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 skills add samibs/skillfoundry --skill 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/skills/samibs/skillfoundry/performance)<a href="https://agentmods.dev/skills/samibs/skillfoundry/performance"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/performance/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/samibs/skillfoundry/performance"><img src="https://agentmods.dev/badge/skills/samibs/skillfoundry/performance.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Prompt Injection · line 577 This pattern attempts to override system instructions or ignore safety constraints. Without LLM analysis, manual review is recommended.Fix: Remove or rewrite any text that instructs the agent to ignore prompts, override safety rules, or trust unverified content. Ensure skill content cannot be injected to alter agent behavior.
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 5.1 | $0.00005 | $0.04233 |
| Opus 5.5 | $0.00002 | $0.01693 |
| Sonnet 5.5 | $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 1mo 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.
- 1mo 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 14d ago), licensed MIT. It adds 5 tokens to every session and 4,233 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other skills, from other repositories
systemic-issue-triage
Trigger: new issue, bug report, triage, backlog, issue flood, community report, root cause, dead-end, blocked user. Attack issues by root class, never one-by-one; fixes must shrink the system, not grow it.
issue-root-resolution
Trigger: root audit, atacar la raíz, issue roots, backlog roots, mechanism map, deletion-driven fix, resolver issues de raíz, close outdated issues. Audit and resolve issue clusters by verified root cause.
rdd-defect-workflow
Trigger: RDD, receipt-driven development, review authority, receipt/lineage, correction/recovery, delivery gate/kill switch, bounded review defects. Guide work.
roam
Codebase comprehension via roam-code CLI. Use when exploring codebases, planning modifications, debugging failures, assessing PR risk, or checking architecture health. Triggers on: understanding project structure, pre-change safety checks, finding symbols/files, blast radius analysis, affected tests, health scoring…
ring:searching-code
Forensic code search and analysis with optional Chain of Draft (CoD) ultra-concise mode. Five-phase methodology (clarification, planning, execution, analysis, synthesis) with severity assessment. Use for targeted investigation of specific patterns, bugs, or vulnerabilities. Skip for broad architecture mapping (use…
memorix-troubleshooting
Use when Memorix MCP, setup, project binding, HTTP control plane, hooks, skills, or agent integration is missing, stale, or failing.