corrections-audit

Solid

Use to analyze correction trends, surface recurring patterns, and graduate repeat corrections to guardrails or anti-patterns.

AI & Automation 41 stars 3 forks Updated today MIT

Install

View on GitHub

Quality Score: 86/100

Stars 20%
54
Recency 20%
100
Frontmatter 20%
70
Documentation 15%
100
Issue Health 10%
80
License 10%
100
Description 5%
100

Skill Content

# Corrections Audit Skill Analyze corrections.md for trends, recurring patterns, and actionable insights. ## When to Use - Loop 2 (Incremental) cadence: after every 3+ corrections are logged - When the same correction category appears 3+ times - During `/mycelium:diamond-assess` if corrections gate has findings - Before starting a new diamond at the same scale as a previously corrected one ## Workflow 1. **Load corrections AND warnings AND clusters**: Read `.claude/memory/corrections.md`, `.claude/memory/warnings-log.md`, AND `.claude/memory/cluster-instances.md` (the cluster log graduated 2026-05-08 — canonical record of recurring-pattern instances and their graduation status; without it, "the cluster has graduated N times" has no auditable backing). - If corrections + warnings both empty AND no clusters logged: report "No corrections, warnings, or clusters to audit" and stop - Treat all three as inputs to the same pattern analysis. Corrections capture agent-introduced failures; warnings capture framework-state debt; cluster-instances capture cross-cluster pattern accumulation with explicit graduation criteria. Same recurring-pattern shape, different vantage points. 2. **Categorize by frequency**: - Group corrections by `Category` (bias, security, engineering, process, communication) - Group by `Scope` (discovery, delivery, orchestration, quality) - Count occurrences per group 3. **Detect recurring patterns**: - [ ] Same category appears 3+ times -> ...

Details

Author
haabe
Repository
haabe/mycelium
Created
3 months ago
Last Updated
today
Language
Python
License
MIT

Similar Skills

Semantically similar based on skill content — not just same category