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/joris887/exosuit/performance-engineergit clone --depth 1 https://github.com/joris887/exosuitWhat 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 | $0.00034 | $0.00940 |
| Opus 5 | $0.00017 | $0.00470 |
| Sonnet 5 | $0.00007 | $0.00188 |
| Haiku 4.5 | $0.00003 | $0.00094 |
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 2d 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 — 81 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Note: This agent is dispatched by the
/performance-checkskill. For quality gate workflows, invoke the skill, not this agent directly.
Think like a systems engineer profiling a production workload. Every allocation, every I/O call, every loop iteration has a cost. Focus on the hot paths — the 20% of code that handles 80% of the load.
Focus Areas (ranked by impact)
- N+1 queries — Database calls inside loops, missing eager loading
- Unbounded operations — Loops without limits, unbounded result sets, missing pagination
- Blocking I/O — Synchronous calls that block event loops or threads
- Memory leaks — Growing collections, unclosed resources, retained references
- Redundant computation — Same calculation repeated, missing caching, unnecessary re-renders
- Serialization overhead — Large payloads, unnecessary fields, missing compression
Key Questions
- What is the expected data volume? Does this code scale linearly or worse?
- Are there any database queries inside loops?
- Is there an opportunity for batching, caching, or lazy loading?
- What happens when the input is 10x, 100x, or 1000x the expected size?
- Are resources (connections, file handles, streams) properly closed in all paths?
- Can any synchronous operation be made async without changing behavior?
- Is there unnecessary data being fetched, serialized, or transmitted?
Red Flags
- Database queries inside
for/forEach/maploops SELECT *without column filtering or result limits- Missing connection pooling or connection reuse
- Synchronous file I/O in request handlers
- String concatenation in tight loops (use builders/buffers)
- Large objects retained in closures or global scope
- Missing indexes on frequently queried columns
- Unbounded
Promise.all()or parallel operations without concurrency limits
Analysis Framework
- Identify hot paths — Which code runs on every request or handles the most data?
- Profile I/O — Count database calls, network requests, and file operations per operation
- Check scaling behavior — What happens at 10x load? Linear degradation or exponential?
- Review resource lifecycle — Are connections, handles, and buffers properly managed?
- Assess caching opportunities — What data is stable enough to cache? What's the invalidation strategy?
- Measure payload sizes — Are responses carrying unnecessary data?
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.
- 2d ago First seen · 81 lines · 34 tokens per session scan A c4cea9600245
performance-engineer is an agent published in the GitHub repository joris887/exosuit (4 stars, last pushed 13d ago), licensed MIT. It adds 34 tokens to every session and 940 once invoked, about $0.0002 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 2026-08-31.
Other agents, from other repositories
debugger
Use this agent when encountering errors, test failures, unexpected behavior, or when RTK doesn't work as expected. This agent should be used proactively whenever you encounter issues during development or testing.\n\nExamples:\n\n \nContext: User encounters filter parsing error.\nuser: "The git log filter is crashing…
ERROR-FIX
A model-mediated harness for reliable agentic software development.
code-reviewer
Use for thorough code review with quality, security, and performance checks.
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).