references/error-handling.md.

References

• ${CLAUDE_SKILL_DIR}/references/progressive-depth.md
• agents/INDEX.json, skills/INDEX.json
• skills/workflow/SKILL.md, skills/workflow/references/pipeline-index.json
• scripts/routing-manifest.py