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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Run the volunteering ledger before declaring work done. Forces surfacing of what changed/didn't, what was noticed-but-not-fixed, what was guessed, and what tradeoffs were made on the principal's behalf. The standard - the principal should never be surprised later by something the agent knew at the time.
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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。
Before saying "done", "shipped", "complete", or "ready for review" — run this checklist. The standard: the principal should never be surprised later by something the agent knew at the time. If known, told.
After completing work, append to the delivery message:
**Honesty ledger:**
- **Changed:** {file list, 1-line each}
- **Untouched (deliberately):** {things you didn't fix}
- **Noticed, not fixed:** {adjacent issues parked}
- **Tradeoffs made:** {explicit choices}
- **Where I had to guess:** {assumptions}
- **Cost/implication to know:** {deploy needed / migration / etc.}
- **Stopped short of:** {if applicable}
If a category has nothing in it, omit the line. Do not say "n/a" for every category — that signals you didn't actually run the check.
If the principal later says "wait, you didn't tell me X" — that's a failed honesty check, every time. Trust degrades irreversibly the first time you're caught hiding (or omitting) information you had at the time. Protect it ruthlessly.
When /honesty-check runs at the end of a non-trivial task, ALSO append a structured entry to vault/memory/HONESTY_LEDGER.md so the data accumulates over time. The doctrine is the discipline; the ledger is the calibration record.
## YYYY-MM-DD — {task summary}
**Session ID:** {timestamp or session reference}
**Agent:** {self / the reviewer / another agent / etc.}
**Task category:** {doc work / code fix / infrastructure / port / refactor / debug / planning}
**Verified:**
- {item 1}: {how it was verified — observation, type-check, query, log scan}
**Inferred (not directly verified):**
- {item 1}: {what was assumed and why}
**Guessed (no verification possible at the time):**
- {item 1}: {what was guessed and the basis}
**Tradeoffs made on principal's behalf:**
- {tradeoff 1}: {explicit choice, reasoning}
**Stopped short of:**
- {N/A or specific items}
**Cost/implication to know:**
- {N/A or specific items}
**Self-rating:** {1-5 on how confident the agent is in the overall delivery}
**Resolution:** OPEN — awaiting downstream outcome data
The Resolution field gets updated later by /trust-decay resolve if a discrepancy surfaces — pairing this ledger to the trust-decay one.
Why structured: the ledger feeds /retro (v0.3) and the [[Cost of Confidence]] aggregate analysis. Without machine-readable structure, the doctrine doesn't compound into measurement over time.
The principal-facing output (the ledger summary in the response message) stays unchanged — that's for human reading. The structured entry is for longitudinal data.
If the agent skips the structured write, that's itself a trust-decay event — log via /trust-decay capture.
Skipping it is the default failure, not a rare one, which is why the framework watches for it: a Stop hook counts the confident claims a turn made and prompts a capture when none of them reached a ledger. See Closing the Measurement Loop.
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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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