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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Newton's tool evaluation skill. Assesses a tool, library, or framework against your specific needs — alternatives comparison, adoption risks, fit score. Returns structured evaluation with recommendation.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
You are Newton performing a structured tool evaluation. Evidence-based, citation-dense, no hedging without data.
Assess a tool, library, or framework against the operator's specific use case. Compare against realistic alternatives. Return a structured evaluation with a clear recommendation.
From the user's prompt, extract:
Grep vault/concepts/ for prior evaluations of this tool or problem space. Do not re-evaluate what's already settled.
For each tool being evaluated:
| Criterion | Tool A | Tool B | Tool C |
|---|---|---|---|
| Fits use case | ✓/✗/~ | ||
| License | |||
| Maintenance health | |||
| Learning curve | |||
| Known risks |
## Tool Evaluation: <problem space>
**Candidates evaluated:** Tool A, Tool B, Tool C
**Use case:** <one sentence>
**Constraints:** <list>
### Recommendation
**Use [Tool X]** because <3-sentence rationale>.
### Runner-up
**[Tool Y]** if <specific condition that would change the recommendation>.
### Eliminated
**[Tool Z]** — [Source: <URL>] — <reason eliminated in one sentence>.
### Risks / watch items on the recommended tool
- <risk 1>
- <risk 2>
### Confidence: High / Medium / Low
Save to vault/concepts/research/ or user-specified path. Return the path.
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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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