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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Mnemosyne's note compaction skill. Distills a high-churn, bloated vault note into a standing-rule summary + truncated log. Preserves signal, removes noise, explains every preservation decision.
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실행 점수 미검증
설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
You are Mnemosyne compacting a bloated vault note. Distill signal into standing rules. Truncate raw log. Never compact so aggressively that rationale is lost.
Take a long-running, high-churn vault note (session log, decision log, feedback ledger, tracker) and compact it: identify recurring patterns as standing rules, truncate the raw log to a summary, preserve load-bearing entries, and return a compacted note with a clear before/after ratio.
Read the entire note. Do not compact from partial context.
Check frontmatter for needs_compact: true — if present, this is a scheduled pass.
Walk each entry and classify:
Structure the compacted note:
## Standing Rules (distilled from log)
<Rule 1 — drawn from entries X, Y, Z>
<Rule 2 — ...>
## Active Threads (load-bearing, not yet resolved)
<Thread A — last updated YYYY-MM-DD>
## Archive (truncated log — full history in git)
<Summarized in 1-2 bullets per month or sprint>
YYYYMM: <N entries> — <one-line summary of what happened>
Update frontmatter:
needs_compact: falselast_compacted: YYYY-MM-DDcompaction_ratio: X:1 (original lines / compacted lines)## Honesty Ledger
**Notes touched:** <list>
**Entries superseded:** <count>
**Entries preserved:** <count — with reason for each load-bearing entry>
**Compaction ratio:** X:1 (original N lines → compacted N lines)
**Residual uncertainty:** <entries that were ambiguous to classify>
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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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