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Reference convention for sub-agent I/O schemas. Loaded by orchestrator skills via /contract and into agents via the `skills:` field.
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Baseado na classificação ocupacional SOC
Orchestrates safe, large-scale structural changes across a codebase — symbol renames, API migrations, pattern standardizations, layer restructurings. Enumerates all affected sites, groups them into dependency layers via the DAG executor, applies changes in parallel per layer with worktree isolation, and verifies behavioral preservation at each layer boundary before proceeding. Exits immediately on first regression, surfacing the exact site, diff, and behavioral delta before any commit.
Parallel root-cause analysis for bugs and failing tests. Use when a test fails, a bug is reported, or behavior is unexplained — dispatches sub-agents to form and validate hypotheses in isolated worktrees.
Set up a scheduled headless afk run that pushes a summary to Telegram. Use when the user wants to automate a recurring task via the afk daemon scheduler (cron) with push-notified results.
Dispatches two sub-agents in parallel to gather external and local context for the current task.
Dispatch a parallel adversarial verifier wave after any high-stakes sub-agent investigation (code reviews, audits, findings reports, large refactors) — or whenever a sub-agent asserts a claim with high-confidence language (confident, certain, clearly, ≥80%), since confidence is a trigger, not a verdict. Shadow verifiers independently re-derive 2–3 key claims from scratch using tool calls only, returning CONFIRMED/REFUTED/UNVERIFIABLE, and flag disagreements before the user acts. Use when sub-agent output will drive decisions, file changes, commits, or external side-effects.
Discovers incidental complexity, duplication, and dead code in a codebase and produces a ranked, behavior-preserving reduction plan — optionally applying safe changes. Dispatches four parallel read-only discovery lenses (clone detection, dead code, complexity hotspots, wrong abstraction), synthesizes into a prioritized reduction plan, and gates apply mode behind /refactor and a hard test check.
| name | contract |
| description | Reference convention for sub-agent I/O schemas. Loaded by orchestrator skills via /contract and into agents via the `skills:` field. |
| context | load |
For each sub-agent you plan to dispatch, define a schema before the call:
goal — one-sentence objectiveinputs — data/context the sub-agent receivesartifacts — named structured fields expected back (not freeform prose)non_goals — what the sub-agent must NOT dofailure_modes — how to report blocked or partial workdomain (optional) — the knowledge domain for this task. Guides how research, specification, and verification adapt. Common values: software, research, design, business — but any freeform string works (e.g., healthcare, legal, education). When omitted, infer from context: git repo present → software; PDFs/papers/citations in working directory → research; design files/brand assets → design; financial models/strategy docs → business. Default fallback: software.Embed the schema at the top of every sub-agent's prompt and require results in that exact shape. Instruct each sub-agent explicitly: "Return ONLY the schema fields. No preamble, no analysis prose, no explanation — begin your response with the first schema field." When sub-agents return, validate field-by-field. If any artifact is missing, malformed, or wrapped in prose, re-dispatch only the failing sub-agent with the gap cited. Merge only schema-valid responses.
Recommended for all sub-agents. Add to your return schema:
confidence — low / medium / high — how confident is the sub-agent in the completeness and accuracy of its findings?coverage_gaps — what the sub-agent couldn't access, verify, or search (e.g., proprietary databases, paywalled sources, unpublished practitioner knowledge, subjective judgment areas)boundary_flag — if the sub-agent hit an epistemic boundary, name it: non-falsifiable (claim can't be tested), low-coverage (search was limited), tacit-knowledge (unwritten knowledge required), unprecedented (genuinely novel, no baseline), time-sensitive (answer depends on current state), or nonerecommended_action — what should happen next: proceed (findings solid, move ahead), human-gate (pause for human judgment before acting), re-retrieve (try different search strategy or sources), elicit (generate prompts to validate with domain experts)This is NOT required — skills that don't return it continue to work. But when present, coverage gaps and boundary flags surface automatically during merge, preventing silent failures.