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Taixu Pipeline

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fainshare
taixu-pipeline

Pipeline(串联五转):osborn→hegel→bayes→feynman→debono 自动串联编排器。触发词: 一键五转, 跑五转, taixu-pipeline.

Overview

Publisherfainshare
RepositoryLiam-Skills
Skill nametaixu-pipeline
Stars
113
Forks
19
Bundled files
19
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.

  • 19 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 Taixu Pipeline 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/taixu-pipeline
  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/taixu-pipeline .claude/skills/taixu-pipeline
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Taixu Pipeline 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 Taixu Pipeline 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 Taixu Pipeline 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.

太虚五转自动串联编排器

一键串联 osborn→hegel→bayes→feynman→debono,保证完整五转流程不被嵌套降级污染。

使用方式

触发词:太虚五转、一键五转,taixu-pipeline

需要提供 input.topic(分析主题,如"太虚五转自动串联 + 防 Hegel 嵌套降级")。

可选参数:

  • input.type — 显式指定问题类型(deterministic / non_deterministic / mixed),透传给 osborn 作为最高优先级信号
  • --steps osborn,hegel,bayes — 仅执行指定步骤子集
  • 每个步骤后设 CHECKPOINT,用户可选择继续、跳过或终止

前置路由(brainstorm/ce-brainstorm 已废弃,能力归入 pipeline)

ce-brainstorm 和 brainstorm 已完全废弃,其能力分散到太虚五转各层。当用户使用以下触发词时,pipeline 执行前置路由:

用户说路由逻辑
"brainstorm" / "头脑风暴" / "先设计"Step 0 scope assessment → 主题清晰且需求明确 → quick exit(hegel --spec-only 或 liam-plan);主题清晰但方向待探索 → osborn P0;主题模糊 → osborn P0-clarify
"出方案" / "规格化" / "写spec"hegel --spec-only(快速规格化,跳过完整收敛循环)
"从太虚产出写需求"hegel --lens specification-bridge(从五转产出转化为 requirements doc)

Step 0 — Scope Assessment(吸收自 ce-brainstorm Phase 0.2-0.3)

pipeline 入口执行 scope assessment,判定工作量和路由:

  • Lightweight(小、边界清晰,低模糊度)→ 快速路由:osborn P0 简化发散 → hegel --spec-only → 结束
  • Standard(正常功能、有些决策要做)→ 完整五转
  • Deep(跨领域、战略性,高模糊度)→ 完整五转 + bayes 异构验证

路由判定由 osborn P0-clarify 的清晰度测试自动完成:能用一句话说清主题 + 识别 ≥3 个维度轴则直接发散,否则先做 Q&A 澄清。

Quick Exit(吸收自 ce-brainstorm Phase 0.2)

当用户需求已明确(有验收标准、有可参考模式,行为已描述清楚、scope 有约束),不需要发散:

  • 需要规格化 → hegel --spec-only
  • 需要执行计划 → 建议直接用 liam-plan
  • 仅需确认理解 → 简要回复,不启动 pipeline

组合概览

  1. osborn (skill: osborn) — 发散阶段,产出 idea-pool.md
  2. hegel (skill: hegel --standalone) — 收敛阶段,⚠️ 硬编码传入 --standalone 跳过内嵌验证
  3. bayes (skill: bayes) — 验证阶段,使用独立 lens 完整能力
  4. feynman (skill: feynman) — 审查阶段,使用独立 lens 完整能力
  5. debono (skill — debono) — 归真阶段,on_failure: skip,可选。debono 完成后自动触发五转综合总结,V2.2 强制,写入 docs/reports/{topic-slug}-taixu-summary-{date}.md
  6. 执行契约补齐 + plan 桥接(收尾步骤)— 检查 hegel spec,若走了 specification-bridge是否含"执行契约附录",Constraints/Boundaries/Pause if,见 hegel references/lens-specification-bridge.md;缺则从五转产出中派生补齐,标注来源 finding 或 [假设,可推翻]。然后明示用户:可进入 liam-plan,spec 已就绪,走 0.3 carry forward 直接规划路径,零追问。

方法论映射

两大策略(确定性/非确定性)→ 六要素 → 五转的多对多映射。详见 references/methodology-mapping.md。

防嵌套机制

  • hegel step 的 args.standalone: true 在 composition block 中硬编码,配置级强制
  • hegel SKILL.md §6.1/§7.1 检测到 --standalone 时跳过 bayes/feynman 内嵌调用
  • 独立调用 /hegel 时不带此参数,行为完全不变(向后兼容)

审查与验证阶段强制条款(D11/D12 修复,2026-07-19)

受控 A/B 实验(runtime/taixu-ab-test/2026-07-18/)发现 2.0 在 D12,审查检出力相对 1.0 退坡 -2.5、D11,验证退坡 -1.0。根因在编排层而非 skill 本体,修复如下:

  1. feynman 盲审不可降级:时间预算紧不构成降级理由。仅当 subagent 工具调用本身失败时才允许降级为主 Agent 自审,且降级必须在 feynman 报告头部显式标注 review_mode: self (fallback)。
  2. 宽松度自查:盲审结果 0 veto 且合规项 ≥5/6 时,主 Agent 必须列出 3 条「最可能被否决的断言」逐条自证,写入 feynman 报告附录;自证不通过的断言按 veto 处理。
  3. 深挖优先:bayes 报告中所有 ⚠️[待验证] 项在进入 debono 前必须完成 Bayes 联动深挖(references/depth-engine-principles.md),禁止带未深挖的待验证项进入归真阶段。
  4. bayes 精确数据源约束 —refuted 判定必须附 file:line / 脚本输出 / 台账定位;估算值只能支撑 unverified 或 partial,不得用于推翻断言。

CHECKPOINT 协议

每个 step 完成后暂停,展示信号、时间预算,相变检测、problem_type 路由。详见 references/checkpoint-protocol.md。

UltraPlan 并行模式

可选增强,支持 osborn 维度并行 fan-out + bayes 异构验证 fan-out。详见 references/ultraplan-parallel.md。

--steps 子集执行

支持 --steps hegel,bayes 语法:

  • 仅执行指定步骤及其依赖
  • 未指定步骤的产出物需已存在(从上次运行恢复)
  • 若依赖缺失且无法恢复,报错并提示补全

注意

  • 所有原子 skill 仍可独立使用,本 composed skill 是可选上层编排

  • debono 为 soft_depends_on + on_failure: skip,即使 feynman 失败也不阻断管线

  • debono 完成后自动触发五转综合总结(debono V2.2 内置),生成 docs/reports/{topic}-taixu-summary-{date}.md,包含全程叙事、关键转折,核心洞察、元认知收获。此为强制步骤,不可跳过

  • 全程产出物写入 docs/hegel-reports/ 或 docs/osborn-pools/,综合总结写入 docs/reports/,按日期命名

  • pipeline 全部 step 完成后(或用户终止后),将各 step 的执行时间汇总追加到 debono 综合总结末尾,格式:

    markdown
    ## 时间日志
    | Step | 耗时 | Budget | Typical | 偏离 | 累计% |
    |------|------|--------|---------|------|-------|
    | osborn | 8min | 8min | 8min | 1.0x | 18% |
    | hegel | 12min | 8min | 8min | 1.5x | 45% |
    | bayes | 10min | 10min | 10min | 1.0x | 68% |
    | feynman | 15min | 10min | 10min | 1.5x | 102% 🔴 |
    | debono | 6min | 8min | 8min | 0.75x | 116% 🔴 |
    | **合计** | **51min** | **44min** | **44min** | **1.16x** | — |
    
    ## 相变信号汇总
    | Step | 信号 | 值 | 动作 |
    |------|------|-----|------|
    | hegel | terminal_status | converged | — |
    | bayes | refuted_count | 3 | 🔴 highlight |
    | feynman | veto_count | 2 | 🔴 highlight |
    | **相变簇** | **2 个 highlight** | — | **系统性偏差提示** |

    时间日志用于 liam-retro 分析和 typical 值校准(≥10 次后用实际中位数替换初始估计值)。预算列用于评估 problem_type → budget 映射的准确性,≥3 次同类型后校准预算值。相变信号汇总用于识别系统性认知偏差。

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 Taixu Pipeline AI skill do?

Pipeline(串联五转):osborn→hegel→bayes→feynman→debono 自动串联编排器。触发词: 一键五转, 跑五转, taixu-pipeline.

Why use Taixu Pipeline on TypingMind?

Because you install it once and use it with any model. Taixu Pipeline 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 Taixu Pipeline in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fainshare/Liam-Skills/tree/main/skills/taixu-pipeline. 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 Taixu Pipeline?

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 Taixu Pipeline?

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

Is the Taixu Pipeline 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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