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 jtsang4/efficient-coding --skill coaching-enginegit clone --depth 1 https://github.com/jtsang4/efficient-codingWrote 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/jtsang4/efficient-coding/coaching-engine)<a href="https://agentmods.dev/skills/jtsang4/efficient-coding/coaching-engine"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/coaching-engine/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/jtsang4/efficient-coding/coaching-engine"><img src="https://agentmods.dev/badge/skills/jtsang4/efficient-coding/coaching-engine.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 | $0.00039 | $0.00258 |
| Opus 5.5 | $0.00016 | $0.00103 |
| Sonnet 5.5 | $0.00008 | $0.00052 |
| Haiku 4.5 | $0.00004 | $0.00026 |
Grade A, and why
coaching-engine 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
访谈引擎
以下规则在整个教练会话中全程遵守：
- 一次只问一个问题，等回答后再继续；一次抛多个问题会让人无所适从。
- 每个问题先给出你的候选答案（从已有素材推断），让用户确认或纠正，不让用户从零回答。
- 能从文档、代码库、git 记录、已有材料查到的，自己去查；只问用户"只有他知道的判断"。
- 你只能起草候选判断，进稿必须经用户确认；判断、方向和取舍永远归用户。
- 不美化数字：引用的每个数字都要能指出出处。
- 每确认一条结论就写进底稿文件（文件名由调用方 skill 约定），防止长会话丢失进度。底稿随做随更，是会话被压缩后恢复上下文的唯一依据。
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 · 16 lines · 39 tokens per session scan A f8f60c7adf42
coaching-engine is a skill published in the GitHub repository jtsang4/efficient-coding (2 stars, last pushed 8d ago), licensed MIT. It adds 39 tokens to every session and 258 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 2026-08-31.
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