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 ttxttx1111/sts2-llm --skill review-latest-rungit clone --depth 1 https://github.com/ttxttx1111/sts2-llmWrote 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/ttxttx1111/sts2-llm/review-latest-run)<a href="https://agentmods.dev/skills/ttxttx1111/sts2-llm/review-latest-run"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/review-latest-run/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/ttxttx1111/sts2-llm/review-latest-run"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/review-latest-run.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 | $0.00029 | $0.00351 |
| Opus 5 | $0.00015 | $0.00176 |
| Sonnet 5 | $0.00006 | $0.00070 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
review-latest-run 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 12d 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
复盘最新 run
适用场景：需要从最新完成的 run 中提取 lesson、hypothesis 或开发改进点。
角色定位
这张 skill 对应的是正式 review，默认由 reviewer / 组织者来执行。
- player 可以提供自评和当时意图
- 但正式复盘必须以已持久化证据为主
- 不要把 player 的回忆当成 review 主体
阅读内容
data\python-runtime\runs\下最新 runbattle-summaries.jsonscene-summaries.jsonrun-summary.json- 最近的
events.jsonl data\python-runtime\knowledge\lessons.jsondata\python-runtime\knowledge\playbook.json
复盘角度
- battle 执行
- deck-building
- route selection
输出结构
请分开：
- durable lessons
- tentative hypotheses
- developer / runtime gaps
- player self-report（如果有，也只作为补充材料）
约束
- 必须基于证据。
- 优先引用 event 和 summary，不要给泛泛而谈的攻略。
- 不要把一次 run 的现象直接升格为长期定律。
- battle / deck-building / route selection 三个角度的正式判断，优先由 reviewer / 组织者读记录完成。
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.
- 12d ago First seen · 49 lines · 29 tokens per session scan A d119d382154e
review-latest-run is a skill published in the GitHub repository ttxttx1111/sts2-llm (41 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 351 once invoked, about $0.0001 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 2026-08-30.
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