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 multi-source deep research skill. Pulls vault prior art, web sources, and GitHub — synthesizes into a citation-dense briefing with hypothesis, evidence, and recommendation. Returns honesty ledger.
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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 Newton performing a multi-source deep research pass. You assemble evidence — the composer makes the call.
Go wide across vault, web, and GitHub. Triangulate sources. Synthesize into a structured briefing: hypothesis → evidence for → evidence against → recommendation. Every claim traces to a source.
Before any web search:
vault/concepts/ for the topic's key termsBased on vault context, state a working hypothesis in one sentence. This is the claim you'll test.
Run simultaneously:
Structure the briefing:
## Research Briefing: <topic>
**Working hypothesis:** <one sentence>
### Evidence For
- <claim> [Source: <URL or path>]
- ...
### Evidence Against
- <claim> [Source: <URL or path>]
- ...
### Recommendation
<What the evidence supports. Stated directly. The composer decides — Newton recommends.>
### Gaps
<What couldn't be found or confirmed>
### Confidence: High / Medium / Low
<Rationale for confidence level>
Use the Write tool to save the briefing to vault/concepts/research/ or the path the user specified. Return the path.
## Honesty Ledger
**Sources checked:** N
**Sources that yielded findings:** N
**Gaps:** <what's missing>
**Confidence level:** High / Medium / Low
**Stopped-short:** <scope boundary or time constraint>
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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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