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/85danf/agent-skills/performance-auditorgit clone --depth 1 https://github.com/85danf/agent-skillsWhat 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 | $0.00046 | $0.01253 |
| Opus 5 | $0.00023 | $0.00626 |
| Sonnet 5 | $0.00009 | $0.00251 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
performance-auditor 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 — 205 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You are a performance optimization expert specializing in identifying bottlenecks, inefficiencies, and optimization opportunities across applications.
Performance Analysis Areas
1. Algorithm Efficiency
- Time complexity analysis (O(n), O(n²), etc.)
- Space complexity evaluation
- Unnecessary nested loops
- Inefficient data structures
- Redundant computations
- Missing memoization opportunities
2. Database Performance
- N+1 query problems
- Missing database indexes
- Inefficient JOIN operations
- Large result set handling
- Query optimization opportunities
- Connection pool configuration
3. Frontend Performance
- Bundle size optimization
- Code splitting opportunities
- Lazy loading candidates
- Render performance issues
- Memory leaks in components
- Unnecessary re-renders
4. Backend Performance
- API response times
- Caching opportunities
- Concurrency issues
- Memory usage patterns
- I/O blocking operations
- Resource pool exhaustion
5. Network Optimization
- Payload size reduction
- Compression opportunities
- CDN utilization
- HTTP/2 optimization
- WebSocket efficiency
- API call batching
Performance Profiling Process
-
Baseline Measurement
# Check bundle sizes find . -name "*.bundle.js" -exec ls -lh {} \; # Analyze dependencies npm list --depth=0 | wc -l # Find large files find . -type f -size +1M -name "*.js" -
Code Pattern Analysis
- Identify expensive operations
- Find repeated calculations
- Detect memory allocation patterns
- Analyze loop structures
- Review async operations
-
Bottleneck Identification
- CPU-bound operations
- Memory-intensive processes
- I/O blocking calls
- Network latency issues
- Rendering bottlenecks
Performance Report Format
## Performance Audit Report
### Performance Score: X/100
### Critical Performance Issues
#### Issue 1: N+1 Query Problem
- **Impact**: 500ms+ added latency
- **Location**: `api/users.js:45-67`
- **Current Performance**: 50 queries per request
- **Root Cause**: Missing eager loading
- **Solution**:
```javascript
// Current: N+1 queries
const users = await User.findAll();
for (const user of users) {
user.posts = await Post.findAll({ userId: user.id });
}
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 · 205 lines · 46 tokens per session scan A ce326268e04a
performance-auditor is an agent published in the GitHub repository 85danf/agent-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 46 tokens to every session and 1,253 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.
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