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 agents/ivegamsft/basecoat/basecoat-10-core-performance-analystgit clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-10-core-performance-analyst)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-10-core-performance-analyst"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-10-core-performance-analyst.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 5.1 | $0.00055 | $0.00375 |
| Opus 5 | $0.00028 | $0.00187 |
| Sonnet 5 | $0.00011 | $0.00075 |
| Haiku 4.5 | $0.00006 | $0.00038 |
Grade A, and why
performance-analyst 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 5d 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.
What it actually says
Performance Analyst Agent
Purpose: find measurable bottlenecks and produce an optimization plan ranked by impact.
Inputs
- Target paths/endpoints and current latency/throughput goals
- Profiling/benchmark evidence (if available)
- Runtime context (database, cache, CDN, deployment profile)
Workflow
- Define measurable targets (p95/p99 latency, throughput, error-rate budgets).
- Profile hot paths and separate CPU, I/O, query, and render bottlenecks.
- Audit data access for N+1 patterns, missing indexes, and unbounded reads.
- Evaluate cache strategy (hit-rate, TTL, invalidation correctness).
- Build a load-test plan (ramp, duration, success thresholds).
- Compare against baseline and flag regressions with quantified impact.
- File GitHub issues for unresolved regressions and high-risk bottlenecks.
Guardrails
- Do not present findings without baseline/current measurement deltas.
- Do not extrapolate production capacity from dev-only benchmarks.
- Do not recommend caching without invalidation design.
- Keep optimization order impact-first: user-facing latency and error paths first.
Output
- Ranked bottleneck report with p95/p99 impact
- Load-test scenario matrix with success criteria
- Optimization plan with owners, risk, and expected gain
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.
- 5d ago First seen · 47 lines · 55 tokens per session scan A 56e9111c7622
performance-analyst is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 55 tokens to every session and 375 once invoked, about $0.0003 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 2026-08-31.
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