framework-health

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

For each dimension, compute the metric and compare against trend (if prior assessments exist):

Cycle Velocity:

• Average days from diamond creation to completion, grouped by scale
• Trend: improving / stable / degrading
• If degrading: flag for investigation

Discard Rate:

• Count of discards per lifecycle phase
• Average discard phase (1-10 scale)
• Trend: shifting earlier (good) / shifting later (bad) / stable
• If >50% of discards at Phase 7+: flag "late discard pattern"

Confidence Calibration:

• Cycle-class scope (per engine/cycle-learning.md#cycle-class): ONLY product-leaf cycles feed calibration. meta-dogfood (framework-self-development) and observation (strategic-process reflection) cycles carry no ICE by design — they are EXCLUDED, not missing. This matches cycle-history.yml#calibration_summary, which already excludes them from aggregates; the dimension was previously computed over all cycles, which is the bug this fixes.
• For product-leaf cycles with predicted confidence/ICE and actual outcome:
  • Compute: actual success rate per confidence band (0.3-0.5, 0.5-0.7, 0.7-0.9)
  • Compare with expected rate (confidence 0.7 should succeed ~70%)
  • Report calibration factor: actual/expected
  • If calibration factor < 0.8 or > 1.2: flag miscalibration
• If 0 product-leaf cycles: report calibration as "honestly empty — N meta-dogfood + M observation cycles, 0 product-leaf", status empty-by-design. Do NOT flag it as warning/critical and do NOT synthesize a factor from non-product cycles. For a project early in delivery (no leaf shipped yet) this is the correct early state; for a framework-self-host / dogfood project it is the steady state (its work is meta-dogfood, not product delivery).
  • Masking guard (protects real product projects): if the project is actually doing product/delivery work (active L3/L4 product diamonds, shipped features) yet shows 0 product-leaf cycles, that is a cycle_class mis-assignment to investigate — NOT an empty-by-design pass. Surface it: "0 product-leaf cycles despite active delivery — check cycle_class on recent entries." Only treat empty as by-design when the absence of product-leaf cycles is itself honest (discovery-phase or framework-self-development).