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/corallips/thinking-tree/catchnpx skills add CoralLips/thinking-tree --skill catchgit clone --depth 1 https://github.com/CoralLips/thinking-treeWhat 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 | $0.00587 |
| Opus 5 | $0.00017 | $0.00293 |
| Sonnet 5 | $0.00007 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
catch 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 yesterday.
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
补录捕获
当自动记录遗漏了有价值的对话认知时，用户可以用这个命令手动补录。
用法
/catch 我们刚才关于碎片转化速度的讨论
/catch 上面 AI 分析的那个架构边界的判断
/catch 最近几轮关于捕获和思考关系的讨论
$ARGUMENTS 是自然语言提示，描述想要捕获的内容方向。
执行步骤
1. 回溯对话
回顾最近 10-15 轮对话内容（当前会话内），重点关注：
- 用户提示方向（
$ARGUMENTS）相关的讨论 - AI 回复中的分析结论、架构判断、独立洞察
- 对话碰撞中产生的新认知
2. 提取认知
从回溯的对话中提取值得记录的独立认知点。每个点必须：
- 脱离对话上下文后仍然可理解
- 不与
~/.thinking-tree/fragments.md中已有碎片重复 - 有明确的一个点（不是模糊的"讨论了 XXX"）
3. 路由判断
对每个提取出的认知点：
- 独立观点 → 碎片（fragment）
- 明确的疑问 → 问题（question）
- 具体可执行 → 行动项（todo）
4. 写入
通过原子写入脚本逐条写入：
echo '{"type":"fragment","title":"#标签 标题（日期）","body":"内容描述。"}' | node ~/.thinking-tree/bin/write-item.js
5. 输出确认
列出所有补录的条目：
补录完成：
📝 #标签1 标题1
📝 #标签2 标题2
❓ 问题标题
共 N 条
注意事项
- 先读
~/.thinking-tree/fragments.md确认不重复 - 碎片标题加
#标签前缀和日期后缀，与现有碎片风格一致 - 如果
$ARGUMENTS为空，扫描最近 5 轮对话，自动识别遗漏 - 每次补录不超过 5 条，避免噪音
- 写入使用
write-item.js，不要直接 Edit/Write 文件
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.
- yesterday First seen · 70 lines · 34 tokens per session scan A bc0b030b4485
catch is a skill published in the GitHub repository CoralLips/thinking-tree (1 stars, last pushed 4mo ago), licensed MIT. It adds 34 tokens to every session and 587 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 2026-08-31.
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