feedback-review

name	feedback-review
description	Aggregate feedback signals across all active loops. Reports health, trajectory, overdue checks, regression warnings, and Goodhart's Law violations.
metadata	{"instruction_budget":"55","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."}

Feedback Review

Single-place health check across all Mycelium feedback loops. Run periodically or when something feels off.

Preflight: Read target canvas file(s) before any Write/Edit

Hard rule. Before issuing Write or Edit against any .claude/canvas/*.yml, use the Read tool on that file in this session. Claude Code's Read-before-Write check requires the Read tool specifically — cat/head/grep via Bash do NOT satisfy it.

Edit vs Write — different cost profiles (verified 2026-05-14):

• Edit (exact-string replacement): Read with limit: 1 satisfies the check at ~50 tokens. State-tracking is per-file, not per-byte — subsequent Edit calls work anywhere in the file. Use this for partial updates against large canvas files (e.g., purpose.yml at 800+ lines).
• Write (full replacement): do a full Read first. Write obliterates the file; you should see what you're about to replace. The limit:1 shortcut is not appropriate here.

ID-bearing entries — scan the ID space before assigning (added 2026-05-15, v0.23.19): When adding a new component, opportunity, solution, or any other ID-bearing entry to a canvas file, run a Bash grep first to confirm the next ID in your prefix sequence is actually free:

grep "^  - id: <prefix>-" .claude/canvas/<file>.yml | sort -u

Replace <prefix> with the canvas's ID prefix (comp for landscape, opp for opportunities, sol for solutions, ht for human-tasks, etc.). Then pick the next free integer. validate_canvas.py has a duplicate-ID check (lines 230-239) that catches the failure on CI, but a duplicate can persist in the working tree for days if CI isn't run between edit and discovery — see roadmap-repo corrections.md 2026-05-15 "Duplicate canvas ID created in landscape.yml" for the worked example.

Original failure mode: anti-pattern #7 instance #5, 2026-05-09 — agent conflated Bash head with the Read tool, lost ~14k tokens to a Write-fail → remedial-full-Read → re-Write loop. The limit:1 discipline (graduated 2026-05-14, v0.23.18) prevents the second-order cost where the agent correctly follows the rule but full-Reads every time. The ID-scan discipline (graduated 2026-05-15, v0.23.19) prevents the related class where the agent reads enough of the file to satisfy the Edit check but not enough to see existing ID assignments — kin to anti-pattern #8 (Stale State Read).

If this skill writes to multiple canvas files, register each one first (limit:1 for Edit-only paths; full Read for Write paths) AND ID-scan any prefix you intend to assign.

See CLAUDE.md Canvas writes — Read before Write for the canonical rule.

When to Use

• Weekly as part of regular practice
• When metrics aren't moving despite active work
• When the team feels busy but unproductive
• After a failed launch or unexpected regression
• When /mycelium:diamond-assess shows stale diamonds

Workflow

1. Check Loop 1 (Immediate) Health

• How many reflexion iterations are averaging? (1 = healthy, 3 = struggling)
• Any corrections logged this session? Are they new patterns or repeats?
• Is the preflight gate catching issues, or are issues slipping through?

2. Check Loop 2 (Incremental) Health

• Are diamond phases progressing? Or stalled?
• Are ICE confidence scores increasing with each GIST step?
• Is the delivery journal being updated? (Empty = no incremental learning)
• Are retrospectives happening after delivery increments?

3. Check Loop 3 (Strategic) Health

Read canvas trend data and check cadence:

• BVSSH: Last assessed when? Any dimension declining? (Check bvssh-health.yml trend fields)
• North Star: Are input metrics moving? Flat for 2+ months = strategic concern.
• Delivery metrics: Any metric degrading? Check the product-type-appropriate canvas: dora-metrics.yml (software), content-metrics.yml (content), ai-tool-metrics.yml (ai_tool), service-metrics.yml (service).
• Wardley Map: Last refreshed when? Stale > 3 months = risk of strategic blind spot.
• Corrections themes: Are the same types of mistakes recurring? (Pattern = graduate to guardrail)

4. Check Loop 4 (Transformative) Health

• When was the last eval benchmark run?
• Are eval pass rates improving, stable, or declining?
• Are any skills consistently underutilized? (Check .claude/harness/decision-log.md for skill invocation patterns)
• Has the escape hatch been used? How often? (Frequent = process too heavy for context)

5. Regression Warning Check

From ${CLAUDE_PLUGIN_ROOT}/engine/feedback-loops.md, check active triggers:

• DORA declined 2+ times? -> Warn about L4/L3 regression
• Confidence stagnant 3+ steps? -> Warn about opportunity reframing
• Same correction 3+ times? -> Suggest guardrail graduation
• BVSSH Safer declining while Sooner improving? -> Flag the BVSSH anti-pattern

6. Goodhart's Law Check

For each active metric, verify its counter-metric:

• Deployment frequency up BUT change failure rate also up? -> False improvement
• Confidence score up BUT evidence type hasn't changed? -> Inflation
• Test coverage up BUT defect leakage unchanged? -> Meaningless tests
• Diamond velocity up BUT regression rate also up? -> Rushing through gates
• Evidence source count up BUT external evidence ratio declining? -> Internal echo chamber risk. Suggest /mycelium:handoff to plan external conversations.

Output Format

## Feedback Loop Health Report

### Loop 1 (Immediate): [Healthy / Warning / Struggling]
- Reflexion avg iterations: [N]
- New corrections this period: [N] ([N] repeats of existing patterns)
- Secret detection blocks: [N]

### Loop 2 (Incremental): [Healthy / Warning / Struggling]
- Diamonds progressed this period: [N]
- Confidence trajectory: [improving / flat / declining]
- Delivery journal entries: [N]
- Retrospectives completed: [N]

### Loop 3 (Strategic): [Healthy / Warning / Overdue]
- BVSSH last checked: [date] ([days ago])
  Trajectory: B[trend] V[trend] S[trend] S[trend] H[trend]
- North Star: [current] -> [target] ([trajectory])
- DORA: [classification] ([trajectory])
- Wardley map last refreshed: [date]

### Loop 4 (Transformative): [Active / Dormant]
- Last eval run: [date]
- Pass rate trend: [improving / stable / declining]
- Escape hatch uses: [N] in last quarter

### Regression Warnings
- [Any active triggers from ${CLAUDE_PLUGIN_ROOT}/engine/feedback-loops.md]

### Goodhart's Law Check
- [Any metric/counter-metric divergences]

### Recommended Actions
1. [Most urgent feedback loop action]
2. [Second priority]
3. [Third priority]

Canvas Output

Update .claude/canvas/bvssh-health.yml trend fields if BVSSH was assessed. Update .claude/canvas/dora-metrics.yml trend fields if DORA was assessed. Log review in .claude/memory/product-journal.md.

Theory Citations

• Kim: Three Ways of DevOps (Second Way: amplify feedback)
• Argyris: Single/double/triple-loop learning
• Meadows: Leverage points in systems
• Goodhart: When a measure becomes a target
• Forsgren: DORA metrics as feedback signals
• Smart: BVSSH as holistic health feedback