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 commands/samibs/skillfoundry/performancegit clone --depth 1 https://github.com/samibs/skillfoundryWrote 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/commands/samibs/skillfoundry/performance)<a href="https://agentmods.dev/commands/samibs/skillfoundry/performance"><img src="https://agentmods.dev/badge/commands/samibs/skillfoundry/performance.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 | $0.00000 | $0.04218 |
| Opus 5 | $0.00000 | $0.02109 |
| Sonnet 5 | $0.00000 | $0.00844 |
| Haiku 4.5 | $0.00000 | $0.00422 |
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 yesterday.
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 — 580 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.
- yesterday First seen · 580 lines · 0 tokens per session scan A b2dea1810cb0
performance is a command published in the GitHub repository samibs/skillfoundry (12 stars, last pushed today), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 4,218 tokens. 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 2026-09-03.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.