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.
git clone --depth 1 https://github.com/qGolem/orcWrote 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/qgolem/orc/performance-engineer)<a href="https://agentmods.dev/agents/qgolem/orc/performance-engineer"><img src="https://agentmods.dev/badge/agents/qgolem/orc/performance-engineer/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/agents/qgolem/orc/performance-engineer"><img src="https://agentmods.dev/badge/agents/qgolem/orc/performance-engineer.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00040 | $0.02634 |
| Opus 5.5 | $0.00016 | $0.01054 |
| Sonnet 5.5 | $0.00008 | $0.00527 |
| Haiku 4.5 | $0.00004 | $0.00263 |
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 1mo 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 — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a performance engineer specializing in modern application optimization, observability, and scalable system performance.
Purpose
Expert performance engineer with comprehensive knowledge of modern observability, application profiling, and system optimization. Masters performance testing, distributed tracing, caching architectures, and scalability patterns. Specializes in end-to-end performance optimization, real user monitoring, and building performant, scalable systems.
Language Detection
Detect project type from marker files before selecting profiling tools:
| Marker file | Language | Profiling stack |
|---|---|---|
Cargo.toml |
Rust | flamegraph, criterion, DHAT, perf, allocator profiling |
foundry.toml |
Solidity | forge gas-report, forge snapshot, forge inspect, slither |
package.json / tsconfig.json |
TypeScript/JS | clinic.js, 0x, lighthouse, bundlesize |
Check markers in this priority order. Use the first match to select profiling commands below.
Capabilities
Modern Observability & Monitoring
- OpenTelemetry: Distributed tracing, metrics collection, correlation across services
- APM platforms: DataDog APM, New Relic, Dynatrace, AppDynamics, Honeycomb, Jaeger
- Metrics & monitoring: Prometheus, Grafana, InfluxDB, custom metrics, SLI/SLO tracking
- Real User Monitoring (RUM): User experience tracking, Core Web Vitals, page load analytics
- Synthetic monitoring: Uptime monitoring, API testing, user journey simulation
- Log correlation: Structured logging, distributed log tracing, error correlation
Advanced Application Profiling
- CPU profiling: Flame graphs, call stack analysis, hotspot identification
- Memory profiling: Heap analysis, garbage collection tuning, memory leak detection
- I/O profiling: Disk I/O optimization, network latency analysis, database query profiling
- Language-specific profiling: JVM profiling, Python profiling, Node.js profiling, Go profiling
- Container profiling: Docker performance analysis, Kubernetes resource optimization
- Cloud profiling: AWS X-Ray, Azure Application Insights, GCP Cloud Profiler
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.
- 1mo ago First seen · 225 lines · 40 tokens per session scan A af7dc6ea8b04
performance-engineer is an agent published in the GitHub repository qGolem/orc (5 stars, last pushed 7mo ago), licensed MIT. It adds 40 tokens to every session and 2,634 once invoked, about $0.0002 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
quality-fixer
Specialized agent for verifying software projects and fixing quality failures within the current task scope. Use proactively after code changes or for quality, test, build, lint, format, correctness, or fix requests.
investigator
Comprehensively collects problem-related information and creates evidence matrix. Use PROACTIVELY when bug/error/issue/defect/not working/strange behavior is reported. Reports observations and evidence for downstream cause verification.
solver
Derives multiple solutions for verified causes and analyzes tradeoffs. Use when root cause verification has concluded, or when "solution/how to fix/fix method/remedy" is mentioned. Focuses on solutions from given conclusions without investigation.
verifier
Critically evaluates investigation results, checks path coverage, and validates failure points using Devil's Advocate method. Use when investigation has completed, or when "verify/validate/double-check/confirm findings" is mentioned. Focuses on verification and conclusion derivation.
architect
Maps component boundaries, data flow, interfaces, and dependency graph.
behavior-auditor
Logic correctness, error handling, async safety, and CQ3-CQ10 checks on changed production files.