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Intent

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Yeachan-Heo
intent

Shipyard's internal requirements intake for non-engineer contributors (support/ops) — turn a pasted chat log or verbal problem report into a five-section intent.md through numbered batch questioning with a completion gate, walk it through tracker review with harbor's four records, and hand the accepted intent to /launch as its mission brief. Produces decisions, not code.

Overview

PublisherYeachan-Heo
Repositoryoh-my-claudecode
Skill nameintent
Stars
39.8K
Forks
3.6K
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by Yeachan-Heo on GitHub. Read the source before you install it.

Installation

Install the Intent 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/Yeachan-Heo/oh-my-claudecode/tree/main/skills/intent
  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/Yeachan-Heo/oh-my-claudecode.git /tmp/oh-my-claudecode
mkdir -p .claude/skills
cp -r /tmp/oh-my-claudecode/skills/intent .claude/skills/intent
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Intent 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 Intent 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 Intent 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.

Intent

Intent is the shipyard's internal intake: support/ops staff see product problems first but cannot write engineering specs. The intent skill converts their conversation into a goal-level YAML+markdown artifact that an owner reviews and signs, so engineering receives a fog-free mission. It stands on the verifiability boundary — the agent asks, records, and drafts; humans submit, verify, and sign.

Design provenance: every decision here was pinned by a wayfinder map (7 tickets) and walked end-to-end in a demo repository before implementation.

Role contract

  • The contributor (e.g. support staff) pastes raw feedback or a chat log. The agent never invents facts; unknowns become explicit 待确认 items.
  • Decisions are the humans': the contributor confirms wording, the supervisor submits, the product owner verifies facts and signs accept/reject. The agent never signs, never rejects on anyone's behalf, and never stands in for a reviewer.
  • The stop gate is a completion gate, not an ambiguity score — no math is borrowed from deep-interview; only its Goal and Constraint clarity judgments are reused as quality bars.

The conversation (numbered batch questioning)

  1. Opening inventory: restate the problem in one sentence, then show the five-section inventory (known / missing per section).
  2. Numbered batch questioning: list every gap as numbered questions in one batch. The contributor answers by number (1 是; 2 不确定). Unknown answers are recorded as 待确认 and pushed into open questions — never pressed until guessed.
  3. Re-inventory: apply the answers; if gaps remain, emit the next numbered batch. One round = one batch.
  4. Stop gate (all five sections must pass):
    • 问题: observable phenomena, no solution embedded
    • 目标: one sentence, no qualifiers, names entities and relations (deep-interview Goal Clarity bar)
    • 用户和系统: real names for users and systems; unknowns written as 待确认 and counted as open questions
    • 约束: boundaries, limits, and non-goals explicit (deep-interview Constraint Clarity bar)
    • 未决问题: list non-empty, or an explicit 无
  5. Soft limits: soft prompt at round 6 ("draft with current clarity?"), hard cap at round 12. An early exit is never blocked — thin sections surface as longer open questions, and review is the gate that rejects them.
  6. Draft: the agent drafts docs/intents/<slug>/intent.md (template below) at status: draft; the contributor confirms wording before any submission.

The intent.md template

markdown
---
intent: <slug>
title: <one-line title>
author: <name (role)>
date: <YYYY-MM-DD>
status: draft | in-review | accepted | rejected
round: <tracker review round>
---

## 问题
<observable phenomena, no solution>

## 目标
one sentence, no qualifiers

## 用户和系统
<who the users are, which systems, real names; unknowns as 待确认>

## 约束
<boundaries, non-goals; cite Rules pillar files when a constraint comes from them>

## 未决问题
- [阻塞|非阻塞] <question>   ← agent suggests, product owner finalizes

Grading open questions

The test for 阻塞: "can the spec still be approved without answering it?" No → blocking; yes → non-blocking (tracked, may ride into development). The drafting agent suggests grades; the product owner finalizes at spec approval.

Review handoff (harbor four records)

Review runs on the repo's tracker — the tracker is the only record source; intent.md frontmatter mirrors status only. One record file per Intent, rounds accumulate inside it. Required fields per record: signer, date, verdict, link to intent.md, round, rejection reason (mandatory on reject).

  • proposal = supervisor submits the Intent (intent.md → status: in-review)
  • verification = product owner checks facts against repo evidence before deciding; the agent may draft the fact-check table, but the signature belongs to the reviewer — no repo evidence to check? Then verification records why none was needed
  • decision = accept or reject; reject requires a reason, which is the key input to resubmission
  • execution result = spec generation completed for this Intent

Rejected → revise → resubmit in the same file under the next round number. History stays traceable.

Handoff to launch

An accepted intent (status: accepted) is a valid mission brief. Launch's spec synthesis then follows the four-step contract:

  1. read docs/intents/<slug>/intent.md (must be accepted, latest round)
  2. combine with the existing codebase
  3. follow the Rules pillar (CLAUDE.md + docs/standards/ + docs/business/)
  4. list every doubt and rule conflict — graded into the spec's pending-confirmation section

Intent open questions carry into the spec verbatim, keeping their pending status.

Amendment

New information after acceptance (typically surfaced by the first spec) is an amendment: anyone may initiate, the submitter signs, and it always re-enters full review as a new round — no minor-change exemption. An intent change marks the generated spec stale; the spec is regenerated from the new intent and re-approved. No separate amendment record type: rounds carry it.

Conflict sedimentation

Conflict conclusions in the spec's pending section follow precedent-based routing: case-local answers stay in the spec; conclusions that will bind later Intents sediment into the Rules pillar (docs/business/ for business rules, docs/standards/ for behavior rules); hard-to-reverse technical tradeoffs become ADRs. Never double-write; never defer archiving. The product owner decides whether a conclusion is precedent-setting; the tech lead advises the destination.

Frequently asked questions

What does the Intent AI skill do?

Shipyard's internal requirements intake for non-engineer contributors (support/ops) — turn a pasted chat log or verbal problem report into a five-section intent.md through numbered batch questioning with a completion gate, walk it through tracker review with harbor's four records, and hand the accepted intent to /launch as its mission brief. Produces decisions, not code.

Why use Intent on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Yeachan-Heo/oh-my-claudecode/tree/main/skills/intent. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Intent?

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 Intent?

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

Is the Intent AI skill free?

Yes. It is published on GitHub by Yeachan-Heo 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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