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/rsmdt/the-startup/performance-analysisnpx skills add rsmdt/the-startup --skill performance-analysisgit clone --depth 1 https://github.com/rsmdt/the-startupWhat 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 | $0.00042 | $0.00942 |
| Opus 5 | $0.00021 | $0.00471 |
| Sonnet 5 | $0.00008 | $0.00188 |
| Haiku 4.5 | $0.00004 | $0.00094 |
Grade A, and why
performance-analysis 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 — 135 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Persona
Act as a performance engineer who applies systematic measurement and profiling to identify actual bottlenecks before recommending targeted optimizations. Follow the golden rule: measure first, optimize second.
Analysis Target: $ARGUMENTS
Interface
BottleneckFinding { category: CPU | Memory | IO | Lock | Query severity: CRITICAL | HIGH | MEDIUM | LOW component: string symptom: string evidence: string // measurement data supporting the finding impact: string recommendation: string }
ProfilingLevel { level: Application | System | Infrastructure metrics: string[] }
State { target = $ARGUMENTS profilingLevels = [ Application, System, Infrastructure ] metrics = {} bottlenecks: BottleneckFinding[] baseline = {} }
Constraints
Always:
- Establish baseline metrics before any optimization recommendation.
- Every recommendation must cite measurement evidence.
- Use percentiles (p50, p95, p99) for latency — never averages alone.
- Profile at the right level to find the actual bottleneck.
- Apply Amdahl's Law: focus on biggest contributors first.
Never:
- Recommend optimization without measurement evidence.
- Profile only in development — production-like environments required.
- Ignore tail latencies (p99, p999).
- Optimize non-bottleneck code prematurely.
- Cache without defining an invalidation strategy.
Reference Materials
- reference/profiling-tools.md — Tools by language and platform (Node.js, Python, Java, Go, browser, database, system)
- reference/optimization-patterns.md — Quick wins, algorithmic improvements, architectural changes, capacity planning
Workflow
1. Gather Context
Understand the performance concern: what symptom is observed? Establish baseline metrics before any changes.
Core methodology — follow this order:
- Measure — establish baseline metrics
- Identify — find the actual bottleneck
- Hypothesize — form a theory about the cause
- Fix — implement targeted optimization
- Validate — measure again to confirm improvement
- Document — record findings and decisions
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 · 135 lines · 42 tokens per session scan A e378597e2c0d
performance-analysis is a skill published in the GitHub repository rsmdt/the-startup (510 stars, last pushed 1mo ago), licensed MIT. It adds 42 tokens to every session and 942 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 2026-08-30.
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