canvas-health

Solid

Lint 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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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

# 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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