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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.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Use to evaluate the current state of a diamond. Checks theory gates, confidence levels, and recommends next action.
Use when building anything USER-FACING (or with persuasion/retention/cancellation/consent/pricing flows, or that touches vulnerable people) to surface design-level harm the security/privacy/compliance gates miss: dark/deceptive patterns and foreseeable misuse. Assumes the product works as designed and asks who it could harm and whether it is Happier-negative. NUDGE, not a block.
Lint canvas files for staleness, missing fields, inconsistent evidence types, and orphaned references. Run periodically or before major transitions.
Accessibility audit against WCAG 2.1 AA. Checks semantic HTML, ARIA, keyboard navigation, color contrast, screen reader compatibility.
Design the smallest viable test to validate or invalidate a critical assumption. Based on Torres's assumption testing framework, organized by Gilad's AFTER model (Assessment → Fact-Finding → Tests → Experiments → Release Results).
Use before any research activity or significant decision. Reviews cognitive biases relevant to the current stage.
| name | framework-health |
| description | Evaluate Mycelium's own process effectiveness. Measures cycle velocity, discard trends, confidence calibration, gate effectiveness, regression rate. Run quarterly or every 20 cycles. |
| metadata | {"instruction_budget":"50","framework_dependency":"mycelium","framework_dependency_note":"This skill is designed to run within the Mycelium framework (https://github.com/haabe/mycelium). Standalone use will skip the canvas state, theory gates, and harness behavior the skill assumes. Install: /plugin install mycelium@haabe-mycelium."} |
Mycelium evaluates its own process. This is triple-loop learning — the framework assessing whether it is getting better at producing good outcomes.
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:
.claude/memory/corrections.md (total, and ×graduated-to-mechanism in the last 90 days)..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").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."
For each dimension, compute the metric and compare against trend (if prior assessments exist):
Cycle Velocity:
Discard Rate:
Confidence Calibration:
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.product-leaf cycles with predicted confidence/ICE and actual outcome:
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).
Gate Effectiveness:
${CLAUDE_PLUGIN_ROOT}/harness/theory-tensions.md Tension 7): for any hard-block gate, check it is scaffolding (surfaces its why, an escape hatch exists, leaves the user more capable), not coercion (compliance for its own sake, no surfaced reason, no escape). A high-block gate that fails this audit is a Theory-X drift to remediate, not just a strict gate.Regression Rate:
Re-run any eval scenario tagged regression AND router-discipline from .claude/evals/scenarios/integration/. These are deferred design-time decisions that need periodic re-verification (the AGENTS.md router design is the canonical case — see agents-md-router-discipline.yml).
For each scenario:
/mycelium:eval-runner against the scenario filebaseline_reference fieldIf a scenario fails its success_criteria for the first time, log to corrections.md as a new generalizable correction with the scenario name as evidence. Do not auto-remediate — surface the regression for human review.
If cycle count ≥ minimum_n for any threshold in .claude/canvas/thresholds.yml:
${CLAUDE_PLUGIN_ROOT}/engine/adaptive-thresholds.mdFor each dimension, verify the counter-metric is not degrading:
Read .claude/memory/cluster-instances.md. For each cluster:
spec-status clusters with linked spec docs (e.g., ${CLAUDE_PLUGIN_ROOT}/engine/consistency-check-spec.md): check whether the spec's promotion-bar conditions have been met. Concretely: count detection rules drafted vs. required, FP-rate measurements available vs. needed.This step closes the recursion the cluster log was created to address: graduation criteria become mechanically auditable rather than promises stored in commit messages.
The README's "How Mycelium got smarter" section shows 5 case headers; the full list lives in docs/receipts/cases/. Stale README highlights are a Goodhart signal: if the receipts surface freezes, the framework's "we get smarter with each cycle" claim degrades to "we got smarter once".
For each case currently on the README:
docs/receipts/cases/ newer than the rotation candidate that better demonstrate the framework's recent behavior?docs/receipts/cases/ in >60 days, flag as a possible-low-friction signal — either the framework genuinely caused no recent friction (rare), or the dogfood loop has weakened (usually).Per docs/contributing/style.md#highlights-rotation. Cases stay in docs/receipts/cases/ even when rotated off README; only the README mention rotates.
Run a lightweight version of /mycelium:canvas-health step 9b on docs/:
Last updated >60 days)Surface in the dashboard. Full details delegate to /mycelium:canvas-health.
The chat-UX nudges in ${CLAUDE_PLUGIN_ROOT}/harness/design-principles.md ("the chat is a UI") shape live output, which has no stored corpus to audit retroactively. What is auditable is the static surface that pre-shapes live output: the ## Output/## Output Format blocks in ${CLAUDE_PLUGIN_ROOT}/skills/*/SKILL.md. Scan each for two axiom violations:
ON HOLD, Gated by:, a leading verdict line). Flag blocker-bearing templates whose blocker does not visually pop.This is the buildable form of the self-audit; the live-output version is unenforceable (no corpus). Surface counts + offending skills in the dashboard. Do not auto-edit skills — flag for maintainer review (a template's flat option-list may be deliberate). Graduation path: if the same skill is flagged across two assessments, promote to a mechanical tests/bash check (then it inherits G-V12 / Check 37).
Temporal independence required. "Flagged across two assessments" means two assessments separated by independent observation windows — a quarterly run, a cycle-count-trigger run, or an explicit re-audit after deliberate skill-template edits. Same-day re-runs with no intervening skill edit are mechanically the same flag, not two observations; record the prior run's flag-set as the baseline and check the next independent run against it. Without this rule, any agent running /mycelium:framework-health twice in a row would graduate the entire flagged set on the second run. The rule applies symmetrically to 4b (cluster graduation-readiness) and 4d (docs health) — re-flagging in the same session does not count as independent confirmation.
The 2026-06-12 five-dimension gap analysis (code/hooks, doc-vs-mechanism consistency, flows/lifecycle, documentation, packaging/CI/schemas — decision-log "Five-dimension deep-dive gap analysis") is a repeatable audit shape. Its expensive failure mode was NOT finding gaps — it was stale premises: 4 of 6 backlog-derived items were already shipped or already graduated, found only by ground-truthing against the current tree.
On each quarterly run:
${CLAUDE_PLUGIN_ROOT}/engine/consistency-check-spec.md § Promise registry. For each open row, check whether the implementing artifact has landed (close the row, citing the version) or the trigger has fired (escalate to the cluster catalog). New "skill X will do Y" claims found in docs without an implementing artifact or Gated by: marker become new rows.validate_canvas.py and record the schema-less WARN count in the dashboard. The count should be monotonically non-increasing; an increase means a new canvas file was taught without a schema or waiver (preemptive-registry trigger fired — see spec)./theory-fidelity is the semantic audit (is each theory faithfully operationalized vs distorted/over-claimed?) — it needs source-grounding and an LLM, so it runs on cadence, not every push. The check_theory_fidelity.py CI guard covers only the structural subset (references resolve, gates grounded, no name-only theory). This step is the trigger for the semantic half.
/theory-fidelity when the theory surface moved OR quarterly. Check whether docs/theories.md, engine/theory-gates.md, or any skill's Source:/## Theory Citations line changed since the last /theory-fidelity decision-log entry (git-log the paths against that date). If yes, or if >90 days since the last run, surface in the dashboard: "Theory surface changed since last fidelity audit — run /theory-fidelity."check_theory_fidelity.py inline and report. A guard failure is a structural-drift finding (phantom skill/gate ref, name-only theory) for the dashboard; it does NOT substitute for the semantic audit.## Framework Health Dashboard
Assessment date: [date]
Cycles analyzed: [N]
Period: [date range]
### Dimensions
| Dimension | Current | Trend | Status | Counter-Metric |
|-----------|---------|-------|--------|----------------|
| Cycle velocity | [X days avg] | [improving/stable/degrading] | [healthy/warning/critical] | Outcome quality: [OK/degrading] |
| Discard rate | [avg phase X] | [earlier/stable/later] | [healthy/warning/critical] | False positive rate: [OK/rising] |
| Confidence calibration | [factor X.XX, or "empty (0 product-leaf)"] | [improving/stable/diverging/—] | [healthy/warning/critical/empty-by-design] | Decision speed: [OK/slowing] |
| Gate effectiveness | [see detail] | — | [healthy/warning/critical] | Flow speed: [OK/slowing] |
| Regression rate | [X%] | [decreasing/stable/increasing] | [healthy/warning/critical] | Innovation rate: [OK/declining] |
### Threshold Calibration
| Threshold | Default | Calibrated | Based On | Change |
|-----------|---------|-----------|----------|--------|
| ICE advance | 100 | [value or "insufficient data"] | N cycles | [+/-] |
| Confidence factor | 1.0 | [value or "insufficient data"] | N cycles | [+/-] |
| Bakeoff delta | 20% | [value or "insufficient data"] | N bakeoffs | [+/-] |
### Pattern Signals Active
[List any active pattern detector signals from ${CLAUDE_PLUGIN_ROOT}/engine/pattern-detector.md]
### Recommendations
[Specific actions based on findings — not generic advice]