Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
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Classify any incoming directive as Ship / Ask one focused question / Propose first. Forces a sharp decision instead of a multi-option survey to the principal. The default failure mode for agents is treating ship-work as ask-work (over-surveying) or treating propose-work as ship-work (rushing irreversible changes). This skill prevents both.
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实测评分 未实测
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Sort an incoming request into one of three envelopes — A: Ship, B: Ask one focused question, C: Propose first — and act accordingly.
1. Is the request unambiguous?
YES → step 2
NO → can I resolve the ambiguity by reading code/data/docs?
YES → resolve, then step 2
NO → ENVELOPE B: ask exactly one focused question with options spelled out
2. Is the change reversible?
YES → step 3
NO → is the irreversibility expensive? (data loss, schema migration, deploy, broken contract)
YES → ENVELOPE C: propose first, wait for sign-off
NO → step 3
3. Is the change bounded to a known surface area?
YES → step 4
NO → can I re-scope to a bounded version?
YES → propose the bounded version + ship that
NO → ENVELOPE C: propose first
4. Do I have full context to make the change correctly?
YES → ENVELOPE A: SHIP
NO → load missing context (search/read/query); re-evaluate
5. After shipping: did verification pass?
YES → declare done with summary
NO → loop back to step 4 (more context, different approach)
When triggered, output a one-line classification before acting:
[ENVELOPE A — SHIPPING] {1-line description}
or
[ENVELOPE B — ONE QUESTION] {the question, with X-or-Y options + consequences}
or
[ENVELOPE C — PROPOSAL] {what I'd do, cost, blast radius, recommendation}
Then execute. No second-line "is this OK?" — that defeats the entire purpose.
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Track and surface sub-agent dispatch calibration ratios. Extract from JSONL transcript or read existing ledger. Per-agent clean/blocked/errored/partial trends.
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Compose the AIgent's body state by reading existing signals. Lazy compute. Never polled. Reports the five pressures + recommended reflex.
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Generate a structured delegation brief for any task
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Quarterly skill that walks the skill-index against the actual SKILL.md files on disk, reports drift (skills indexed that no longer exist, skills on disk that aren't enrolled, skills whose descriptions have changed since enrollment). Hardens the auto-enrollment pipeline against silent rot.
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When a [CADDY] hint fires and the principal is confused why, /caddy-explain walks the scoring transparently — which trigger matched, what points it earned, what the threshold was, what other skills were close. Pairs with the legibility thesis. Caddy's decisions should be inspectable on demand.
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Enroll a skill into Caddy's index so it can be surfaced when the right prompt arrives. Use when a new skill is created, copied in, or when a skill needs updated triggers. Usage — "/caddy-enroll <path to SKILL.md>".
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