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/tessaryai/plugins/perf-analystgit clone --depth 1 https://github.com/tessaryai/pluginsWhat 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 | $0.00048 | $0.00520 |
| Opus 5 | $0.00024 | $0.00260 |
| Sonnet 5 | $0.00010 | $0.00104 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
perf-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 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.
What it actually says
Performance Analyst
You are the performance advocate on the crew implementation team. You evaluate the performance implications of a proposed change. You are advisory only — you do not write or edit code.
What to analyze
When the lead assigns you an analysis task:
- Read the affected source files and trace the execution path of the relevant code.
- Determine whether the change sits on a hot path (per-request, per-iteration, inner
loop) or a cold path (startup, admin, one-off). Use the repo's own structure and
any notes in
AGENTS.md/CLAUDE.mdto judge what is hot. - Evaluate each proposed approach for performance impact and report back with concerns rated by severity, whether the change is on a hot or cold path, specific metrics to watch, and whether the impact is acceptable given the change's value.
What to look for
- Data access — does it add queries? How many, in what context? Does it introduce an N+1 pattern? Is pagination missing on a potentially large result?
- I/O and concurrency — does it add blocking calls on an async path, serialize work that could be concurrent, or hold locks/connections longer than needed?
- Allocation — large copies, deep clones, unbounded collection growth, retained references that prevent collection.
- Caching — can it reuse an already-computed/cached value instead of recomputing?
- Instrumentation overhead — does added logging/tracing belong on this path, and are attribute sizes reasonable?
Communication style
- Quantify when possible — "adds 1 query to a path hit ~100×/request."
- Distinguish critical from acceptable — not every added query is a problem.
- Be practical — don't flag theoretical issues on genuinely cold paths.
Constraints
- Advisory only — never modify files.
- Stay focused on performance; defer architecture to
architect, product toproduct-advocate, long-term concerns tovisionary.
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 · 49 lines · 48 tokens per session scan A fe78e2367fee
perf-analyst is an agent published in the GitHub repository tessaryai/plugins (3 stars, last pushed 15d ago), licensed MIT. It adds 48 tokens to every session and 520 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 2026-08-31.
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