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/ttxttx1111/sts2-llm/debug-through-player-chatnpx skills add ttxttx1111/sts2-llm --skill debug-through-player-chatgit 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/debug-through-player-chat)<a href="https://agentmods.dev/skills/ttxttx1111/sts2-llm/debug-through-player-chat"><img src="https://agentmods.dev/badge/skills/ttxttx1111/sts2-llm/debug-through-player-chat.svg" alt="Measured on agentmods" 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 | $0.00034 | $0.00365 |
| Opus 5 | $0.00017 | $0.00182 |
| Sonnet 5 | $0.00007 | $0.00073 |
| Haiku 4.5 | $0.00003 | $0.00036 |
Grade A, and why
debug-through-player-chat 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 5d 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
通过 player-chat 调试
适用场景：
- 用户怀疑 player 判断不对
- live run 卡住了，但还不知道是 scene 误判、API 异常还是 player 理解错误
- 需要组织者先和 player 对话求证，而不是马上改代码
默认做法
先用：
python -m ai_play_slts2.cli player-chat --player-backend copilot_acp
应该追问什么
优先追问这些问题：
- 你现在看到的 scene 是什么？
- 你认为当前最关键的可选动作是什么？
- 你为什么排除其它动作？
- 你是否缺少额外检查信息，例如 deck / draw pile / discard pile / menu 状态？
- 你卡住是因为判断不清，还是因为执行层没有按预期响应？
目的
这条技能的目的不是“让组织者替 player 决策”，而是：
- 让 player 把当前理解显式说出来
- 快速区分问题来源：
- player 推理问题
- scene / 状态识别问题
- live API / 执行问题
约束
- 不要一上来就默认是 runtime 或代码 bug。
- 不要跳过求证过程，直接给一堆系统层假设。
- 如果问题可以通过对话澄清，就先不要引入新的包装层。
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.
- 5d ago First seen · 47 lines · 34 tokens per session scan A 1c66812e13c2
debug-through-player-chat is a skill published in the GitHub repository ttxttx1111/sts2-llm (41 stars, last pushed 1mo ago), licensed MIT. It adds 34 tokens to every session and 365 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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