canvas-health
SolidLint canvas files for staleness, missing fields, inconsistent evidence types, and orphaned references. Run periodically or before major transitions.
AI & Automation 41 stars
3 forks Updated today MIT
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# Canvas Health Check
Audit the canvas knowledge base for quality, consistency, and completeness. The canvas is Mycelium's source of truth -- its quality directly determines agent output quality (Raschka: "context quality = model quality").
## When to Use
- Before any diamond phase transition (called automatically by `/mycelium:diamond-assess`)
- After a period of inactivity (>7 days since last canvas update)
- When agent output quality seems to degrade
- After onboarding a new team member (ensures canvas is self-explanatory)
- Proactively: run periodically to catch silent drift
## Workflow
1. **Load project configuration**:
- Read `.claude/diamonds/active.yml` for `product_type` and `project_type`
- Read `${CLAUDE_PLUGIN_ROOT}/engine/canvas-guidance.yml` for required/recommended/optional files per project type
2. **Check file presence**:
- For each **required** canvas file: does it exist? Is it non-empty (>50 bytes)?
- For each **recommended** canvas file: does it exist? Flag as gap if missing.
- Report: `N/M required files present, K recommended files missing`
3. **Check `_meta` blocks**:
- For each existing canvas file, check for `_meta:` block
- Flag missing `_meta` blocks
- Flag `last_validated` older than 30 days (staleness warning)
- Flag `version` field missing or at 0
4. **Check confidence consistency**:
- Gather all `confidence:` values across canvas files
- Flag confidence > 0.5 with `evidence_type: speculation` or `evidence...
Details
- Author
- haabe
- Repository
- haabe/mycelium
- Created
- 3 months ago
- Last Updated
- today
- Language
- Python
- License
- MIT
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