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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Spawn a reviewer subagent with 4-6 specific verification questions before declaring non-trivial code work done. Triggers when changes are >30 LOC, multi-file, hot-path, prompt-affecting, or genuinely uncertain. Bad asks produce bad answers — this skill forces structured verification questions, not "is this good?"
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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。
Run an explicit second-pair-of-eyes pass on non-trivial work before declaring done. Quality of review depends on quality of question.
If skipping, say so explicitly: "Skipping self-review — N-line trivial fix." Don't silently bypass.
State, in plain language:
- path/to/file_one.ts:L123-L145 — replaced regex with structured parser
- path/to/file_two.ts:L56 — added null guard for empty corpus
- path/to/config.json — bumped diversity_threshold 0.6 → 0.75
Bad asks (do not produce useful answers):
Good asks (concrete, falsifiable, named):
routeRequest?"recent_titles is empty (first run), does the sort comparator return stable output?"null rows or only new inserts?"Each question must:
"Edge cases I'm uncertain about:
- What happens when input X is empty?
- Does this break callers Y and Z that I didn't read?
- Any regression on the prior fix from PR #N?"
"Please rate findings as HIGH / MEDIUM / LOW with concrete line references."
Use the Agent tool with a code-reviewer subagent_type (or equivalent). Pass the full context block (steps 1-5) as the prompt. Set model to whatever is appropriate for the review depth.
The reviewer is a second pair of eyes, not an authority. Evaluate every suggestion. Blindly applying every note is as bad as ignoring them all.
After review completes, append to your delivery message:
**Self-review:** {APPROVED — severity X} / {N findings applied} / {N deferred — listed below}
- {finding} → {fix applied OR reason deferred}
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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.
中文简介正在准备中。当前显示原文说明。
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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