Borrowing it
Nothing to install: this file belongs to gaoconggit/SharpPad. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/gaoconggit/SharpPad/main/.claude/commands/optimize.mdgit clone --depth 1 https://github.com/gaoconggit/SharpPadWrote 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/gaoconggit/sharppad/optimize)<a href="https://agentmods.dev/commands/gaoconggit/sharppad/optimize"><img src="https://agentmods.dev/badge/commands/gaoconggit/sharppad/optimize/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/gaoconggit/sharppad/optimize"><img src="https://agentmods.dev/badge/commands/gaoconggit/sharppad/optimize.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 | $0.00000 | $0.00010 |
| Opus 5.5 | $0.00000 | $0.00004 |
| Sonnet 5.5 | $0.00000 | $0.00002 |
| Haiku 4.5 | $0.00000 | $0.00001 |
Grade A, and why
optimize 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 13d 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.
This is a copy
100% identical to varcheck — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
分析此代码的性能问题并建议优化:
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.
- 13d ago First seen · 2 lines · 0 tokens per session scan A 5860f17ae33c
optimize is a command published in the GitHub repository gaoconggit/SharpPad (83 stars, last pushed 7mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 10 tokens. A static security scan graded it A with 0 findings. It is 100% identical to varcheck, differing in 2 lines, and is treated as a copy.
Other commands, from other repositories
check-tcc
Print che-ical-mcp's current TCC permission state, binary path, and ready-to-paste reset / re-prompt commands. Use when user says "check TCC", "permission status", "tccutil"。.
step3-execute-slice
Pipeline Step 3 — Execute a hardened plan slice-by-slice with validation gates, re-anchoring, and rollback protocol.
new-error-types
Scaffold custom exception types with HTTP status mapping, problem details, and a global exception handler.
step4-completeness-sweep
Pipeline Step 4 — Completeness sweep to eliminate TODOs, mocks, stubs, and placeholder code before review.
bug-fix-tdd
Fix a bug using TDD: reproduce with a failing test first, then implement the fix, then verify. Prevents regressions.
doctor
A command that diagnoses the Computer Use plugin, its environment, and its mapped capabilities. Computer Use lets an agent interact with a computer through supported actions.