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/jeremydev87/codingbuddy/performance-optimizationnpx skills add JeremyDev87/codingbuddy --skill performance-optimizationgit clone --depth 1 https://github.com/JeremyDev87/codingbuddyWhat 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 | $0.00021 | $0.00717 |
| Opus 5 | $0.00010 | $0.00358 |
| Sonnet 5 | $0.00004 | $0.00143 |
| Haiku 4.5 | $0.00002 | $0.00072 |
Grade A, and why
performance-optimization 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance Optimization
Iron Law: NO OPTIMIZATION WITHOUT PROFILING FIRST
No exceptions - not for "obvious" bottlenecks, "quick wins", or "best practices".
Use for: Slow APIs, UI lag, high memory, slow builds, database queries, any "make it faster" request.
Five Phases
| Phase | Activity | Output |
|---|---|---|
| 1. Profile | Find hot paths with profiler | Bottlenecks with % of time |
| 2. Benchmark | 5 warm-up + 10 measured runs | Baseline with mean, std, p95 |
| 3. Prioritize | Apply Amdahl's Law | ROI-ranked list |
| 4. Optimize | ONE change, verify | Measured improvement |
| 5. Prevent | CI gates, monitoring | Regression detection |
Phase 1: Profile
- Clarify metrics ("It's slow" → p50? p95?)
- Select profiler:
- CPU: perf, py-spy, node --prof, Chrome DevTools
- Memory: heaptrack, memray, Chrome Memory
- I/O: strace, iostat, slow query log
- Distributed: OpenTelemetry, Jaeger, Datadog
- Serverless: AWS X-Ray, CloudWatch
- Mobile: Android Profiler, Xcode Instruments
- Profile cold AND warm cache
- Flame graphs: Wider bars = more time
- Time-box: Max 2 hours → Escalate if unclear
Phase 3: Prioritize (Amdahl's Law)
Formula: Speedup = 1 / ((1 - P) + P/S)
| Bottleneck % | Max Speedup | Action |
|---|---|---|
| < 5% | < 1.05x | Skip |
| 5-20% | 1.05-1.25x | Low priority |
| 20-50% | 1.25-2x | Medium |
| > 50% | > 2x | High priority |
Multiple similar %? Optimize easiest first. Re-profile after each.
Phase 4: Optimize
- Write benchmark test first
- ONE change at a time
- Compare statistically
- Rollback: Separate commit, feature flags
Phase 5: Prevent
Add CI gate: PERF_BUDGET_MS: 150 → fail build if exceeded
Red Flags - STOP
- "I know where the bottleneck is" → Profile first
- "Let's just add caching" → Measure first
- "One run is enough" → High variance
- "The fix is obvious" → Return to Phase 1
What ships with it
2 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.
- 2d ago First seen · 80 lines · 21 tokens per session scan A 041482bcded6
performance-optimization is a skill published in the GitHub repository JeremyDev87/codingbuddy (31 stars, last pushed 4mo ago), licensed MIT. It adds 21 tokens to every session and 717 once invoked, about $0.0001 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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