Experiment arms

The experiment arm method turns a pile of ideas into measured, landed improvements. Each hypothesis gets its own small, revertible PR (an arm) that exists only to be measured. Arms run in parallel against a shared control that does the same work, and each one is judged against a keep bar you wrote down before looking. Winners get composed into a stack and landed at whatever autonomy level the repo allows. Losers become closed [negative result] PRs plus a ledger row, so no one has to try them again. The number you report at the end comes from real traffic after merge, not from the estimate.

Start from a Loop Card (templates/loop-card.md). If you don't have one yet, run find-the-loop first.

When to use / not use

Use it when:

• the metric has run-to-run noise and you can run the real system on demand (CI, a perf harness, an eval, a benchmark);
• there are several candidate changes, or one popular tip nobody has measured on your setup;
• the result must survive review by someone who will ask "compared to what?".

Don't use it when:

• one deterministic test settles the question. Write the test.
• the expected effect is smaller than the noise floor and you can't afford the n needed to see it. Pick a different instrument or a bigger lever.
• the system can't be run on demand. Observe production instead and say the evidence is weaker.

Roles

Role	Owns
Coordinator (you, or one session)	Loop Card, hypothesis table, ledger, budget, cutoff, status
One worker per arm	One change, its arm PR, its measurement, a KEEP/DROP report with numbers
Reviewer on a different model	Critique of the hypothesis table and the final stack (see cross-model-review)

A coordinator that starts editing code stops coordinating. Keep it to orchestration.

Steps

1. Name the felt metric and the statistic. Use the number a person actually experiences: PR time-to-green, not one job's duration; long frames, not render count. Report p50 and p90. Note any guard metrics (cost, failure rate, test count) that must not regress.
2. Baseline and critical path. Pull 50–150 historical samples. Decompose them and name the slowest required lane, since only changes to the critical path move the felt metric. Write the result to baseline.md.
3. Measure noise first (A/A). Run the control against itself (two identical arms) for at least 3 rounds. The spread of those deltas is the noise floor. If the keep bar is inside it, raise n or change the instrument before you run a single treatment.
4. Write the hypothesis table. One row per arm, with these columns: concept, expected Δ, on critical path?, risk, equivalence proof, owner. One concept per arm and no drive-by edits. Read the ledger first, and don't re-run a recorded negative without a new reason.
5. Pre-register the keep bar in the Loop Card before any treatment runs. The default: the arm wins against control in every round, the median paired Δ beats the bar and is outside the noise floor, the 95% CI of the paired Δ excludes zero, and equivalence passes (same test identities, byte-identical outputs, same eval answer key).
6. Freeze a measurement base, a branch or SHA that stays fixed for the whole round. Branch every arm from it.
7. Build arms in parallel, in batches. Run as many at once as your spend tier's lane cap and the repo's budgets.max_parallel_arms allow, whichever is lower; queue the rest for the next batch. Each gets one worktree, one worker and one draft PR from a lever/arm-<name> branch, titled [measurement — do not merge] <arm> with a do-not-merge label. Push early so other work can stack on it. Don't serialize measurement while waiting for an earlier arm's result. Run everything now and decide the order afterwards.
8. Make the control cost-matched. The control must do the same work as the treatment. Put the same nonce change in every arm and the control so that caches bust equally and path filters can't skip lanes. Reject any arm whose heavy checks were skipped. An empty commit that goes green in 2 minutes is not a control.
9. Run paired, interleaved rounds. In each round, push every arm and the control together. Pilot with 3 pairs, decide with n ≥ 6 per arm (12 for small effects), and use n ≥ 10 for any headline number. Below 6 complete pairs the verdict is INCONCLUSIVE, whatever the tier. A failed or missing treatment run counts against the arm, not as a skipped pair. Run at least 2 rounds. Label small-n p90s as exploratory. For CI, use scripts/ci_arm_driver.py (see ci-adapter.md).
10. Watch the base. If main (or the harness) breaks, void every round that ran on it. Merge the fix into every arm's measurement commit, since that's for measurement only, and re-run.
11. Decide keep or kill against the bar from step 5, using scripts/arm_stats.py (--min-pairs, default 6; it checks reliability before speed). Write every DROP to the ledger (templates/ledger.md) with its numbers, close its PR, and retitle it [negative result] <arm>. Re-test popular tips like any other arm: a widely shared one can lose on your hardware.
12. Compose the winners. Order them by dependency, then by measured value. Run one combined confirmation round (about 5 pairs) of the full stack against control, because effects don't always add up. If interactions are unclear, report a range ("between A and B depending on whether X and Y stack").
13. Land at the configured level. Open the real PRs, validate each one on its exact head SHA, and hand the set to landing-plan. Arms stay at L1. Winners land at the level resolved by docs/autonomy.md.
14. Re-measure after merge on real traffic at +24 h and +72 h. Publish the estimate next to the realized result, and say which is which. Paired arms are the decision instrument; post-merge traffic is the reporting instrument.
15. Clean up. Close every remaining arm and validation PR, file follow-ups, and feed the realized-vs-estimated error back into the next Loop Card.

Report status only when something changes, in the form metric: x → y (Δz%), with p50/p90, arms running/kept/dropped, budget used, and evidence level (templates/status-digest.md).

Dials

Dial	Default	Options
Metric / statistic	felt metric, p50 + p90	any metric with an instrument the agent can run
Keep bar	wins every round; median paired Δ ≥ bar and outside A/A noise; 95% CI excludes 0; equivalence passes	absolute (e.g. ≥ 1 min on the critical path), relative (≥ X%), cost or reliability win with no regression
A/A rounds before treatments	3	0 (only if the noise floor is already known and recent)
Pairs per arm	pilot 3 → decide 6 → headline 10+	12+ for small effects
Rounds	2	3 for borderline arms
Max concurrent arms	min(tier lane cap, budgets.max_parallel_arms), batched	hard cap 10, so shared runners aren't starved for real users
API rate floor	pause below 3,000 remaining core calls	any; set a shared budget across all agents
Poll interval	≥ 4 min, query by head SHA only	longer for long jobs; never tight loops
Drivers per arm set	1	a duplicate driver's push cancels runs
Compute budget	the budget /lever hands down; billed minutes per arm, reported (unknown runner cost says unknown, not $0)	hard cap per round
Rerun policy	rerun failed jobs ≤ 2× as a person would; count reruns in the metric	0 for pure-speed metrics on a stable fleet
Broken-base policy	void the round; merge the fix into every arm	pause until the base is green
Arm PRs	draft + do-not-merge label, closed at hand-off	local-only arms (no PR) for harness-only metrics
Negative results	closed [negative result] PR + ledger row	ledger row only
Merge authority for winners	repo ceiling via docs/autonomy.md	L2 hand-off · L3 on a recorded grant · L4 gated self-merge or enqueue
Cutoff	none	wall-clock time after which no new arms start
Post-merge re-measure	+24 h and +72 h	off only if the metric has no production signal

Outputs

• baseline.md: historical p50/p90, critical path, noise floor.
• A hypothesis table and a ledger, with every arm, kept or dropped, and numbers.
• One draft do-not-merge PR per arm, plus closed [negative result] PRs for the drops.
• A stack of real PRs, each with a before/after table, cost delta and risk.
• Final report: estimated vs realized x → y (Δz%), p50/p90, n, cost delta.

This skill's own feedback loop

• Estimate error: realized post-merge Δ vs pre-merge paired Δ. If they keep disagreeing, the instrument is missing something, such as flake reruns or load.
• Hit rate: kept arms / total arms. A rate near 100% means the bar is too loose or you're only testing safe bets.
• Repeat negatives: arms re-run without a new reason. This should be 0.
• Budget incidents: rate-limit exhaustion and starved runners. This should be 0.

Failure modes it prevents

• Comparing against history instead of a paired control, or against a cheap control that skipped the real work.
• Declaring wins from n = 1, or from deltas inside the noise floor.
• Waiting for each arm in turn when they could all run now.
• Speed-ups that weaken the signal: fewer tests, skipped lanes, retries hiding failures.
• Agents exhausting the shared API quota or runner pool.
• Re-trying an idea the team already disproved.
• Reporting a projection as a result.

Why this exists

• In one CI speedup project across two repos, 79 do-not-merge experiment PRs produced 27 kept changes in about 1.5 days. Measured on real traffic after merge, p50 time-to-green fell 43% in one repo and 34% in the other.
• The first control was an empty commit. Path filters skipped the heavy lanes, so it went green in 2.25 minutes. Controls must do the same work as treatments.
• "Why do we have to wait until 4pm?" That one question turned serial measurement into parallel arms with ordering decided afterwards.
• The project began from a viral Windows Dev Drive tip. It lost in both paired runs on those runners and broke checkout credentials. Meanwhile "cache corruption" turned out to be page-file exhaustion (15.2% → 0% failures), so the speed work also found reliability bugs.
• Agents polling CI made about 45k API calls in 3 hours and took down unrelated tools. The rate floor, poll interval and arm cap are here because of that.