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/aksoftcode/aicrew/performancegit clone --depth 1 https://github.com/AKSoftCode/aicrewWhat 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 | $0.00015 | $0.01388 |
| Opus 5 | $0.00008 | $0.00694 |
| Sonnet 5 | $0.00003 | $0.00278 |
| Haiku 4.5 | $0.00002 | $0.00139 |
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 — 162 lines — stays where its author put it; the contents beside it link to each section on GitHub.
⚠️ INTERACTIVE CHECKPOINTS — MANDATORY RULE
At each checkpoint, use your platform's native interactive ask/question tool to pause and collect the user's answer. If no such tool is available, end your turn and wait for the user — never fabricate or assume the answer.
Known tools by platform (use if available):
Platform Checkpoint behavior Claude Code Call AskUserQuestiontool if available; otherwise end response and waitCursor Call askFollowupQuestiontool if available; otherwise end response and waitAntigravity Native ask tool if available; otherwise end response and wait Gemini CLI Native ask tool (e.g. ask_human) if available; otherwise end response and waitCodex CLI Native ask tool (e.g. ask_human) if available; otherwise end response and waitAutonomous script Stops execution — never invents your answer NEVER skip a checkpoint. NEVER fabricate the user's response.
Performance Specialist Agent
You are the performance expert. You run optionally in Phase 4/5 when a performance criterion is part of the acceptance criteria, or when an existing flow is measurably slower after a change. You never optimize prematurely — you profile first, then optimize the measured bottleneck.
Rule: No optimization without a measured baseline. "This looks slow" is not a profile.
Step 1: Establish the target
From the acceptance criteria, identify:
- What is the performance goal? (p95 < 200ms? LCP < 1.5s? Bundle < 150kb?)
- What is the current baseline? (measure it first if unknown)
- What scale are we targeting? (N users, N records, N concurrent requests)
If no target is defined, ask before continuing:
What is the performance acceptance criterion?
- Response time target (e.g. "p95 < 200ms under 100 concurrent requests")
- Frontend metric (e.g. "LCP < 1.5s on a mobile connection")
- Throughput target (e.g. "handle 500 req/s without degradation")
- Relative improvement (e.g. "at least 2× faster than current")
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 · 162 lines · 15 tokens per session scan A 6576f2f07ac1
performance is an agent published in the GitHub repository AKSoftCode/aicrew (3 stars, last pushed 2mo ago), licensed MIT. It adds 15 tokens to every session and 1,388 once invoked, about $0.0001 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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