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/reducenpx skills add CoralLips/thinking-tree --skill reducegit 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 | $0.00036 | $0.01374 |
| Opus 5 | $0.00018 | $0.00687 |
| Sonnet 5 | $0.00007 | $0.00275 |
| Haiku 4.5 | $0.00004 | $0.00137 |
Grade A, and why
reduce 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 — 129 lines — stays where its author put it; the contents beside it link to each section on GitHub.
碎片池瘦身
对 thinking-tree 碎片池进行清理:偏好过滤、去重、过时删除。 纯减法操作——不读不写思路文件,只在碎片池内部操作。 使用 AskUserQuestion 提供选项式交互,用户无需打字即可审核。
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) 全部删除 2) 逐条选择 3) 全部保留) - 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(主要处理对象)~/.thinking-tree/.preferences.md— 用户偏好(判断碎片是否符合记录范围)
2. 分析碎片
对每个碎片(以 ## 开头、由 --- 分隔的段落)逐条评估,分为四组:
| 组 | 判断标准 | 动作 |
|---|---|---|
| 偏好外组 | 不符合 .preferences.md 中的记录范围(如工程细节) | 删除 |
| 重复组 | 核心论点与另一条碎片相同(仅措辞/例子不同) | 合并为一条 |
| 过时组 | 已被后续思考覆盖、推翻、或已无参考价值 | 删除 |
| 保留组 | 独立有效,不重复不过时 | 保留原位 |
3. 输出总览
先输出一行统计,让用户了解整体情况:
整理分析完成:73 条碎片 → 偏好外 6 条,重复 12 条,疑似过时 5 条,保留 50 条
4. 逐组确认(AskUserQuestion 交互)
重要:使用 AskUserQuestion 工具让用户通过选项确认,不要让用户打字。 交互原则:推荐操作放第一位,让用户一键确认即可进入下一步。
4a. 偏好外组 — 单选批量确认
读取 .preferences.md 的记录范围,标记不符合的碎片。使用 AskUserQuestion:
- question: "发现 N 条碎片不符合当前记录偏好(如:工程细节),如何处理?"
- header: "偏好过滤"
- multiSelect: false
- options:
- label: "全部删除 (Recommended)", description: "删除全部 N 条偏好外碎片:[列出标题摘要]"
- label: "逐条选择", description: "展开每条碎片,逐一确认是否删除"
- label: "全部保留", description: "不做改动"
如果用户选"逐条选择",再用 multiSelect 展开每条碎片供选择(最多 4 条一批)。
4b. 重复组 — 每组重复碎片一起确认
对每组重复碎片(2-3 条一组),使用 AskUserQuestion:
- question: "这 N 条碎片表达了相似观点,如何处理?"
- header: "去重"
- options:
- label: "合并 (Recommended)", description: "保留最精炼的表述,删除冗余"
- label: "全部保留", description: "不做改动"
- preview: 展示这几条碎片的标题和关键内容对比
- multiSelect: false
4c. 过时组 — 单选批量确认
使用 AskUserQuestion:
- question: "发现 N 条碎片可能已过时,如何处理?"
- header: "过时清理"
- multiSelect: false
- options:
- label: "全部删除 (Recommended)", description: "删除全部 N 条过时碎片:[列出标题摘要+过时原因]"
- label: "逐条选择", description: "展开每条碎片,逐一确认是否删除"
- label: "全部保留", description: "不做改动"
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 · 129 lines · 36 tokens per session scan A 83ae203909e8
reduce is a skill published in the GitHub repository CoralLips/thinking-tree (1 stars, last pushed 4mo ago), licensed MIT. It adds 36 tokens to every session and 1,374 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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