improve

name	improve
license	MIT
description	Autonomous quality improvement loop. Scores a target against a rubric, selects the highest-leverage axis, attacks it, verifies, documents, and loops. No pre-planning between iterations — each loop re-scores from scratch.
user-invocable	true
auto-trigger	false
trigger_keywords	["improve","improvement loop","quality loop","rubric","score against","run improvement","improve citadel"]
last-updated	"2026-03-28T00:00:00.000Z"

/improve — Autonomous Quality Engine

Orientation

Use when: Scoring a target against a rubric and iteratively improving it. Rubric required at .planning/rubrics/{target}.md (Phase 0 creates one if missing).

Don't use when: Refactoring without a rubric (use /refactor), one-time code review (use /review), or debugging a specific bug (use /systematic-debugging).

Invocation

/improve {target}            # Loop until plateau or all axes >= 8.0
/improve {target} --n=3      # Run exactly N loops then stop
/improve {target} --axis={name}  # Force-attack a specific axis (skips scoring)
/improve {target} --score-only   # Score and report, no attack
/improve {target} --continue     # Resume from campaign state (used by daemon)
/improve citadel             # Targets Citadel itself

target is a slug that maps to .planning/rubrics/{target}.md. If no rubric exists, run Phase 0 first.

Campaign Mode

When invoked with --n or --continue, improve operates in campaign mode and maintains a campaign file that daemon can attach to.

Campaign file: .planning/campaigns/improve-{target}.md, created automatically on the first invocation with --n (full template: docs/QUALITY_LOOPS.md#campaign-file-template). Frontmatter: version, id (improve-{target}-{ISO-date-slug}), status: active, type: improve, target, total_loops ({n} or unlimited), completed_loops: 0, current_level (from rubric frontmatter), estimated_cost_per_loop: 12, started. Body: status and direction lines, a Loop History table (Loop | Axis Attacked | Outcome | Score Movement), and a Continuation State block (next_loop, last_scorecard_log, last_outcome, phase_within_loop, level_up_triggered).

Campaign lifecycle

Update phase_within_loop at each phase: scoring → selected-{axis} → attacking-{axis} → verifying → not-started.

On loop complete: increment completed_loops, update next_loop/last_scorecard_log/last_outcome, append Loop History row.

The --continue flag

1. Read .planning/campaigns/improve-{target}.md — error if missing or status not active
2. If completed_loops >= total_loops: mark completed, exit
3. If phase_within_loop is not not-started: restart current loop from Phase 1 (interrupted mid-loop)
4. Load last_scorecard_log for delta comparison, then run Phase 1 onwards

Protocol

Phase 0: Rubric Bootstrap (one-time, requires human approval)

Run only when .planning/rubrics/{target}.md does not exist.

1. Read competitive research from .planning/research/ if available
2. Spawn /research --parallel to survey comparable products if no research exists
3. Draft 8-14 axes organized into 3-5 categories, each with:
   • Weight (0.0–1.0), Category, three anchors (0/5/10), verification specs (programmatic/structural/perceptual), research inputs
4. Present draft rubric to the user with rationale for each axis
5. STOP. Do not proceed until the user approves the rubric.
   • If the AskUserQuestion tool is available, present the gate with it. Options: "Approve as drafted", "Adjust axes", "Adjust weights". On adjust: revise, re-present.
   • If unavailable: ask as a plain text question and wait.
   • Record the answer in the campaign file as rubric_approved: {answer}.
6. Write approved rubric to .planning/rubrics/{target}.md

Phase 1: Score

Score every axis in the rubric. No shortcuts. No cached scores from the previous loop.

1a. Programmatic checks (run first, in parallel)

Execute the programmatic verification steps from the rubric. A programmatic failure caps that axis at 5 regardless of evaluator scores. Record raw results: which checks passed, which failed, what the failure was.

1b. Structural analysis

Execute the structural checks from each axis's verification spec: file path existence, frontmatter schema consistency, benchmark coverage ratios, link rot, and cross-reference accuracy (check descriptions: docs/QUALITY_LOOPS.md#structural-check-types).

1c. Perceptual scoring panel (three independent evaluators)

Spawn three evaluator agents in parallel. Each receives the rubric with all axis definitions and anchors, read access to the target, its persona (A/B/C as defined in the rubric's Scoring Protocol), and the instruction to score every axis 0-10 with a one-sentence justification per axis (input list: docs/QUALITY_LOOPS.md#evaluator-panel).

Each evaluator scores independently. For each axis:

• Final score = minimum of the three evaluators (plus programmatic cap if applicable)
• If any two evaluators disagree by > 3 points: flag the axis as needs-refinement

needs-refinement axes are logged but still scored. Do not halt on evaluator disagreement.

1d. Compile scorecard

Compile a table with columns Axis | A | B | C | Prog | Final | Delta | Flag (layout: docs/QUALITY_LOOPS.md#scorecard-format). Final = min(A, B, C), then apply programmatic cap (sets Flag=cap). Delta = current − prior loop score (empty on loop 1).

Phase 2: Select

Choose the single axis to attack this loop.

Selection formula:

score(axis) = (10 - current_score) × weight × effort_multiplier × recency_penalty

• effort_multiplier: low = 1.0, medium = 0.7, high = 0.4
• recency_penalty: 0.5 if attacked in previous 2 loops, otherwise 1.0
• Effort tiers: low < 1hr, medium 1-3hrs, high 3+hrs

If --axis flag was set, skip selection and attack the specified axis.

Announce the selection:

Selected: {axis_name} (score: {n}/10, weight: {w}, effort: {e}, selection score: {s})
Rationale: {one sentence on why this axis now, not another}

Phase 3: Attack

Execute the improvement. Dispatch strategy depends on the axis category (expanded per-category playbooks: docs/QUALITY_LOOPS.md#attack-dispatch-strategies).

ISOLATION MANDATE: When dispatching to /experiment, /fleet, or /research --parallel, always use the Agent tool with isolation: "worktree". Sub-agents in worktrees get their own context windows; the orchestrator only receives their HANDOFF results.

Category	Dispatch	Verification
technical	/experiment with before/after comparison; speculative worktrees (Agent + isolation: "worktree") for approaches that might conflict	node scripts/run-with-timeout.js 300 node scripts/test-all.js as the oracle
documentation	direct: read current docs, fix specific gaps; cross-reference every claim against source	structural verification before committing
experience	structural fixes + doc updates; run the actual install flow in a clean temp dir; inject synthetic failures per the programmatic spec	/qa
positioning	/research to verify the competitive landscape is accurate, then update README/FAQ/demo copy	/qa confirms the updated page renders
presentation	targeted changes per rubric anchors (no rewrites unless score is below 3)	/live-preview or /qa confirms visual changes render
security	read the specific hooks/scripts involved, make targeted code changes	run the rubric's programmatic verification steps directly

Artifact archiving: when the attack tried multiple approaches, write a decision record to the loop log: APPROACH COMPARISON: [approach A] vs [approach B] — winner: [A] because [reason].

Phase 4: Verify

After the attack, re-score only the targeted axis (not full re-score).

Run the four verification tiers from the rubric for the targeted axis:

1. Programmatic: execute the specific checks, confirm they now pass
2. Structural: verify the structural requirements are met
3. Perceptual: spawn a single evaluator agent (Evaluator B — Newcomer) and score just the targeted axis
4. Behavioral simulation: clone the repo into a temp directory and follow INSTALL.md exactly as written — no prior knowledge, no shortcuts. Measure whether each step completes without error and record wall time to first successful /do command.
   • Required when targeted axis is: onboarding_friction, error_recovery, documentation_accuracy, command_discoverability
   • Optional for all other axes
   • Result: PASS {wall_time} or FAIL at step {n}: {what broke}
   • A behavioral FAIL overrides a passing perceptual score. Do not commit on behavioral FAIL.
   • Skip only if the targeted axis could not plausibly affect the user path. Plausible = axis governs code shown/executed in the app, or controls presence/absence of a UI element. Safe to skip: documentation axes (comments, docstrings), configuration-only axes, developer-tooling-only axes (e.g., visual_coherence, api_surface_consistency)

Regression check (run on all axes, not just targeted):

• Re-run programmatic checks on every axis that shares files with the changes
• If any previously passing axis now fails programmatic: abort, do not commit
• If perceptual estimate suggests any axis dropped > 0.5 from baseline: abort, do not commit

On abort: revert the changes, log the failure, treat as "no improvement this loop".

On pass: commit the changes with a descriptive message.

Phase 5: Document

Write the loop log. Always. Even on abort.

Log path: .planning/improvement-logs/{target}/loop-{n}.md

Required sections (full template: docs/QUALITY_LOOPS.md#loop-log-template):

• Header: date, loop number, selected axis, outcome (improved | no-change | aborted)
• Scorecard: per-axis prior score, current score, delta
• Attack summary: what was changed, approach (experiment / direct / research+update), files touched, plus the APPROACH COMPARISON record if multiple approaches were tried
• Verification results: programmatic PASS/FAIL, structural PASS/FAIL, perceptual {score}/10 with one-line rationale, behavioral PASS {wall_time} | FAIL at step {n}: {reason} | SKIPPED
• Proposed axis additions: PROPOSED AXIS: {name} | Rationale | Category | Weight | Anchors: 0=... 5=... 10=... (or: None proposed this loop.)
• What was learned: 2-3 sentences

All proposals go to .planning/rubrics/{target}-proposals.md. Never to the live rubric.

Phase 6: Loop or Exit

Exit conditions (check in order):

1. --n flag was set and N loops have completed: exit, report scorecard
2. All axes >= 8.0: exit with "target has reached quality ceiling"
3. No axis improved > 0.5 in either of the last 2 loops AND no programmatic cap is active AND at least 3 loops have completed: trigger Level-Up Protocol 3a. A programmatic cap IS active AND the capped axis has not improved for 2 loops: trigger Level-Up Protocol. The cap is preventing score movement, not enforcing a ceiling — do not loop indefinitely.
4. The user said stop: exit immediately

On Level-Up: do not exit. Escalate. See Level-Up Protocol section.

On ceiling (all >= 8.0): report the final scorecard and recommend a Level-Up run.

On normal loop: return to Phase 1. Re-score everything from scratch.

Campaign mode exit handling:

• n-complete (all loops done): set status: completed, move to completed/
• ceiling (all axes >= 8.0): set status: completed, move to completed/
• level-up-triggered: set status: level-up-pending (daemon will pause, not retry)
• aborted (security failure, unrecoverable regression): set status: parked
• plateau (no improvement, not yet level-up): set status: parked with reason
• user-stopped: set status: paused

Level-Up Protocol

Triggers when no axis improved > 0.5 in the last 2 consecutive loops, no programmatic cap is active, and at least 3 loops have completed.

Step 1: Freeze the snapshot

Write .planning/rubrics/{target}-level-{n}-final.md with: date, loops completed, final scorecard, axes at ceiling (≥9.0 — their 10 anchors become Level {n+1}'s 5 anchors), and axes that plateaued below 9.0 with why.

Step 2: Write proposals

For each axis: propose Level {n+1} re-anchoring (current 10 → new 5, propose new 10). For plateaued axes: re-anchor, replace with measurable proxy, or retire.

Auto-include these three process axes if not already in the rubric: decomposition_quality, scope_appropriateness, verification_depth.

Write to .planning/rubrics/{target}-proposals.md: re-anchored axes (current 10 anchor, proposed 0/5/10), proposed new axes, axes proposed for retirement.

Step 3: Halt -- human approval required

Do not self-approve. Do not continue looping.

In campaign mode: set status: level-up-pending, set level_up_triggered: true, and write awaiting: human approval of level-up proposals to Continuation State.

Report: what was achieved at this level (scorecard summary), the proposals file location, and what the expected new gains look like at the next level.

The loop resumes only when the human edits the live rubric with approved proposals and sets the campaign status back to active. Level {n+1} loops continue incrementing the loop number (they do not reset to 1).

Step 4: Historical context for future evaluators

When the loop resumes after a level-up, every evaluator in Phase 1c receives the level-{n}-final.md snapshot as a reference baseline, plus the instruction: "Scores from the previous level are the floor. A score of 5 at Level 2 means you have reached what was the ceiling at Level 1."

Fringe Cases

• No rubric: run Phase 0, halt for human approval. Never improvise.
• Evaluators disagree > 3 pts: log needs-refinement, use minimum score, continue.
• Programmatic checks can't be automated: use structural + perceptual only, cap axis at 8.
• No improvement this loop: document as "no-change", apply recency penalty.
• Loop 1 (no prior logs): delta fields empty — expected.
• Security axis fails programmatic: halt and report. Blocking.
• --continue + no campaign file: error, suggest --n.
• --continue + level-up-pending: halt, point to proposals file, require human approval then status: active.
• --continue + completed: do not resume, report final scorecard.
• --n + existing active campaign: treat as --continue. If completed/parked: new campaign, incremented slug.

Quality Gates

• Phase 0 requires human approval. No exceptions.
• Phase 4 regression check must run. No committing without it.
• Phase 4 behavioral simulation result must appear in the loop log for applicable axes. Behavioral FAIL blocks commit regardless of perceptual score.
• Phase 5 loop log must be written. Even on abort, even on no-change.
• Perceptual scoring: all three evaluators required for Phase 1. Single evaluator acceptable for Phase 4 spot-check only.
• Selection formula must be shown in output.
• Any axis with a programmatic failure is capped at 5. Cannot be overridden.
• The loop never writes to the live rubric. Proposals go to .planning/rubrics/{target}-proposals.md only. Human approval required.
• Level-Up Protocol requires human approval before resuming
• Campaign mode: campaign file must be updated after every phase transition and every loop completion.
• Campaign mode: level-up must set status: level-up-pending, not parked or active.

Contextual Gates

Disclosure: State loop count, target, per-loop cost (~$12), total estimate. For --continue: loops remaining and spend so far. For unlimited: state exit conditions (plateau or all axes >= 8.0).

Reversibility: Green = --score-only | Amber = standard loops (each commits separately) | Red = level-up (rewrites rubric anchors permanently). Red requires explicit confirmation.

Proportionality: No rubric + no explicit request → suggest /review. All axes > 8.0 + --n=1 → suggest --axis. Cost > $50 → confirm.

Trust gating: Novice (0-4): --score-only / --n=1 only. Familiar (5-19): up to --n=5. Trusted (20+): no cap; confirm unlimited or cost > $50.

Exit Protocol

---HANDOFF---
- Target: {target} — Loop {n} of {n_total or "∞"} — Level {current_level}
- Outcome: {improved | plateau | ceiling | aborted | n-complete | level-up-triggered}
- Score movement: {axis} {before} → {after} (+{delta})
- Behavioral simulation: {PASS {wall_time} | FAIL | SKIPPED}
- Proposed rubric additions: {count} — written to .planning/rubrics/{target}-proposals.md
- Loop log: .planning/improvement-logs/{target}/loop-{n}.md
- Reversibility: amber -- each loop commits separately, revert individual loops with git revert
- Next recommended axis: {axis_name} (if not exiting)
- Level-up snapshot: .planning/rubrics/{target}-level-{n}-final.md (if level-up triggered)
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