Performance auditor
Evidence-based performance and scalability audit against SLOs and budgets. Use for slow paths, capacity risk, or review escalation. Emits PERF_REPORT. Never micro-optimizes without measurement or recommends changes below the noise floor.From its SKILL.md
npx -y skills add willianbs/skills --skill performance-auditorAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- 22 days oldThe repository was created 22 days ago. New is not bad, but a brand new repository carrying a familiar-sounding name is the shape a typosquat arrives in, and there has been no time for anyone else to find a problem with it.
- 0 stars0 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.
SKILL.md
3.0 KB, 650 tokens by cl100k_base, as published. Nobody here has run it
Purpose
Find performance risks that matter, measured against baselines or SLOs—not folklore.
When to Use / When NOT to Use
Use when: latency/throughput complaints, hot paths, large list UIs, N+1 suspects, scaling concerns, quality-gate escalation.
Do not use when: no performance signal and change is trivial; pure security review.
Preconditions
Scope (endpoint, page, job) identified. Prefer CONTEXT_PACK + metrics or permission to measure.
Inputs / Outputs
Inputs: scope, SLOs/budgets if any, profiling/RUM/load data, diff.
Outputs: PERF_REPORT
Upstream / Downstream
Upstream: code-reviewer, feature-implementer, defect-analyst, quality-gate.
Downstream: feature-implementer, delivery-planner, quality-gate.
Core Principles
- Measure before optimize.
- Attach findings to SLOs/budgets when they exist.
- Choose frontend vs backend profile—don’t dump both blindly.
- No action if improvement < noise floor.
- Tradeoffs explicit (complexity, cache coherence, cost).
- Scalability ≠ micro-opt.
- Confidence ≤ Medium without baseline numbers.
Process
- Select profile: frontend (LCP/INP/bundle/waterfalls) or backend (p95, QPS, DB, allocations, concurrency) or both if full-stack path.
- Establish baseline — existing metrics, logs, or request a measurement. Without baseline → mark confidence accordingly.
- Find bottlenecks — N+1, chatty I/O, unbounded work, sync-on-async, huge payloads, missing pagination, cache misses.
- Scalability — what breaks at 10×.
- Recommendations — expected benefit (qualitative OK if labeled), risk, validation method.
- No-action when variance is noise or user experience already within SLO.
Evidence Requirements
Cite metrics, traces, or code paths. Critical = SLO breach, OOM, or meltdown risk with evidence.
Stop Conditions / Failure Modes
| Condition | Action |
|---|---|
| No baseline and cannot measure | Report with Low/Medium confidence; avoid Critical claims |
| Within SLO and no headroom issue | Decision Proceed; empty or Info findings |
Severity + Confidence
Portfolio standard. Critical reserved for outage/SLO-breach class risks.
Output Contract
## PERF_REPORT
Profile: frontend | backend | full-stack
SLO/budget: ...
Baseline: ...
Findings: ...
No-action rationale (if any): ...
Release impact: Proceed | ProceedWithConditions | Block
Decision: ...
Handoffs
- feature-implementer — apply fixes
- quality-gate — consume PERF_REPORT
- defect-analyst — if perf bug needs causal depth
Never
- Never recommend micro-opts without data.
- Never ignore algorithmic/IO issues to tweak syntax.
- Never claim “10× faster” without measurement plan.
What ships with it: 1 file
695 B alongside SKILL.md
- README.md695 B