Corrections Audit

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 /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: Read .claude/memory/corrections.md

   • If empty or no corrections logged: report "No corrections to audit" and stop

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 -> candidate for guardrail graduation
   • Same scope appears 3+ times -> candidate for domain-level CLAUDE.md update
   • Same mistake repeats after prevention was documented -> prevention strategy failed, needs escalation

4. Check origin distribution (APEX alignment):

   • Count corrections by Origin (ai-generated, human-written, ai-assisted)
   • If ai-generated corrections dominate (>60%): flag for prompt/context improvement, BUT see detection_origin cross-check below before acting on this interpretation
   • If human-written corrections dominate (>60%): flag for process/training improvement
   • If ai-assisted is high: check if the AI contribution or the human contribution caused the issue

4b. Cross-check with detection_origin (when field is present — see memory/README.md):

• Count corrections by Detection_origin if present (user / agent_self / hook / evaluator / eval_runner / external_review)
• Critical disambiguation: if Origin is heavily ai-generated AND detection_origin is heavily user, the apparent AI-quality signal is actually a HARNESS-DETECTION GAP. The AI is generating failures and the user is the only entity catching them. The right intervention is more harness checks (hooks, evaluators), NOT more AI context.
• If detection_origin is dominantly user (>70%): flag for harness-detection gap. Suggest where new hooks or evaluators could catch the failure modes earlier.
• If detection_origin is well-distributed across mechanisms: harness coverage is healthy; trust the Origin signal at face value.
• Surfaced 2026-05-03 (mycelium-roadmap dogfood): without this cross-check, the audit's "100% ai-generated → improve prompt context" framing would have driven the wrong intervention. Real signal was "AI generates, user catches" — fixed by shipping the framework-guard hook (harness-detection layer), not by improving prompts.

5. Root-cause recurring corrections (5 Whys): For each correction that appears 3+ times, apply 5 Whys to find the systemic root:

   • Why did this happen? -> Why did that happen? -> ... -> [systemic root cause]
   • Stop when you reach something changeable: a guardrail, gate, process step, or prompt instruction
   • Anti-pattern: stopping at "human error" or "agent didn't follow instructions" — ask why the system allowed it Source: Toyoda/Ohno (5 Whys), adapted for agentic workflows.

6. Identify graduation candidates:

   • Correction logged 3+ times with same root cause -> propose new guardrail (draft G-XX entry)
   • Correction reveals a failure mode not in anti-patterns.md -> propose new anti-pattern entry
   • Correction reveals a successful mitigation -> propose new pattern in patterns.md

7. Consolidate memory files (automated hygiene):

   • Deduplication: Identify corrections that describe the same root cause in different words. Merge into a single entry, preserving all dates and evidence.
   • Contradiction detection: Flag corrections that contradict each other (e.g., "always use X" vs "never use X"). Present conflicts to the user for resolution.
   • Staleness removal: Corrections older than 6 months whose prevention has been verified effective (no recurrence) can be archived to memory/corrections-archive.md.
   • Size cap: If corrections.md exceeds 50 entries, consolidate the oldest resolved entries into a summary paragraph in the archive.
   • Apply the same consolidation to memory/patterns.md. Inspired by: greyhaven-ai/autocontext curator agent — periodic dedup, cap, and contradiction removal.

8. Update TL;DR section:

   • Regenerate the TL;DR in corrections.md with the top 5 most impactful corrections
   • Impact = frequency x severity (blocking vs. quality vs. cosmetic)

9. Recommend actions:

   • For each graduation candidate: specific guardrail text, tier, and constraint type
   • For failed preventions: what went wrong and what stronger mechanism to use
   • For origin imbalances: specific context improvements

Output Format

## Corrections Audit

### Summary
Total corrections: [N]
Period: [earliest date] to [latest date]

### Frequency Analysis
| Category | Count | Trend |
|----------|-------|-------|
| engineering | 3 | rising |
| bias | 1 | stable |

### Origin Distribution
| Origin | Count | % |
|--------|-------|---|
| ai-generated | 4 | 57% |
| human-written | 2 | 29% |
| ai-assisted | 1 | 14% |

### Recurring Patterns
- [Pattern description]: [N] occurrences -> [recommendation]

### Graduation Candidates
1. [Correction pattern] -> Proposed guardrail: G-XX "[text]" `[TIER]` `[type]`

### Failed Preventions
- [Correction] was logged again despite prevention "[strategy]" -> [escalation]

### TL;DR Update
[Updated summary for corrections.md TL;DR section]

Theory Citations

• Mycelium internal learning loop
• APEX framework (origin-aware quality tracking)
• Senge: systems thinking (recurring patterns signal structural issues)