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 skills add mehrad-dm/mastermind --skill performancegit clone --depth 1 https://github.com/mehrad-dm/mastermindWrote 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/skills/mehrad-dm/mastermind/performance)<a href="https://agentmods.dev/skills/mehrad-dm/mastermind/performance"><img src="https://agentmods.dev/badge/skills/mehrad-dm/mastermind/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/skills/mehrad-dm/mastermind/performance"><img src="https://agentmods.dev/badge/skills/mehrad-dm/mastermind/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 | $0.00057 | $0.00746 |
| Opus 5.5 | $0.00023 | $0.00298 |
| Sonnet 5.5 | $0.00011 | $0.00149 |
| Haiku 4.5 | $0.00006 | $0.00075 |
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 15d 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 — 43 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance: measure, find the real bottleneck, fix the biggest, verify
Slowness has a real, measurable cause. The cardinal sin is optimizing by intuition. You'll spend effort on the wrong thing and maybe trade away correctness for nothing. Get data first.
The loop
- Reproduce + measure. Get a real number under a realistic scenario: wall-clock, FPS/frame time,
query ms (
EXPLAIN ANALYZE), request latency, bundle size, memory. No number, no optimizing. Write it down; it's your before. - Find the bottleneck: profile it. Use the right instrument (browser Performance panel /
React Profiler, a flame graph, DB query plan, a tracer) and find where the time actually goes, the
~20% causing ~80%. The universal classes of waste: repeated work (recomputed per item/render
instead of once), amplified work (one request fanning out into N), missing lookup structure
(a scan where an index/map belongs), serial waiting (round-trips that could be batched or
parallel), oversized payloads, and no caching of stable results. For the domain-specific
suspects, load the active field pack (
engineering/active-field.md→ the pack's performance section); if the field has no pack, let the profile, not a checklist, name the suspect. - Fix the biggest one. Make the single change with the most impact; resist micro-optimizing noise. Prefer doing less work (cache, batch, index, memoize, defer, paginate) over doing the same work faster.
- Verify the win. Re-measure the same way: confirm the number actually moved, and that behavior
and correctness are unchanged (
core/rigor.md). A "faster" version that's subtly wrong is a regression. - Guard it. Note the metric (a comment, a budget, a perf test) so the regression is visible next time.
After
Run levelup (capture) to record the bottleneck class and its lesson in the active field's
lessons.md: including the wrong suspect you ruled out, so MasterMind doesn't re-profile it next
time. Report: before → after numbers, the cause, the fix, and the guard added.
What ships with it
1 file 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.
- 15d ago Changed cf9d68342936
- 1mo ago First seen · 43 lines · 57 tokens per session scan A e79cef67c02b
performance is a skill published in the GitHub repository mehrad-dm/mastermind (23 stars, last pushed 6d ago), licensed MIT. It adds 57 tokens to every session and 746 once invoked, about $0.0002 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 2026-08-30.
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