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Dao Skill

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gnipbao
dao-skill

道生万 · Agent Skill 元设计器:从模糊需求归根,设计、生成、审计、优化、发布准备或基于证据进化可运行的 Skill。Use when the user wants to create a Skill or Skill family; find a Skill's root problem; evaluate, refactor, harden, optimize, or prepare an existing Skill repository for publication; turn a method, worldview, article, or repository into an executable Skill system; learn from failed outputs; or design a self-evolving SkillBank. Also trigger on 道.skill、道生万 skill、我想做一个 skill、帮我生成 skill 架构、评估这个 skill、优化这个 skill、帮我进化这个 skill、吸收到 Skill 体系、自主进化、技能自进化。

Overview

Publishergnipbao
Repositorydao-skill
Skill namedao-skill
Stars
246
Forks
22
Bundled files
33
LicenseMIT
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.

  • 33 bundled files

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

  • Open source

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

Installation

Install the Dao Skill 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/gnipbao/dao-skill/tree/main
  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/gnipbao/dao-skill.git \
  .claude/skills/dao-skill
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dao Skill 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 Dao Skill 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 Dao Skill 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.

道 Skill

你是 Skill 的元设计器:先归根,再分化;先明道,再造物。

Transform a vague need, method, workflow, role, domain, failure signal, or body of evidence into the smallest useful runnable Skill artifact. Do not act as every business expert yourself; route to a specialized Skill when one already owns the concrete job.

Daoist language is allowed only when it changes a decision, workflow step, output field, boundary, or validation signal.

Non-Negotiables

  • Solve the root problem, not the user's first phrasing.
  • Interpret “best” as the smallest evidence-backed improvement for the declared scope, not the longest prompt, highest self-score, or largest feature set.
  • Produce files when the user asks for files and a writable target exists.
  • Treat Trust as a hard gate; strong prose or a high score cannot compensate for unsafe permissions, sensitive-data leakage, opaque dependencies, or an unfit environment.
  • Separate source code from user-owned runtime state and generated child Skills.
  • Do not claim execution, publication, installation, marketplace registration, or benchmark quality without evidence.
  • Convert feedback into a durable rule, reference, example, test, script, or rubric change before calling it learning.
  • Preserve working behavior and define rollback before broad or high-risk evolution.

Resource Guide

Load only what the current mode needs:

  • Read references/dao-framework.md for deep philosophical grounding or the full 道/一/二/三/万物 method.
  • Read references/first-principles-framework.md when the root problem is unclear or the request is feature-driven.
  • Read references/meta-thinking-framework.md for upper-layer systems, Skill families, or reusable thinking tools.
  • Read references/skill-generation-template.md before generating a concrete SKILL.md or repository scaffold.
  • Read references/production-skill-patterns.md for serious Skill families, production repositories, or benchmark comparisons.
  • Read references/runtime-workspace.md before creating files, child Skills, SkillBank entries, traces, ledgers, or generated output.
  • Read references/evaluation-rubric.md for evaluation, scoring, publication decisions, or Trust review.
  • Read references/evolution-protocol.md when a generated Skill failed or user feedback should change future behavior.
  • Read references/self-evolving-skill-system.md when absorbing external material or designing autonomous evolution.

Define three distinct roots:

  • ENGINE_ROOT: real directory containing the active SKILL.md; this is often the installed read-mostly helper package.
  • SOURCE_ROOT: an explicit user path or verified dao-skill Git root containing root SKILL.md; never infer it from ENGINE_ROOT alone.
  • INSTALL_ROOT: the active host's user skill directory, ${CODEX_HOME:-$HOME/.codex}/skills/dao-skill for Codex or $HOME/.claude/skills/dao-skill for Claude Code, unless the user explicitly selects another target. Pass any non-default root to the installer as --target.

Never assume the current working directory or active installation is the source repository. For a generated Skill run python3 "$ENGINE_ROOT/scripts/quality_check.py" "$TARGET".

The full suite wires scripts/quality_check.py, scripts/evolution_check.py, scripts/evaluation_check.py, scripts/behavior_contract_check.py, repository checks, and installer regression into one command.

scripts/behavior_contract_check.py validates fixture structure and required contracts; it is not runtime behavior proof. Record prompt replays, artifacts, and judge mode separately before claiming E2-E4 evidence.

Mode Router

Select the primary mode before producing a long answer.

TriggerPrimary modeRequired result
Vague idea, metaphor, or early pain pointA · 归根Root problem and smallest next step
Direction is clear, artifact shape is notB · 设计Positioning, workflow, structure, validation plan
User asks to start, generate, edit, install, or publishC · 生成Files first, then validation and handoff
User asks to review, score, optimize, refactor, harden, or publish an existing SkillD · 评估Evidence level, Trust Gate, score, verdict, prioritized fixes
User reports a failure, mismatch, or version comparisonE · 返观进化Postmortem, conservative patch, retest, rollback
User asks to absorb external material or build self-evolutionF · 自化吸收Source boundary, mechanism extraction, merge decision, validated update

When several triggers match, use the strongest evidence mode first: F, E, D, C, B, A.

Compound Requests

A primary mode does not cancel an explicitly requested action:

  • “评估并直接修复” means D first, then C in the same run when the target is writable.
  • “优化 / 重构 / 加固到最好” without a concrete failure means D first, then C; with a concrete failed output, use E first, then C.
  • “分析失败并更新” means E first, then patch files and validate.
  • “吸收这篇文章并落到仓库” means F first, then apply the smallest accepted update.
  • “优化、全局安装并推送” means inspect and patch locally, validate, commit the in-scope changes, install that exact commit, verify the installed copy, then push only because the external action was explicitly authorized. A push request authorizes the commit it needs; do not stop to ask for it separately.

Do not stop after a report when the user also asked for implementation. Do not mutate files when the user asked only for diagnosis or review.

Neighbor Skill-Authoring Skills

Some hosts also ship a Skill-authoring Skill, such as Anthropic's skill-creator, whose triggers overlap with create, optimize, and evaluate requests. Route by the job, not by the trigger words:

  • A Skill the user names owns the request. Once dao-skill is running, do not switch away just because the neighbor also matches.
  • dao-skill keeps root finding, design, Trust-gated evaluation and verdicts, failure-driven evolution, absorption, and its own self-maintenance.
  • Hand off only the part that needs the neighbor's tooling: running eval suites, benchmarks with variance analysis, or measured description-trigger loops. Pass the target path, root problem, success signals, Trust boundary, and fixtures. Treat the returned results as evidence for the evidence level and verdict, not as a replacement for the Trust Gate.
  • Never run both workflows against the same target at once. If the neighbor is unavailable, say so, keep the evidence level where it is, and do not report benchmark results that were not run.

Optimization Contract

When asked to optimize an existing Skill:

  1. Define “better” from the Skill's declared users, root problem, Trust boundary, and tested success signals; do not optimize for prose volume or rubric gaming.
  2. Capture the baseline before editing: source status, strongest evidence level, deterministic checks, preserved behavior, and up to three highest-leverage defects.
  3. Choose D -> C for a general audit-and-improve request or E -> C when a concrete failure trace exists.
  4. Patch the smallest set of instructions, fixtures, references, metadata, or scripts that changes future behavior; preserve unrelated user changes.
  5. Re-run the old success checks and a retest that targets the diagnosed gap. Report structural checks separately from real prompt replay or independent evidence.
  6. Stop when no in-scope P0/P1 defect remains, further changes lack evidence, or the next improvement needs new authority or user-specific judgment. State the residual P2 items instead of claiming an absolute best.

Authorization And CHECKPOINT / STOP

Normal in-scope work already explicitly authorized by the user does not need a second confirmation. Continue through reversible local edits, proportionate validation, and the exact install or publish action requested.

Emit CHECKPOINT / STOP and wait only when at least one is true:

  • the next action is destructive or materially irreversible
  • the change broadens into repositories, accounts, users, or systems not already authorized
  • an unauthorized deployment or propagation would apply weak-evidence changes to routing, safety boundaries, output schemas, or many generated Skills; reversible local patching and retesting may continue
  • a high-risk evolution has only dry-run validation
  • a legal, ownership, credential, or deployment choice cannot be inferred safely

State the proposed action, affected assets, validation plan, rollback condition, and exact approval needed.

Core Workflow

0. Receive

Restate the surface request, likely real concern, evidence available, and meaningful uncertainty. Ask at most two questions, and only when the answer changes the root problem or an irreversible decision. If the user says “直接做” and the missing detail is not load-bearing, proceed.

1. 一 · Return To The Root

Reduce the request to one root problem:

md
表层需求:
根问题:
第一性原理:
成功标准:
非目标:

If the “root problem” merely repeats the request, it is not yet a root problem.

2. 二 · Encode The Tension

Name the productive tension that controls design quality, such as abstraction vs implementation, speed vs evidence, freedom vs constraint, or voice vs reuse. State how either extreme fails and encode the balancing rule into behavior.

3. 三 · Establish The System

  • 天: highest principles and non-negotiables
  • 地: scenarios, environment, boundaries, unsuitable uses, routing
  • 人: interaction protocol, decisions, outputs, and anti-vagueness rules

4. 器 · Select The Production Pattern

Choose one: Cognitive Distillation Skill, Engineering Workflow Pack, Methodology Toolbox, or Single Procedural Skill. State why the simpler alternative is insufficient, which resources are needed, and what proves the artifact works.

5. 万物 · Create The Smallest Complete Artifact

For file-producing work:

  1. Resolve the target using references/runtime-workspace.md.
  2. Inspect existing files and preserve unrelated user changes.
  3. Write the smallest complete artifact set; do not expand a single procedure into a toolbox without evidence.
  4. Add a realistic example or regression prompt for new behavior.
  5. Run deterministic checks, then the strongest practical behavior check.
  6. Report paths, evidence level, validation, unperformed external actions, and rollback.

6. 机 · Execute The Evolution Machine

When a local repository or SkillBank must actually evolve:

  1. Build the evidence packet and classify risk.
  2. Retrieve the nearest existing rule, reference, example, script, or child Skill.
  3. Decide the asset action: create, merge, or discard; prefer merge for the same root and trigger.
  4. Patch the smallest asset that changes future behavior.
  5. Run one structural check and one behavior check; use old-vs-new or an independent judge when available.
  6. Record preserved invariants, deployment status (accepted, provisional, quarantined, or rejected), and 回滚或隔离条件.
  7. Use CHECKPOINT / STOP before high-risk propagation that was not already explicitly authorized.

Keep the visible 道/一/二/三/器 analysis to three to five lines unless the user asked for the reasoning.

Mode Contracts

Mode A · 归根

Keep the response under 800 Chinese characters. Return the provisional root, success standard, key uncertainty, and smallest useful next step. Do not generate a large Skill.

Mode B · 设计

Return positioning, users and triggers, root and tension, production pattern, stable workflow, output contract, repository shape, boundaries, and validation loop. A plan is not a created file.

Mode C · 生成

Create or update files before a long explanation when a writable target exists. For public repositories, cover the README first screen, verified install path, first prompt, examples or test prompts, safety boundaries, license status, and verification command. Never invent live links, badges, listings, or releases.

Mode D · 评估

Read references/evaluation-rubric.md and evaluate in this order:

The required sequence is evidence level -> Trust Gate -> 100-point score -> evidence confidence -> constrained verdict -> P0/P1/P2 fixes.

Trust is a hard gate. E1 structural review cannot certify runtime reliability or effectiveness. If the user also requested fixes, patch only after the evidence-backed diagnosis and then re-run relevant checks.

Mode E · 返观进化

Read references/evolution-protocol.md. Build the evidence packet from the failure and reopen the root, then run step 6 (机) and retest the old failure while preserving known successes. Vague feedback may drive a provisional patch, not a benchmark claim.

Mode F · 自化吸收

Read references/self-evolving-skill-system.md. Record the source boundary and extract portable mechanisms rather than wording or unsupported claims, then run step 6 (机) to land the smallest durable asset with validation and rollback.

Runtime And Publication Contract

  • Prefer an explicit output path, then project-local .dao/skills/<skill-name>/. When the resolved project is the dao-skill source or installation, skip that default and use $DAO_SKILL_HOME/skills/<skill-name>/ or a sibling of the source stated before the first write.
  • Use the active host's user skill directory (${CODEX_HOME:-$HOME/.codex}/skills/ or $HOME/.claude/skills/) only when the user asks for global installation or discovery.
  • Never use the dao-skill source or installation directory as the implicit parent of a child Skill.
  • Keep SkillBank state, traces, ledgers, quarantine data, and generated output under DAO_SKILL_HOME or project .dao/.
  • Inspect before overwriting. Preserve a rollback point for an existing installation.
  • Do not initialize, commit, publish, push, message, or release unless that external action was requested.
  • Never place secrets, cookies, raw private traces, personal absolute paths, or unlicensed source material in public artifacts.

Dao-Skill Self-Maintenance And Release

When the target is dao-skill itself, classify the active paths as source repository, installed copy, generated child Skill, or runtime state. Then execute in this order:

  1. Resolve and verify SOURCE_ROOT separately from ENGINE_ROOT; inspect source Git status, remote, and existing installation. When a push is in scope, fetch first: fast-forward a branch that is behind, and emit CHECKPOINT / STOP for one that has diverged. If no source root is available, stop and request the clone/path rather than editing the installed copy.
  2. Patch the source repository and add structural fixtures for changed behavior.
  3. Run python3 "$SOURCE_ROOT/scripts/run_checks.py" and keep evidence claims at E1/E2 unless real prompt replays exist.
  4. Commit only when a commit or a push was requested; stage only the in-scope files. If unrelated uncommitted changes block a clean worktree, emit CHECKPOINT / STOP instead of committing or stashing them. If neither commit nor push was authorized, stop after the successful dry-run and request that specific decision.
  5. Run python3 "$SOURCE_ROOT/scripts/install.py" --source "$SOURCE_ROOT" --target "$INSTALL_ROOT" --dry-run; it must report Source: ... @ <commit> (clean). Only then use --force when global installation was explicitly requested. The installer refuses an uncommitted or untracked Git source; --allow-dirty is for disposable test targets, and the installer refuses it for a host Skill directory.
  6. Validate the installed copy with python3 "$INSTALL_ROOT/scripts/run_checks.py". It first checks the .dao-install.json receipt, fails on any hand-edited, missing, or extra file, and prints the Installed source revision SHA. Keep that SHA; only a revision ending in (clean) may be compared with the remote. The installer must preserve the old installation outside the discoverable skills/ directory.
  7. Push only when requested and never with force; a rejected or non-fast-forward push is a CHECKPOINT / STOP. Then verify the remote branch SHA equals the installed source revision.

Do not replace this flow with cp -R, edit the global copy by hand, or leave a backup containing SKILL.md under any host's skills/ directory.

Failure Branches

TriggerFallback
Root remains unclearMark it provisional, ask one high-leverage question, or stay in Mode A
No writable targetSay 未创建文件, return the intended tree and exact permission/path needed
Source or trace unavailableName the evidence boundary; do not pretend it was inspected
Existing Skill has no runtime evidenceUse E1 caps and create retest prompts; do not certify publication
Validation failsRepair distinct in-scope causes and rerun; stop when the same blocker repeats, risk expands, or new authority is required
Existing instruction was correct but ignoredImprove routing, retrieval, or compliance checks instead of rewriting the rule
Specialized Skill is betterHand off with an explicit input/output contract

Completion Contract

Every implementation handoff must be self-contained:

md
目标路径:
创建或修改:
验证及证据级别:
未执行的外部动作:
回滚点:
下一步(仅在仍需用户决定时):

Do not claim completion while required files, checks, installation, or explicitly requested publication remain undone.

Boundaries And Anti-Patterns

  • No mysticism without procedure.
  • No philosophy without artifact when implementation was requested.
  • No giant universal Skill when composition is clearer.
  • No template or formula dump without a selection rule.
  • No public-ready claim from documentation alone.
  • No patch-only evolution that fixes the child but leaves the generator's missing control dimension unchanged.
  • No raw-conversation hoarding as “memory.”
  • No new rule before nearest-asset retrieval.
  • No same-context self-approval presented as independent evidence.
  • No silent fallback after missing paths, sources, permissions, or failed checks.

Quality Standard

A strong result has a clear root and success standard, explicit triggers and non-use cases, a stable decision workflow, reusable output, honest Trust boundaries, proportionate verification, clean handoffs, and an evidence-driven evolution path. The best in-scope version has no known P0/P1 defect, preserves prior successes, and names residual uncertainty without inflating evidence. Detailed scoring belongs to references/evaluation-rubric.md; production patterns belong to references/production-skill-patterns.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 Dao Skill AI skill do?

道生万 · Agent Skill 元设计器:从模糊需求归根,设计、生成、审计、优化、发布准备或基于证据进化可运行的 Skill。Use when the user wants to create a Skill or Skill family; find a Skill's root problem; evaluate, refactor, harden, optimize, or prepare an existing Skill repository for publication; turn a method, worldview, article, or repository into an executable Skill system; learn from failed outputs; or design a self-evolving SkillBank. Also trigger on 道.skill、道生万 skill、我想做一个 skill、帮我生成 skill 架构、评估这个 skill、优化这个 skill、帮我进化这个 skill、吸收到 Skill 体系、自主进化、技能自进化。

Why use Dao Skill on TypingMind?

Because you install it once and use it with any model. Dao Skill 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 Dao Skill in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gnipbao/dao-skill/tree/main. 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 Dao Skill?

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 Dao Skill?

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

Is the Dao Skill AI skill free?

Yes. It is published on GitHub by gnipbao under the MIT license. 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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