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Hai Goal

Community
hylarucoder
hai-goal

Creates or rewrites a goal document for ambiguous, multi-phase, high-risk, or explicitly planned execution: verifiable outcome, boundary, current state, phases, proof-bearing todos, dry-run, and Go/No-Go. Use when the user asks for a goal, plan, phases, todos, or to re-anchor an existing plan(定目标、拆阶段、执行计划). Do not interpose it on a small implementation whose outcome and verification are clear; hand ready work to normal execution, using hai-tdd only for suitable behavior changes.

Overview

Publisherhylarucoder
Repositoryhai-stack
Skill namehai-goal
Stars
284
Forks
15
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

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

Installation

Install the Hai Goal 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.
  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/hylarucoder/hai-stack.git /tmp/hai-stack
mkdir -p .claude/skills
cp -r /tmp/hai-stack/skills/hai-goal .claude/skills/hai-goal
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Hai Goal 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 Hai Goal 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 Hai Goal 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.

Hai Goal

For Chinese readers, see SKILL.zh_CN.md. The English SKILL.md is the execution source of truth.

Overview

Use this skill when execution is ambiguous, spans meaningful phases, carries material risk, or the user explicitly asks for a plan. It turns the work into a verifiable target, boundary, route, and proof. It also rewrites an existing plan around a newly stated target. A small task with an obvious outcome and verification should proceed directly without ceremony.

Core Principle

Write the goal before you go.

When this skill's trigger conditions apply, do not start implementation while hoping execution will clarify the target. The "run and watch" failure mode looks like:

  • Start implementation before the target is stable.
  • Discover missing decisions halfway through.
  • Patch local problems instead of shaping the whole route.
  • Let phase boundaries emerge accidentally.
  • Forget to define what counts as done.
  • Treat validation as an afterthought.

Complex or unclear work should first become a goal document with boundaries, phases, dependencies, and proof. When a plan already exists, the target takes priority over the old plan shape.

The Goal Document

A goal document is a runnable plan for execution. It answers:

  • Target: what are we trying to make true?
  • Boundary: what is included, excluded, and intentionally deferred?
  • Current state: what exists now, with known constraints and risks?
  • Route: what phases get us there?
  • Rules: what constraints or working agreements govern each phase?
  • Todos: what concrete tasks belong in each phase?
  • Progress management: for multi-phase execution, what checklist should stay current?
  • Verification: how do we prove each phase, and the whole goal, is done?
  • Stop conditions: when should we pause, ask, or revise the plan?

The exact output shape — including the conditional rewrite sections — lives in references/output-template.md. Read it before finalizing; do not re-derive the field list here.

A fresh goal document and a plan rewrite use the same template; the rewrite case additionally fills the Plan Rewrite Notes and Drift Diagnosis sections (see step 4).

Workflow

This single sequence covers every case — making a goal from vague intent, crafting phases for a known goal, and rewriting an existing plan around a target. Run step 4 only when an existing plan is provided.

  1. Freeze the intent. What does the user want to start? What outcome would make the work worth doing? What must not be broken? What is still unknown?

  2. Make the goal verifiable. Rewrite vague activity into a target outcome. Define the boundary (included, excluded, deferred). Choose the strongest available evidence source — test, metric, review, artifact, command, behavior, user signal, or trace — and a clear pass/fail rule with a confidence note, and name the judgment owner — who or what is authorized to declare it done. Quantify when useful, but do not invent fake metrics. If one intent contains multiple goals, split them.

  3. Convert the goal into a goal document. Record assumptions and unresolved decisions, identify affected surfaces (docs, code, schema, API, UI, tests, workflows, links), and define what "ready to execute" means.

  4. If rewriting an existing plan, re-anchor it to the target. Extract the plan's phases, todos, assumptions, dependencies, and highlighted focus points. Diagnose drift — goal drift, phase drift, validation drift, compatibility drift, cleanup drift — and decide per item what to keep, reorder, merge, or remove. Move high-risk, high-uncertainty, or high-leverage work earlier unless dependencies forbid it. Keep old wording only when it remains accurate under the new target.

  5. Split into phases. Each phase should produce a reviewable state. Split by learning, risk reduction, user-visible value, system boundary, dependency order, vertical slice, decision boundary, or validation boundary — never by department, file type, arbitrary chronology, or topic heading. Avoid phases that only say "implement X" without proof.

  6. Define phase rules. For each phase, state what is allowed, what is explicitly not allowed yet, what compatibility or migration rule applies, what documentation update is required, and what validation must pass before moving on. Concrete patterns to draw from:

    • Do not touch implementation until the target doc is updated.
    • This phase may rename internal callers directly; no compatibility shim unless a public contract exists.
    • This phase may only change docs and tests, not production code.
    • This phase exits only when the command/test/check passes.
    • Stop if a todo requires a product decision not present in the goal document.
  7. Create todos with action + surface + proof (see template). Make them concrete enough to execute without guessing, mark dependencies and blockers that affect order, and keep unrelated cleanup out unless the phase goal requires it.

  8. Add progress management when execution will continue across phases or sessions. Use one phase checklist with nested proof-bearing todos. Mark a phase complete only after its exit proof passes, and record any reprioritization instead of silently following stale order. Omit this machinery for a short one-session plan.

  9. Dry-run the route. Walk the phases in order without executing them; look for missing prerequisites, circular dependencies, vague todos, and unverified assumptions; revise the goal document before any real work. This step is what separates a goal document from generic planning.

  10. Decide whether to Go. If the route is coherent, execution can start. If it carries unresolved decisions, resolve them with the right skill first (see below) and re-run the dry-run.

Output

Return a goal document, not generic advice. Fill references/output-template.md and omit conditional sections that do not apply: rewrite notes for a fresh goal, and progress tracking for a short plan. Do not duplicate the template inside the main instructions.

Use a different skill when

  • hai-idea — it is unclear the work is worth doing at all. Validate or kill the idea before spending effort on a goal document.
  • hai-prd — the route has unresolved product or scope decisions. Settle the requirement, then return to write the goal.
  • hai-architecture — the route has unresolved structural or boundary decisions. Settle the design, then return.
  • Execution — once a phase is ready and implementation is requested, continue through the complete change and proportionate verification. Use hai-tdd for testable behavior; use compiler checks, existing tests, and focused runtime checks for structural/config/style work. Do not stop after producing a plan when the user already asked to implement.
  • code-review-and-quality — inspect completed changes for defects.
  • write-technical-acceptance-report — prove the outcome against acceptance; use compact mode for low-risk work. Reuse existing current evidence instead of repeating checks without cause.
  • hai-rewrite-doc — the drifted artifact is a general document, not a plan or goal document. This skill's rewrite mode owns plans; anchor-first rewriting of everything else lives there.

What this skill is NOT

  • Not generic planning detached from action — a "plan" here is a runnable goal document, and it must be executable.
  • Not a project management calendar. Dates and owners are optional unless the user asks for them.

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 Hai Goal AI skill do?

Creates or rewrites a goal document for ambiguous, multi-phase, high-risk, or explicitly planned execution: verifiable outcome, boundary, current state, phases, proof-bearing todos, dry-run, and Go/No-Go. Use when the user asks for a goal, plan, phases, todos, or to re-anchor an existing plan(定目标、拆阶段、执行计划). Do not interpose it on a small implementation whose outcome and verification are clear; hand ready work to normal execution, using hai-tdd only for suitable behavior changes.

Why use Hai Goal on TypingMind?

Because you install it once and use it with any model. Hai Goal 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 Hai Goal in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/hylarucoder/hai-stack/tree/main/skills/hai-goal. 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 Hai Goal?

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 Hai Goal?

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

Is the Hai Goal AI skill free?

It is published on GitHub by hylarucoder. 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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