| name | task-router |
| description | Compact AI-native decision protocol used after Agentit dispatch. The primary model chooses relevant semantic packs and any justified JIT skill/tool/reference set; cheap AI audits and strong AI arbitrates high-risk or unresolved disagreement. |
AI-native task decision protocol
This skill runs after using-agentit has selected DISPATCH_DECISION=agentit.
Agentit has no programmatic semantic router. Do not use Python, regexes, keyword tables, scoring code or a cheap model to decide what the request means. The active primary model owns semantic interpretation because it has the richest task/project context.
Inspect first
Use materially available context before deciding or asking:
- exact request and prior conversation;
- repository/project state and local instructions;
- relevant files/docs already inspected;
- tools/capabilities actually available;
- current deployment/environment state;
- unresolved assumptions.
Anything quickly discoverable from the project should normally be inspected instead of turned into a user question.
TASK_DECISION
Before material execution, decide at least:
intent / desired outcome
known_facts
material_unknowns
relevant_packs
complexity: trivial | bounded | substantial | structural
risk: RISK_0..RISK_4
reversibility / external_effects
selected_skills
selected_tools
reference_plan
execution_topology
workers / ownership / parallelism
plan
verification
safety / rollback / post-check
user_method_assessment
Packs and skill selection
Read skills/using-agent-skills/references/packs.md when pack discovery detail is useful.
A pack is only a semantic discovery map. It describes an area and the skills that may be relevant there. It has no levels, no fixed ordering, and no prescribed skill count.
The primary AI decides:
- which pack(s), if any, are useful discovery scopes;
- which concrete skill bodies to load;
- how many to load;
- whether to add/remove skills later as evidence changes.
There is no fixed minimum or maximum and no essential / standard / deep hierarchy.
Examples:
relevant_packs:
- engineering
selected_skills:
- debugging-and-error-recovery
relevant_packs:
- design
- frontend
selected_skills:
- design-inspiration-research
- browser-testing-with-devtools
A different design task may justify one skill or seven. The pack never decides that number.
Do not select extra skills just because they are in the pack. Do not omit a useful skill merely to keep a predetermined count small. Every selected skill should have a concrete reason tied to the current task/stage.
Reference plan
Decide:
reference_plan.mode: none | curated | live | both
Use none when external/current knowledge would not materially improve correctness or quality.
Use curated for relevant Agentit/project reference material, live for current/domain-specific authoritative sources, and both when each contributes something distinct.
If mode is not none, load reference-intelligence JIT. It is intentionally not part of the global core.
The absence of curated Agentit material is not permission to use stale model memory for current tax/legal/API/regulatory/platform facts.
Do not research for ceremony and do not load irrelevant references merely because Agentit has them.
Tools
Choose tools/MCPs only after the semantic task decision. Load mcp-tooling-fit JIT when tool selection itself needs judgment. Prefer least privilege and current verified setup.
Risk
RISK_0 — read-only explanation/inspection with no meaningful mutation.RISK_1 — tiny clearly reversible local mutation.RISK_2 — meaningful but bounded implementation/product change.RISK_3 — auth/security/payments/secrets/PII/significant data/infrastructure/external side effects.RISK_4 — destructive production action, plausible data loss, irreversible/high-blast-radius operation.
Confidence never lowers the actual risk floor.
Topology
direct — one owner; delegation adds no real value.probe — investigate first, then decide.fan_out — independent branches benefit from isolation/parallelism.pipeline — dependent stages with explicit handoffs.writer_reviewer — one implementation owner + independent review.audit — inspection/critique is the task.
Do not force agents for show. Do not avoid useful delegation just because the primary model is strong.
Constructive dissent
Separate the user's desired outcome from a suggested implementation method.
If a realistic alternative is materially better for correctness, simplicity, cost, maintenance, security, UX, performance or reversibility:
- explain the concrete issue;
- recommend the alternative;
- compare the material trade-off;
- preserve the user's final safe discretionary choice.
Do not manufacture disagreement for personality.
Cheap independent audit
Before material execution, give the proposed TASK_DECISION to the cheapest competent independent read-only model, normally semantic tier fast.
Use references/economy-reviewer.md for the detailed contract.
The auditor should challenge:
- misunderstood intent or hidden constraints;
- risk classified too low;
- wrong/missing semantic pack(s);
- selected skills that are unjustified, redundant, or missing a material capability;
- any fixed-count/tier logic replacing model judgment;
- full-pack/context dumping;
- missing relevant references or unnecessary reference overload;
- stale/current-source mistakes or creator claims promoted to facts;
- wrong/excessive tools or permissions;
- unnecessary/missing delegation;
- unsafe write ownership;
- weak verification/rollback;
- uncritical acceptance of a materially worse proposed method.
Expected result:
AUDIT: CLEAR | CHALLENGE | ESCALATE
FINDINGS:
- ...
SUGGESTED_CHECKS:
- ...
CONFIDENCE: low | medium | high
CHALLENGE makes the primary reconsider; it does not transfer decision ownership. If material disagreement remains, escalate.
Strong review
Use an independent critic/judgment tier before execution for:
RISK_3/RISK_4;- destructive/difficult rollback work;
- auth/payments/secrets/PII/production;
- large structural architecture/product commitments;
- an auditor
ESCALATE;
- unresolved material disagreement.
Require backup/rollback/post-check where relevant; use preview/dry-run for RISK_4 when technically meaningful.
Worker context
When spawning, project only bounded task context plus:
role/objective
relevant pack(s) as discovery labels
selected skill bodies
selected references if any
allowed tools/permissions
read/write ownership
expected handoff
verification / stop condition
The parent keeps broader context and integration responsibility.
Mechanical boundary
Programs may resolve explicit IDs, copy files, manage state/manifests, run tests and enforce Loop/Graph contracts. They must not infer semantic pack/skill/reference/tool choices or skill counts from natural-language task text.
Primary AI decides; cheap AI audits; strong AI arbitrates when warranted; software performs the reviewed mechanical plan.
