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.
git clone --depth 1 https://github.com/MadAppGang/magusWrote 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/commands/madappgang/magus/performance)<a href="https://agentmods.dev/commands/madappgang/magus/performance"><img src="https://agentmods.dev/badge/commands/madappgang/magus/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/commands/madappgang/magus/performance"><img src="https://agentmods.dev/badge/commands/madappgang/magus/performance.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00025 | $0.00289 |
| Opus 5.5 | $0.00010 | $0.00116 |
| Sonnet 5.5 | $0.00005 | $0.00058 |
| Haiku 4.5 | $0.00003 | $0.00029 |
Grade A, and why
performance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
<user_request>$ARGUMENTS</user_request>
- Read
skills/performance/SKILL.mdin full first, resolved against this plugin's own directory (the tree this command was loaded from). It is the router: it names which files inskills/performance/references/your task actually needs. Read those too before writing code.
Use this when something is slow, when benchmarking, or when deciding whether an optimisation is worth it.
- Measure before changing anything. Copy
skills/performance/assets/perf/and use the harness — it warms up, calibrates iterations against the measured ~42 ns clock granularity, and reports median/MAD rather than a mean that one GC pause destroys. - Work the optimisation order: do it fewer times → off the request path → lazily → only then make it faster. An accidental O(n²) beats every micro-optimisation applied to it.
- Before reporting done: produce a before/after measurement from the same harness in the same session, including the spread. A change inside the noise band is not a change.
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 · 23 lines · 25 tokens per session scan A 3d3ea0954e65
performance is a command published in the GitHub repository MadAppGang/magus (13 stars, last pushed 2d ago), licensed MIT. It adds 25 tokens to every session and 289 once invoked, about $0.0001 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 2026-08-31.
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