/experiment — Metric-Driven Optimization Loop
SkillMonitoring & opsAutomated optimization loop with scalar fitness function. Proposes changes in isolated worktrees, measures with a metric command, keeps improvements, discards failures. Supports convergence detection and diminishing returns.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the /experiment — Metric-Driven Optimization Loop skill
What this skill tells your AI
The instructions your AI receives, as published by sethgammon/citadel in skills/experiment/SKILL.md and read by ahel’s review.
Inputs
The user provides three things:
- scope: Files to modify (glob pattern, e.g., "src/api/**/*.ts")
- metric: Shell command that outputs a single number (e.g.,
npm run build 2>&1 | tail -1 | grep -oP '\d+') - budget: Iteration cap (default: 5) or time cap (e.g., "10 minutes")
If any input is missing, ask for it. The metric MUST output a single number to stdout.
Protocol
Step 1: BASELINE
- Stash any uncommitted changes (restore on exit)
- Run the metric command. Record the baseline value.
- Determine direction: does lower = better (bundle size, error count) or higher = better (FPS, test count)? Ask the user if ambiguous.
- Log:
Baseline: {value} ({metric command})
Step 2: ITERATE
For each iteration (up to budget):
- Create isolation: Spawn a sub-agent in a worktree (
isolation: "worktree") - Propose change: The agent modifies files within scope to improve the metric. Provide context: baseline value, metric direction, scope, what previous iterations tried.
- Measure: Run the metric command in the worktree (via
node scripts/run-with-timeout.js 300) - Gate: Run typecheck (also via timeout wrapper). If it fails, discard immediately.
- Evaluate:
- Improved? → KEEP. Merge the worktree branch. New baseline = new value.
- Same or worse? → DISCARD. Delete the worktree.
- Log iteration:
Iteration {N}: {value} ({delta from baseline}) → {KEEP|DISCARD} Change: {one-line description of what was tried}
Step 3: CONVERGENCE CHECK
After each iteration, check:
- Local optimum: Last 3 iterations all discarded → stop ("no more improvements found")
- Diminishing returns: Last kept improvement was < 0.5% → stop ("diminishing returns")
- Budget exhausted: Iteration count or time exceeded → stop
Step 4: REPORT
Write results to .planning/research/experiment-{slug}.md:
# Experiment: {Description}
> Metric: `{command}`
> Direction: {lower|higher} is better
> Scope: {glob pattern}
> Budget: {N iterations}
> Date: {ISO date}
## Results
| Iteration | Value | Delta | Verdict | Change |
|-----------|-------|-------|---------|--------|
| baseline | {N} | — | — | — |
| 1 | {N} | {+/-} | KEEP | {desc} |
| 2 | {N} | {+/-} | DISCARD | {desc} |
## Outcome
- **Start**: {baseline}
- **End**: {final value}
- **Improvement**: {percentage}
- **Iterations**: {kept}/{total}
- **Stop reason**: {convergence|diminishing|budget}
## Kept Changes
{List of changes that were kept, with commit hashes}
Also log to .planning/telemetry/agent-runs.jsonl:
{"event":"experiment-complete","slug":"{slug}","baseline":0,"final":0,"improvement":"0%","kept":0,"total":0,"timestamp":"ISO"}
Common Metrics
| Goal | Metric Command |
|---|---|
| Reduce bundle size | npm run build 2>&1 | grep -oP 'Total size: \K\d+' |
| Reduce type errors | npx tsc --noEmit 2>&1 | grep -c 'error TS' |
| Increase test pass rate | npm test 2>&1 | grep -oP '\d+ passing' |
| Reduce file count | find src -name '*.ts' | wc -l |
| Reduce line count | wc -l src/**/*.ts | tail -1 | awk '{print $1}' |
When to Use
- When you want to optimize a measurable metric (bundle size, error count, test coverage, FPS)
- When you have a clear hypothesis but aren't sure which of several approaches wins
- When manual A/B testing would be too slow or error-prone
- NOT when the goal is subjective ("make it feel better") — the metric must be a number
Safety Rules
- NEVER modify files outside scope
- ALWAYS use worktree isolation for changes
- ALWAYS run typecheck before keeping a change
- Restore stashed changes on exit (even on error)
- If the metric command fails, treat as DISCARD (not crash)
Contextual Gates
Disclosure: "Running experiment loop on [target] with fitness: [function]. Each iteration commits. Budget: [N iterations]."
Reversibility: amber — modifies source files across iterations; each iteration is committed; undo with git revert on kept commits.
Trust gates:
- Familiar (5+ sessions): iterates and commits autonomously; novices should use /improve with manual review between steps.
Quality Gates
- Baseline was measured before any iterations ran
- Every kept iteration improved the metric AND passed typecheck
- Every discarded iteration has a logged reason
- The stop reason is one of: convergence, diminishing returns, or budget exhausted
- The experiment report exists at
.planning/research/experiment-{slug}.mdwith all iteration rows filled
Fringe Cases
Metric command outputs nothing or non-numeric text: Treat as a metric failure. Ask the user to provide a command that outputs a single number to stdout before starting iterations.
No worktree support (e.g., shallow clone): Fall back to branch isolation. Create a branch, run changes there, measure, then delete or merge the branch. Never modify the working tree directly.
If .planning/research/ does not exist: Create it before writing the experiment report. If .planning/ itself doesn't exist, create the full path or output the report inline.
Budget exhausted with zero kept iterations: Report outcome as "no improvement found". This is a valid result — do not continue past the budget.
Exit Protocol
---HANDOFF---
- Experiment: {description}
- Result: {baseline} → {final} ({improvement}%)
- Kept: {N}/{total} iterations
- Stop reason: {reason}
- Report: .planning/research/experiment-{slug}.md
- Reversibility: amber — undo kept iterations with `git revert` on each kept commit
---
Signals
- GitHub stars
- 921
- Forks
- 82
- Last commit
- Sep 2026
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experiment-sethgammon- Source
- github.com/sethgammon/citadel