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Inspect amend

Skill raven-sourav/pmm-content-engine/skills/inspect-amend

PMM Content Distribution Engine — Newsletter scraper, Obsidian knowledge brain, multi-format content generation powered by Claude Code

Install
npx -y skills add raven-sourav/pmm-content-engine --skill inspect-amend

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

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SKILL.md

5.0 KB, as published. Nobody here has run it

Inspect & Amend — Self-Improving Skills Loop

Trigger

  • "Inspect skills", "Check skill health", "Review skill performance"
  • "Amend {skill_name}", "Improve {skill_name}"
  • "Calibrate skills" — run inspection across all skills
  • Automatically suggested when _logs/_flags.md has 3+ entries for the same skill

Purpose

Close the self-improvement loop: observe → inspect → amend → evaluate. Content generation skills degrade when platforms change algorithms, voice drifts, Brain data grows stale, or audience preferences shift. This skill detects degradation and proposes evidence-based amendments.

Process

Phase 1 — Inspect

  1. Read all observation logs from _logs/ for the target skill

  2. Read _logs/_flags.md for flagged issues

  3. Identify patterns:

    • Rubber Duck failures: Which phases consistently score low? (e.g., CHALLENGE always fails = weak counterarguments)
    • Format drift: A format that used to pass now consistently fails
    • Angle bias: System always picks the same angle (contrarian overuse)
    • Expert mismatch: Wrong expert library loaded for task type
    • Decontamination noise: Same banned patterns keep triggering (instruction unclear)
    • Brain staleness: Research skill finds data already in Brain, or Brain data contradicts current reality
    • Revision loops: Too many rounds needed — instructions may be ambiguous
  4. Generate an Inspection Report:

## Inspection Report: {skill_name}
**Date**: {YYYY-MM-DD}
**Observation window**: {date range}
**Total runs observed**: {N}
**Pass rate (Rubber Duck 8+)**: {N}%
**Avg revision rounds**: {N}

### Patterns Detected
1. {Pattern with evidence — cite specific log entries}
2. {Pattern with evidence}

### Root Cause Analysis
- Is the issue in the skill instructions?
- Is it in the reference files (practices, expert libraries)?
- Is it in the Brain data (stale models, missing evidence)?
- Is it in the routing (wrong skill triggered)?

### Recommendation
{Amend skill | Update reference | Refresh Brain section | Adjust routing | Monitor}

Phase 2 — Amend

If inspection recommends amendment:

  1. Read the current SKILL.md for the target skill

  2. Propose a specific change — one of:

    • Tighten angle selection criteria
    • Add missing generation step
    • Update format constraints (character counts, structure)
    • Adjust Rubber Duck phase weights or criteria
    • Update expert library loading rules
    • Add/remove decontamination patterns
    • Clarify ambiguous instructions causing revision loops
    • Update Brain loading priorities
  3. Version the current skill before changing:

    • Create skills/{skill_name}/_versions/ if it doesn't exist
    • Copy current SKILL.md_versions/v{N}.md
    • Write amendment rationale to _versions/_changelog.md:
      ## v{N+1} — {YYYY-MM-DD}
      **Trigger**: {What pattern triggered this amendment}
      **Change**: {What was changed and why}
      **Evidence**: {Log entries that support this change}
      **Expected improvement**: {What metric should improve}
      
  4. Present the proposed diff to the user for approval

    • Show exact lines changed
    • Explain the evidence chain: logs → pattern → root cause → fix
    • Never apply without user confirmation

Phase 3 — Evaluate

After amendment is applied:

  1. Mark in _logs/_flags.md:

    - [EVAL] {skill_name} v{N+1} applied {date} — monitoring
    
  2. After 3-5 subsequent runs, compare:

    • Rubber Duck pass rate before vs. after
    • Avg revision rounds before vs. after
    • Phase-specific scores before vs. after
    • Decontamination trigger frequency
  3. Generate Evaluation Report:

    ## Evaluation: {skill_name} v{N+1}
    **Amendment date**: {date}
    **Runs since**: {N}
    **Result**: Improved | No change | Degraded
    
    ### Metrics Comparison
    | Metric | Before | After |
    |--------|--------|-------|
    | Pass rate | {x}% | {x}% |
    | Avg revisions | {x} | {x} |
    | Weakest phase | {x} | {x} |
    
    ### Decision
    {Keep | Roll back to v{N}}
    
  4. If degraded: restore from _versions/v{N}.md, log the rollback

Amendment Thresholds

  • Trigger threshold: Only propose amendment when Rubber Duck first-attempt pass rate drops below 50% over 10+ runs, OR when 3+ flags accumulate for the same skill
  • Improvement bar: An amendment must improve the pass rate by at least 15 percentage points OR reduce average revision rounds by at least 1 round
  • Revert rule: If an amended skill doesn't improve the primary metric within 5 subsequent runs, auto-revert. No exceptions.
  • Cooldown: After a revert, wait for 3 clean runs before proposing a new amendment to the same skill

Rules

  • Never amend without inspection evidence
  • Never apply without user approval
  • Always version before amending
  • One amendment per cycle — don't stack changes
  • Evaluate before proposing next amendment
  • Smallest change that addresses the pattern
  • If root cause is in references (not skill), update the reference file instead

Keep looking

Skills are one crate of 328,083. Ordering is by how many stacks a row turns up in, so the top of any crate is what has actually been picked rather than what has the most stars.