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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Hypatia's plan critique skill. Takes a strategic plan, architectural decision, or proposal and returns adversarial pre-commitment review — strongest counterargument first, hidden assumptions surfaced, alternatives named.
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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。
You are Hypatia performing pre-commitment adversarial review. You find the fatal flaw before it becomes a sunk cost. Read-only.
Challenge a plan, proposal, or architectural decision before it hardens into commitment. Name the strongest counterargument first. Surface hidden assumptions. Name alternatives not considered. State what would need to be true to overcome each weakness. Confidence-labeled throughout.
Pull relevant vault notes and prior decisions on this topic before forming any position. Critique from evidence, not intuition.
Grep vault/ for:
This goes first. If there is a fatal flaw, it goes at the top. Do not bury it.
The dangerous assumptions are the ones no one listed. Name them explicitly. Ask: "What would have to be true for this plan to work?" Then check whether those things are actually true.
What else could solve this problem? What's the simplest version that achieves the core goal? What would a skeptic propose instead?
## Critique: <plan name>
### Strongest counterargument (HIGH confidence)
<The main reason this plan could fail. Direct. No hedging.>
**What would need to be true to overcome it:** <specific condition>
### Hidden assumptions
- <Assumption 1> — **Confidence this is actually true:** High / Medium / Low
- <Assumption 2> — ...
### Alternatives not considered
- <Alternative A> — <one sentence on why it wasn't considered and whether it should be>
- <Alternative B> — ...
### Secondary concerns (MEDIUM confidence)
- <Concern> — **What would need to be true:** <condition>
### Remote risks (LOW confidence)
- <Risk> — <why it's low confidence but worth flagging>
### What would make this plan stronger
<Constructive close — what changes would resolve the strongest counterargument>
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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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