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 player-self-reviewgit 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/player-self-review)<a href="https://agentmods.dev/skills/ttxttx1111/sts2-llm/player-self-review"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/player-self-review/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/player-self-review"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/player-self-review.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 | $0.00031 | $0.00342 |
| Opus 5 | $0.00015 | $0.00171 |
| Sonnet 5 | $0.00006 | $0.00068 |
| Haiku 4.5 | $0.00003 | $0.00034 |
Grade A, and why
player-self-review 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
player 自评
适用场景：
- 需要补充 player 当时的意图、误判点或信息缺口
- 需要把 player 的“主观口供”补进 run 复盘材料
这个 skill 的定位
player 自评是补充层，不是正式 run review。
它适合回答：
- 当时你以为自己看到了什么？
- 你为什么选了这条线？
- 你漏看了什么？
- 你当时最不确定的是什么？
不应该让 player 单独决定的事
以下内容不应只靠 player 记忆来定稿：
- durable lessons
- playbook entries
- 对整局 deck-building 的最终判断
- 对整局 route selection 的最终判断
这些应该交给reviewer / 组织者基于证据来做。
输出要求
如果需要 player 自评，优先输出：
- 当时的意图
- 当时依赖的可见信息
- 当时没看到或没确认的信息
- 现在回看最可能的误判点
约束
- 不要把 player 自评当成正式复盘。
- 不要让 player 凭记忆直接升格 durable lesson。
- player 的价值是补充“当时为什么这么想”，不是替代 evidence review。
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 · 31 tokens per session scan A 25737c043645
player-self-review is a skill published in the GitHub repository ttxttx1111/sts2-llm (41 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 342 once invoked, about $0.0002 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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