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debono

Recover(归真·太虚五转):被过度批判命题的最强存活论证。触发词:归真, debono, 价值回溯, 被过度批判, 黄帽, 再想想, /debono.

Overview

Publisherfainshare
RepositoryLiam-Skills
Skill namedebono
Stars
113
Forks
19
Bundled files
7
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by fainshare on GitHub. Read the source before you install it.

Installation

Install the Debono AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/fainshare/Liam-Skills/tree/main/skills/debono
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fainshare/Liam-Skills.git /tmp/Liam-Skills
mkdir -p .claude/skills
cp -r /tmp/Liam-Skills/skills/debono .claude/skills/debono
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Debono in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Debono on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Debono is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Debono 德伯诺回溯 · 太虚五转·归真

"The purpose of thinking is to abolish thinking." — Edward de Bono

批判能找到漏洞,但找不到价值。归真是在批判的废墟里,捡回不该被埋没的东西。

概述

方案被批判管线打得很惨。但被否的不一定是错的,可能只是论证不完整、证据还没到、角度被忽略。这个 skill 为被质疑的命题构建最强存活论证:明确它在什么条件下仍然成立,从四个维度扫描被忽略的价值。

太虚五转第五转:归真(回溯)。散怀、澄源、叩实、破妄、归真,五转闭环。批判管线的终点,是回溯的起点。

定位

Feynman 审查产出、Bayes 验证结论、Hegel 收敛认知,这三者形成了一条强大的批判管线。但批判管线有盲区:它擅长排除,不擅长保留。Debono 补的是这块盲区:给被否定的命题做逆向价值扫描,不问"为什么错",问"在什么条件下对"。

五转分工表 SSOT 在 taixu-pipeline:references/methodology-mapping.md § 五转分工表。debono 在其中的定位:🟡 黄帽,回溯——找回被误杀的价值,问「被否掉的东西,有没有被忽略的价值?」。

铁律

执行 debono 时,禁止在输出中出现以下内容:

  • "但是"、"不过"、"然而"、"风险在于"、"潜在问题"
  • 任何形式的风险提示、问题指出、"值得注意"的转折
  • 隐性批判:"这个方案虽然…但…"的句式

你是辩护律师,不是法官。法官的工作由 feynman/think-tool 承担。 唯一例外:在"存活条件"段落中,可以诚实说明"命题在什么条件下不成立"——这是边界标注,不是批判。

构建方法论

从 5 种构建策略(S1 逆向利益相关方 / S2 条件缩窄法 / S3 时间延迟价值 / S4 类比复活法 / S5 反面代价法)中选择最适用的 3-4 种组合使用。每种策略有明确的分析路径,禁止凭空编造正面理由。

完整决策表——策略详情、problem_type 适配、乐观强度旋钮(L1/L2)、策略选择决策树(按否定类型推荐优先级)、输入输出 schema、触发场景、显式拒绝、边界条件、反模式——见 references/decision-tables.md。

四维框架的理论根基详见 references/methodology.md。

执行流程

命题输入 → 构建存活论证 → 明确存活条件 → 贝叶斯概率量化 → 四维价值扫描 → 输出 → [五转总结]
  1. 接收命题:单条或列表,带或不带上下文
  2. 构建最强存活论证:从 5 种构建策略中选择 3-4 种,沿分析路径生成论证
  3. 🟡 CHECKPOINT — 存活论证确认:展示构建的存活论证摘要,询问用户:「以上存活论证方向是否正确?需要调整或补充角度吗?」用户确认后继续
  4. 明确存活条件:诚实标注——什么情况下成立、什么情况下不成立
  5. 贝叶斯概率量化:为每个存活条件估计概率,计算期望存活概率。详见 references/bayesian-quantification.md
  6. 四维价值扫描:从用户价值、技术收益、团队成长、战略对齐四个维度扫描被忽略的价值
  7. 输出:Markdown 段落,可直接嵌入 hegel 报告或其他文档
  8. 🔒 五转综合总结(强制):当 debono 作为太虚五转管线的最后一步执行时,必须生成五转综合总结文档。详见 references/pipeline-summary.md

跨 Skill 触发协议

含 hegel→debono 黄帽对冲(>70% dismissed 触发)、feynman↔debono 双方论证、debono↔bayes 概率反馈环(先验来源 + 反馈环)、沉默触发器(3 信号建议模式)。详见 references/cross-skill-protocol.md。

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Debono AI skill do?

Recover(归真·太虚五转):被过度批判命题的最强存活论证。触发词:归真, debono, 价值回溯, 被过度批判, 黄帽, 再想想, /debono.

Why use Debono on TypingMind?

Because you install it once and use it with any model. Debono is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Debono in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fainshare/Liam-Skills/tree/main/skills/debono. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Debono?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Debono?

As many as you like. As long as a model supports skills, you can use Debono with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Debono AI skill free?

It is published on GitHub by fainshare. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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