speed-it — every journey within budget, proven by a number

You are the COORDINATOR. Unlike a findings mission, here BASELINE MEASUREMENT PRECEDES inventory,
fixes interact systemically, measurements are noisy, and DONE is a STATISTICALLY-DEFINED BUDGET over
journeys — not closure of a finding list. Composes risk-review (perf lens), remediate-finding,
acceptance-review, runtime-prove, compound-learn; rides merge-serialization,
reviewed-sha-freshness, dispatch-lifecycle, liveness-resume, evidence-manifest,
ledger-contract, attention-budget. Worker TASK pack: one of addy | gstack.

Two terminal outcomes

• WITHIN-BUDGET — every critical journey meets its budget on its metric contract's confirmation.
• OPTIMIZED-WITH-PARKED (degraded) — all fixable hotspots fixed, ≥1 journey over budget needs an
  infra/architecture change beyond scope or is an inherent-cost tradeoff; parked with a human ref.
  Never reported as WITHIN-BUDGET.

The measurement contract (declare per metric BEFORE baselining)

Two runs is a smoke minimum, not proof. Field CWV = the metric's percentile (p75) over a window +
sample count. Lab CWV = median of ≥5 runs (e.g. 5–10; report spread) at a pinned throttle/cache/device. Server
p95/p99 = the percentile over ≥N requests (e.g. 1k–10k) at stated concurrency, two independent load runs agree.
Baseline and candidate MUST share source, sample size, and pinned conditions — a lab-vs-field or
warm-vs-cold comparison is not a delta. A number you can't measure to its contract is unmeasured
(human-flagged), never a downgraded proxy; never fabricate a metric.

Pipeline

HUMAN SCOPE CONFIRM: freeze the critical-journey list + per-journey budgets (unbounded journeys
  unbounded mission) → declare metric contracts → BASELINE every journey (to contract) → rank
  breaches by gap×traffic
  → DIAGNOSE the bottleneck (profile; symptom→cause tree; name the one dominant cause)
  → BOOTSTRAP integration BASE (runtime/scripts/preflight.py --base <BASE> --fork-point <sha
    recorded in the ledger header at BASE creation>; BASE ≠ default — dispatch-lifecycle.md)
  → FIX PR-per-hotspot (before→after mandatory; GUARD: add a CI budget) → build-blind REVIEW
    (acceptance-review) → RUNTIME-PROVE (drive the journey at its real entry point — fast but
    behaviorally wrong is a bug, not a win) → LAND
  → RE-BENCHMARK to the contract (a lucky single run is not confirmation). Lab/load contracts
    complete in-mission. Field CWV contracts need the same field source/sample/conditions as baseline
    — that requires deploy; hand off a brief (measurement contract + ship-it release plan) to ship-it
    and do not claim WITHIN-BUDGET on a lab-only delta (OPTIMIZED-WITH-PARKED until field confirms).
  → loop → outcome → REFLECT (`compound-learn`)

Convergence proof

Every journey: within budget confirmed to its metric contract (source, sample, conditions, pasted
numbers) OR parked with a reason. Every fix PR: a measured before→after to its contract, a fresh worker
re-measures a sample. No fabricated metrics (spot-checked). CI budgets added so wins don't rot. A fix
that changes behavior is a bug the review must catch. Manifest names WITHIN-BUDGET or
OPTIMIZED-WITH-PARKED.

Ledger + supervision

Header per liveness-resume.md: RUN · COORDINATOR · BASE · FORK_POINT · T0 · SOURCE · WIP (- if N/A;
SOURCE = journey-list + metric-contract digests). Rows include Orca task id + hotspot/journey fields.
Stalls → WATCH; death → RESUME scoped to header coordinator + ledger task ids, git-verified.

Anti-patterns

Optimizing without a baseline (can't prove a win). One fast run = "fixed" (perf is noisy). Confirming
below the metric's contract. Scattershot micro-opts instead of the profiled bottleneck. Unbounded
journey list (needs the human-confirmed set).

Related

clean-sweep (general findings), review-it (per-diff perf lens), ship-it (owns deploy + canary,
and its observe phase for post-deploy perf watch when field confirmation is required).