framework-health

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Evaluate Mycelium's own process effectiveness. Measures cycle velocity, discard trends, confidence calibration, gate effectiveness, regression rate. Run quarterly or every 20 cycles.

AI & Automation 41 stars 3 forks Updated today MIT

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Description 5%
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# Framework Health Check Mycelium evaluates its own process. This is triple-loop learning — the framework assessing whether it is getting better at producing good outcomes. ## When to Use - Quarterly review (scheduled) - After 20 completed leaf cycles (triggered by cycle-history.yml count) - When process friction is suspected - Before major framework changes (baseline measurement) ## Workflow ### 1. Load Cycle Data Read `.claude/canvas/cycle-history.yml`. **Framework-self-host detection** (per `engine/cycle-learning.md#framework-on-framework-exemption`): if the project root contains `plugins/mycelium/plugin.json` AND `CLAUDE.md` begins with `# Mycelium:`, this is the framework dogfooding itself. Skip the cycle-count gate and route to a corrections-graduation summary: - Count entries in `.claude/memory/corrections.md` (total, and ×graduated-to-mechanism in the last 90 days). - Read `.claude/memory/cluster-instances.md` and list clusters at-or-above their graduation criterion that are not yet graduated (this is the framework analogue of "actual outcome vs predicted ICE"). - Skip cycle-derived dimensions (velocity, discard rate, confidence calibration, regression rate) — they do not apply. Still run Steps 2b, 4b, 4c, 4d. Otherwise (product project, not framework-self-host): if fewer than 5 cycles recorded, report: "Insufficient cycle data for framework health assessment. [N] cycles recorded; minimum 5 needed. Continue recording outcomes." ### 2. Measure Five Dimensions ...

Details

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

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