Speed it

Skill ravidsrk/orca-fleet/skills/speed-it

Bring declared user journeys within pre-declared performance budgets, proven by measurement. Controlled baseline (measure to a metric contract, not once) → profile the real bottleneck → fix PR-per-hotspot with a mandatory before/after → re-benchmark to the metric's statistical contract → add CI regression guards, looping until every journey is within budget or parked. Use when "the app is slow", "perf sweep", "Core Web Vitals", "get under budget", or an unattended perf-hardening run. Not for a per-diff perf opinion (review-it) or post-deploy watch alone (observe via ship-it).From its SKILL.md

Install

npx -y skills add ravidsrk/orca-fleet --skill speed-it

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

One thing to look at

1 stars1 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.

What its file declares

Copied from the file, not written here

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.0 KB, as published. Nobody here has run it

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).

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