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 skills/zhaono1/agent-playbook/performance-engineernpx skills add zhaono1/agent-playbook --skill performance-engineergit clone --depth 1 https://github.com/zhaono1/agent-playbookWrote 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/skills/zhaono1/agent-playbook/performance-engineer)<a href="https://agentmods.dev/skills/zhaono1/agent-playbook/performance-engineer"><img src="https://agentmods.dev/badge/skills/zhaono1/agent-playbook/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 | $0.00023 | $0.01361 |
| Opus 5 | $0.00012 | $0.00681 |
| Sonnet 5 | $0.00005 | $0.00272 |
| Haiku 4.5 | $0.00002 | $0.00136 |
Grade A, and why
performance-engineer scanned grade A with 1 finding 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 5d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -w "@curl-format.txt" -o /dev/null -s https://example.com/users How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Engineer
Specialist in analyzing and optimizing application performance, identifying bottlenecks, and implementing efficiency improvements.
When This Skill Activates
Activates when you:
- Report performance issues
- Need performance optimization
- Mention "slow" or "latency"
- Want to improve efficiency
Performance Analysis Process
Phase 1: Identify the Problem
-
Define metrics
- What's the baseline?
- What's the target?
- What's acceptable?
-
Measure current performance
# Response time curl -w "@curl-format.txt" -o /dev/null -s https://example.com/users # Database query time # Add timing logs to queries # Memory usage # Use profiler -
Profile the application
# Node.js node --prof app.js # Python python -m cProfile app.py # Go go test -cpuprofile=cpu.prof
Phase 2: Find the Bottleneck
Common bottleneck locations:
| Layer | Common Issues |
|---|---|
| Database | N+1 queries, missing indexes, large result sets |
| API | Over-fetching, no caching, serial requests |
| Application | Inefficient algorithms, excessive logging |
| Frontend | Large bundles, re-renders, no lazy loading |
| Network | Too many requests, large payloads, no compression |
Phase 3: Optimize
Database Optimization
N+1 Queries:
// Bad: N+1 queries
const users = await User.findAll();
for (const user of users) {
user.posts = await Post.findAll({ where: { userId: user.id } });
}
// Good: Eager loading
const users = await User.findAll({
include: [{ model: Post, as: 'posts' }]
});
Missing Indexes:
-- Add index on frequently queried columns
CREATE INDEX idx_user_email ON users(email);
CREATE INDEX idx_post_user_id ON posts(user_id);
API Optimization
Pagination:
// Always paginate large result sets
const users = await User.findAll({
limit: 100,
offset: page * 100
});
What ships with it
6 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 5d ago First seen · 251 lines · 23 tokens per session scan A de4520288e73
performance-engineer is a skill published in the GitHub repository zhaono1/agent-playbook (77 stars, last pushed 10d ago), licensed MIT. It adds 23 tokens to every session and 1,361 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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