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 skills/hecer/yoke/performancenpx skills add HECer/yoke --skill performancegit clone --depth 1 https://github.com/HECer/yokeWhat 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 | $0.00056 | $0.00694 |
| Opus 5 | $0.00028 | $0.00347 |
| Sonnet 5 | $0.00011 | $0.00139 |
| Haiku 4.5 | $0.00006 | $0.00069 |
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 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.
How it starts
The opening of the file, as written. The whole thing — 49 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Performance (measured, not vibed)
"Efficient" is a requirement, not a code style. Untested performance claims rot exactly like untested behavior claims. This skill makes efficiency mechanical — the same move Yoke makes for everything else.
The decision ladder
- Default: clean + minimal. For ~90% of code, the
minimal-coderules ARE the performance strategy — less code, fewer layers, no speculative abstraction. Do not micro-optimize code that no measurement flagged (premature optimization). - Performance requirement? Make it an acceptance criterion. A number, not an adjective:
- Good: "imports 1M rows in < 2s", "p95 request latency < 50ms in the bench test", "no allocation inside the render loop (verified by the bench assertion)"
- Bad: "should be fast", "optimize the importer"
- Whole-project budget? Use the perf gate. Set
perf.commandin.yoke/config.yaml(a benchmark script; exit 0 = within budget). The loop runs it after verify — a story that breaks the budget is blocked, no matter how clean its diff is.
Writing efficient code that agents can maintain
- Clean at the boundaries, aggressive in the leaves. Interfaces, data flow, and names stay simple and obvious. Optimization lives inside a few clearly-bounded leaf functions whose contracts are pinned by tests. An ugly-fast function is maintainable; an ugly-fast architecture is not.
- Profile before optimizing. Find the actual hot 5% (a profiler, a timing harness, the bench script) — never optimize from intuition. Record the measurement in the PR/commit.
- Benchmarks are tests. Commit them. An optimization without a committed benchmark is
one refactor away from silently disappearing. The bench script doubles as
perf.command. - Version the WHY. Every non-obvious optimization gets a one-line comment
(
perf: avoids N+1 — see bench/import.mjs) and, if it shaped a design, a line incontext/DECISIONS.md. The most common AI maintenance accident is a later agent "simplifying" fast code back to slow because nothing said why it was shaped that way. - Know the classics before reaching for cleverness: right data structure (map vs list scan), batching over per-item round trips (N+1), streaming over buffering, avoiding repeated work in loops, caching only with a measured hit rate and an invalidation story.
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 · 56 tokens per session scan A 5d0916174098
performance is a skill published in the GitHub repository HECer/yoke (2 stars, last pushed 11d ago), licensed MIT. It adds 56 tokens to every session and 694 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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