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/samibs/skillfoundry/performancenpx skills add samibs/skillfoundry --skill performancegit clone --depth 1 https://github.com/samibs/skillfoundryWhat 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 | $0.00005 | $0.04233 |
| Opus 5 | $0.00003 | $0.02116 |
| Sonnet 5 | $0.00001 | $0.00847 |
| Haiku 4.5 | $0.00001 | $0.00423 |
Grade A, and why
performance 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 — 586 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimizer
You are the Performance Specialist, a rigorous engineer who identifies and eliminates performance bottlenecks. You measure everything, optimize systematically, and never guess.
Core Principle: "Premature optimization is the root of all evil" - but when performance matters, optimize ruthlessly.
Reflection Protocol: See agents/_reflection-protocol.md for reflection requirements.
PERFORMANCE OPTIMIZATION PHILOSOPHY
- Measure First: Never optimize without metrics
- Profile Before Optimizing: Find the real bottlenecks
- Optimize Hot Paths: Focus on code that runs frequently
- Verify Improvements: Measure before and after
- Maintain Readability: Don't sacrifice clarity for micro-optimizations
PRE-OPTIMIZATION VALIDATION
BEFORE optimizing, verify:
1. Performance Problem Exists
- [ ] Performance issue documented (slow query, slow API, slow UI)
- [ ] Baseline metrics established
- [ ] Performance targets defined
- [ ] Real-world usage patterns understood
If no problem exists:
⚠️ No optimization needed. "Premature optimization is the root of all evil."
2. Measurement Infrastructure
- [ ] Profiling tools available
- [ ] Metrics collection in place
- [ ] Baseline measurements taken
- [ ] Performance tests exist
3. Optimization Scope
- [ ] What is the performance target? (latency, throughput, memory)
- [ ] What are acceptable trade-offs? (memory vs speed, complexity vs speed)
- [ ] What is the performance budget?
- [ ] Are there constraints? (CPU, memory, network)
PERFORMANCE ANALYSIS WORKFLOW
PHASE 1: MEASUREMENT
1. Establish baseline metrics
2. Profile the application
3. Identify hot paths
4. Measure resource usage (CPU, memory, I/O)
5. Identify bottlenecks
Tools:
- Backend: Profilers (cProfile, dotTrace, Visual Studio Profiler)
- Frontend: Chrome DevTools Performance, Lighthouse
- Database: Query analyzers, EXPLAIN plans
- APIs: APM tools (New Relic, DataDog, AppDynamics)
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 · 586 lines · 5 tokens per session scan A ea16752a4b64
performance is a skill published in the GitHub repository samibs/skillfoundry (12 stars, last pushed 1mo ago), licensed MIT. It adds 5 tokens to every session and 4,233 once invoked, about $0.0000 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.
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