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/distillnpx skills add CoralLips/thinking-tree --skill distillgit 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 | $0.00041 | $0.01711 |
| Opus 5 | $0.00020 | $0.00856 |
| Sonnet 5 | $0.00008 | $0.00342 |
| Haiku 4.5 | $0.00004 | $0.00171 |
Grade A, and why
distill 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.
How it starts
The opening of the file, as written. The whole thing — 161 lines — stays where its author put it; the contents beside it link to each section on GitHub.
碎片→体系
将碎片池中的碎片转化为结构化的思路体系。两种动作:
- 归入已有思路文件:碎片主题与已有文件高度匹配 → 整合进去
- 新建思路文件:多条碎片可聚合为新主线 → 合成新文档
/distill → AI 扫描碎片池,建议可归类和提炼的内容
/distill 产品定位的核心思路 → 按用户方向找碎片,直接进入提炼
$ARGUMENTS 是可选的主题方向提示。
推荐顺序:先
/reduce(清垃圾),再/distill(建结构)
工具准备
本 skill 依赖 AskUserQuestion 提供选项式确认。进入交互前:
- 若工具列表已含
AskUserQuestion→ 直接使用 - 未加载 → 先执行
ToolSearch("select:AskUserQuestion")加载 schema - 加载失败(session 不支持该工具)→ 降级为纯文本确认:
- 清晰列出编号选项(例如
1) 全部归入 2) 逐条选择 3) 跳过) - 明确告知回复格式(例如「回复编号」)
- 不要跳过确认步骤
- 清晰列出编号选项(例如
降级只影响呈现方式,不影响功能。
执行步骤
1. 读取数据
读取以下文件(~ = 用户 home 目录):
~/.thinking-tree/fragments.md— 碎片池(提炼素材)~/.thinking-tree/目录下所有 *.md 思路文件 — 读取标题和大纲(判断归入目标、避免主题重复)
2. 识别动作
扫描碎片池,对每条碎片判断:
- 可归入:主题与某个已有思路文件的主线高度匹配
- 可聚合:多条碎片围绕同一主题,可合成新思路文件
- 暂留:独立观点,暂不适合归入或聚合
有参数时:
按用户给的方向,从碎片池中找出语义相关的碎片,进入提炼流程。
无参数时:
输出两部分建议,按顺序处理:
A. 可归入已有思路文件的碎片(如果有):
使用 AskUserQuestion:
- question: "发现 N 条碎片可归入已有思路文件,如何处理?"
- header: "归类"
- multiSelect: false
- options:
- label: "全部归入 (Recommended)", description: "将碎片整合进对应思路文件:[列出 碎片标题 → 目标文件]"
- label: "逐条选择", description: "展开每条碎片,逐一确认"
- label: "跳过", description: "不归类,继续看可提炼的主题"
如果用户选"逐条选择",对每条碎片用单选确认:
- options: "归入 XXX.md (Recommended)" / "保留在碎片池"
B. 可提炼为新思路文件的主题(如果有):
使用 AskUserQuestion:
- question: "发现 N 个主题方向可以提炼为思路文件,如何处理?"
- header: "提炼"
- multiSelect: false
- options:
- label: "全部提炼 (Recommended)", description: "依次生成 N 个思路文件:[列出 主题→文件名(涉及碎片数)]"
- label: "逐个选择", description: "展开每个主题,逐一确认是否提炼"
- label: "跳过", description: "不提炼,保持现状"
如果用户选"逐个选择",对每个主题用单选确认:
- options: "提炼为 XXX.md (Recommended)" / "跳过"
3. 归类执行(如果有)
用户确认归类后:
- 将碎片内容追加到对应思路文件末尾(加
---分隔) - 从
fragments.md移除已归类的碎片 - 保持目标文件的既有风格和结构
4. 展示素材与大纲(提炼新文件时)
展示:
- 涉及的碎片标题列表
- 一句话主线概述
- 建议的文件名(如
positioning.md) - 三级大纲草案
使用 AskUserQuestion:
- question: "以上是提炼方案,如何处理?"
- header: "大纲确认"
- options:
- label: "确认,开始提炼", description: "按此大纲生成思路文件"
- label: "调整方向", description: "补充说明后重新生成大纲"
- label: "取消", description: "不提炼,返回"
- multiSelect: false
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 · 161 lines · 41 tokens per session scan A 43ba41f5320c
distill is a skill published in the GitHub repository CoralLips/thinking-tree (1 stars, last pushed 4mo ago), licensed MIT. It adds 41 tokens to every session and 1,711 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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