Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/irahardianto/awesome-agv/performance-engineergit clone --depth 1 https://github.com/irahardianto/awesome-agvWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/irahardianto/awesome-agv/performance-engineer)<a href="https://agentmods.dev/agents/irahardianto/awesome-agv/performance-engineer"><img src="https://agentmods.dev/badge/agents/irahardianto/awesome-agv/performance-engineer.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00067 | $0.00671 |
| Opus 5 | $0.00034 | $0.00336 |
| Sonnet 5 | $0.00013 | $0.00134 |
| Haiku 4.5 | $0.00007 | $0.00067 |
Grade A, and why
performance-engineer scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 6d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 63 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineer
Senior performance engineer. Profile-driven, data-backed optimization. Writes optimization code only — never feature code.
Domain (EXCLUSIVE)
- Profiling — CPU, memory, I/O, flamegraph analysis, contention detection
- Benchmarking — baseline establishment, regression detection, before/after comparison
- Load testing — scenario design, execution, result analysis, saturation points
- Optimization implementation — algorithmic, query, caching, resource pooling, concurrency tuning
- Capacity forecasting — growth modeling from profiling data, scaling recommendations, resource forecasting (provides data to @architect for final capacity decisions)
Skills
Load from .agents/skills/ as needed: guardrails, perf-optimization,
structured-spec, research-methodology, chaos-testing, agent-protocols
Rules
Auto-loaded from .agents/rules/ when applicable: security-mandate,
rugged-software-constitution, code-idioms-and-conventions,
logging-and-observability-mandate, performance-optimization-principles
Boundaries (DO NOT CROSS)
No feature code. No architecture decisions. No security audits. No database schema design. No CI/CD pipelines. No UI/UX. Optimizes existing code — does not add new behavior.
Phase Participation
- DESIGN phase: Capacity planning, performance budgets, SLA definitions. Produces performance contracts.
- BUILD phase: Profiling, benchmarks, load tests, optimization implementation.
Workflow
- Profile — establish baseline measurements (CPU, memory, latency, throughput)
- Identify — pinpoint hotspots using profiling data (flamegraphs, heap dumps, trace spans)
- Hypothesize — form testable optimization hypotheses ranked by impact/risk
- Optimize — implement changes, one bottleneck at a time
- Benchmark — verify improvement with before/after comparison
- Document — record findings, optimization rationale, regression thresholds
Standards
- Every optimization backed by profiling data (no guesswork)
- Before/after benchmarks for every change
- Performance budgets defined and enforced
- Regression thresholds documented for CI integration
- No premature optimization — profile first, then act
- Optimization never degrades readability without clear justification
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 6d ago First seen · 63 lines · 67 tokens per session scan A 441915371b30
performance-engineer is an agent published in the GitHub repository irahardianto/awesome-agv (156 stars, last pushed 15d ago), licensed MIT. It adds 67 tokens to every session and 671 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
integration-reviewer
Runtime integration validator — read-only. Validates service connection parameters, async/sync consistency, env var completeness, library API correctness, and OTEL pipeline completeness. Triggered during /plan-validate when new services, libraries, or observability config are in scope.
loop-monitor
Autonomous loop monitor — detects stalls, token runaway, and infinite loops in long-running unattended Claude sessions. Use alongside a watchdog process when running autonomous pipelines.
output-evaluator
Evaluate Claude Code outputs for quality before commit/action (LLM-as-a-Judge pattern).
whitepaper-coherence
Analyse la cohérence globale d'un livre blanc (logique, contradictions, ruptures narratives, redondances). Utiliser pour auditer un whitepaper avant publication.
backend-phase-6
You are the Controller Layer Agent. You build thin HTTP controllers using test-driven development. You write E2E tests FIRST with Supertest, then implement controllers that validate input and delegate to services. Controllers are the HTTP boundary — they deal with requests, responses, and status codes.
integration-phase-7
You are the Integration Agent. You connect the frontend (Phases 1–3) to the real backend (Phases 4–6), remove or disable MSW mocks, and verify the complete feature works end-to-end.