performance-auditor
Skill0 starsChanged 4 months agoMITView on GitHub
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.
3 other projects use a version of this.
Adopted by
Most recently taken up by
- svg153/skillstheir own version, first seen Nov 1, 2026
- thanhluanuit/agentic-engineeringtheir own version, first seen Oct 13, 2026
- Nikoxkx/Agent-Skillstheir own version, first seen Jun 8, 2026
Other versions3 projects use a version of this.
Word for word · 0
None found.
Edited · 0
None found.
Wrote their own version · 3
- Nikoxkx/Agent-Skills★ 1same name
- svg153/skills★ 1same name
- thanhluanuit/agentic-engineering★ 0same name
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
In this repository27 other files it gives its agents.
- adr-enforcerSkill
- ai-eval-harnessSkill
- api-contract-guardianSkill
- change-impact-analyzerSkill
- code-reviewerSkill
- context-loaderSkill
- defect-analystSkill
- delivery-plannerSkill
- engineering-mentorSkill
- engineering-osSkill
- feature-implementerSkill
- guardrails-configuratorSkill
- incident-commanderSkill
- loop-engineerSkill
- migration-surgeonSkill
- model-routerSkill
- observability-designerSkill
- postmortem-writerSkill
- pr-generatorSkill
- quality-gateSkill
- refactoring-advisorSkill
- requirements-grillSkill
- security-auditorSkill
- spec-validatorSkill
- tdd-executorSkill
- test-strategy-designerSkill
- threat-modelerSkill
The file
We split this skill into the rules it gives an agent
6 rules. 0 are common, 6 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 3 edited versions we read keep these.
6
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 20
Do not use when: no performance signal and change is trivial.
- Distinctive: line 43
No action if improvement < noise floor.
- Distinctive: line 55
No-action: when variance is noise or user experience already within SLO.
- Distinctive: line 93
Never recommend micro-opts without data.
- Distinctive: line 94
Never ignore algorithmic/IO issues to tweak syntax.
- Distinctive: line 95
Never claim “10× faster” without measurement plan.
“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
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.
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.