do-plan — AI计划工具 do-plan, community, AI计划工具, ide skills, 结构化特性计划, 问题定义, 解决方案概述, 边界定义, GitHub问题跟踪, 计划审查, Claude Code

v1.0.0

关于此技能

非常适合需要结构化功能规划和 GitHub 问题跟踪的开发代理。 AI计划工具是一种创建结构化特性计划的工具

功能特性

创建结构化特性计划
问题定义和解决方案概述
边界定义和GitHub问题跟踪
计划审查和开放性问题
支持Shape Up方法论

# 核心主题

tomcounsell tomcounsell
[7]
[4]
更新于: 3/11/2026

Killer-Skills Review

Decision support comes first. Repository text comes second.

Reference-Only Page Review Score: 9/11

This page remains useful for operators, but Killer-Skills treats it as reference material instead of a primary organic landing page.

Original recommendation layer Concrete use-case guidance Explicit limitations and caution Quality floor passed for review
Review Score
9/11
Quality Score
60
Canonical Locale
en
Detected Body Locale
en

非常适合需要结构化功能规划和 GitHub 问题跟踪的开发代理。 AI计划工具是一种创建结构化特性计划的工具

核心价值

赋予代理生成使用 Shape Up 方法论的全面内容分析计划的能力,创建 Markdown 文档并链接 GitHub 问题,以实现无缝的项目管理和跟踪,利用 GitHub API 和 Markdown 格式。

适用 Agent 类型

非常适合需要结构化功能规划和 GitHub 问题跟踪的开发代理。

赋予的主要能力 · do-plan

自动为开发项目生成功能计划
创建 Markdown 格式的结构化计划文档
链接 GitHub 问题以进行跟踪和项目管理

! 使用限制与门槛

  • 需要 GitHub API 访问以进行问题跟踪
  • 仅限 Shape Up 方法论进行功能规划
  • 计划文档需要 Markdown 格式

Why this page is reference-only

  • - Current locale does not satisfy the locale-governance contract.

Source Boundary

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

评审后的下一步

先决定动作,再继续看上游仓库材料

Killer-Skills 的主价值不应该停在“帮你打开仓库说明”,而是先帮你判断这项技能是否值得安装、是否应该回到可信集合复核,以及是否已经进入工作流落地阶段。

实验室 Demo

Browser Sandbox Environment

⚡️ Ready to unleash?

Experience this Agent in a zero-setup browser environment powered by WebContainers. No installation required.

Boot Container Sandbox

常见问题与安装步骤

以下问题与步骤与页面结构化数据保持一致,便于搜索引擎理解页面内容。

? FAQ

do-plan 是什么?

非常适合需要结构化功能规划和 GitHub 问题跟踪的开发代理。 AI计划工具是一种创建结构化特性计划的工具

如何安装 do-plan?

运行命令:npx killer-skills add tomcounsell/ai/do-plan。支持 Cursor、Windsurf、VS Code、Claude Code 等 19+ IDE/Agent。

do-plan 适用于哪些场景?

典型场景包括:自动为开发项目生成功能计划、创建 Markdown 格式的结构化计划文档、链接 GitHub 问题以进行跟踪和项目管理。

do-plan 支持哪些 IDE 或 Agent?

该技能兼容 Cursor, Windsurf, VS Code, Trae, Claude Code, OpenClaw, Aider, Codex, OpenCode, Goose, Cline, Roo Code, Kiro, Augment Code, Continue, GitHub Copilot, Sourcegraph Cody, and Amazon Q Developer。可使用 Killer-Skills CLI 一条命令通用安装。

do-plan 有哪些限制?

需要 GitHub API 访问以进行问题跟踪;仅限 Shape Up 方法论进行功能规划;计划文档需要 Markdown 格式。

安装步骤

  1. 1. 打开终端

    在你的项目目录中打开终端或命令行。

  2. 2. 执行安装命令

    运行:npx killer-skills add tomcounsell/ai/do-plan。CLI 会自动识别 IDE 或 AI Agent 并完成配置。

  3. 3. 开始使用技能

    do-plan 已启用,可立即在当前项目中调用。

! 参考页模式

此页面仍可作为安装与查阅参考,但 Killer-Skills 不再把它视为主要可索引落地页。请优先阅读上方评审结论,再决定是否继续查看上游仓库说明。

Upstream Repository Material

The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.

Upstream Source

do-plan

使用AI计划工具创建结构化特性计划,包括问题定义、解决方案概述和边界定义

SKILL.md
Readonly
Upstream Repository Material
The section below is imported from the upstream repository and should be treated as secondary evidence. Use the Killer-Skills review above as the primary layer for fit, risk, and installation decisions.
Supporting Evidence

Make a Plan (Shape Up Methodology)

Creates structured feature plans in docs/plans/ following Shape Up principles: narrow the problem, set appetite, rough out the solution, identify rabbit holes, and define boundaries.

What this skill does

  1. Takes a vague or specific request and narrows it into a concrete plan
  2. Writes a structured plan document in docs/plans/{slug}.md
  3. Creates or links a GitHub issue for tracking
  4. Sends the plan for review with open questions

When to load sub-files

Sub-fileLoad when...
PLAN_TEMPLATE.mdWriting the plan document (copy the template into docs/plans/{slug}.md)
SCOPING.mdThe request is vague, a grab-bag, or needs narrowing before planning
EXAMPLES.mdDeciding how to respond to a user request (vague vs. grab-bag vs. good)

When to Use

  • Planning a new feature
  • Updating an existing plan
  • User says "make a plan", "plan this out", "flesh out the idea"
  • Scoping unclear or large requests
  • Before starting significant implementation work

Session Progress Tracking

Extract the session ID from the conversation context. The bridge injects SESSION_ID: {id} into enriched messages. Look for this pattern and store it:

bash
1# Extract SESSION_ID from context 2# Look for a line like "SESSION_ID: abc123" in the message you received 3# Store in variable: SESSION_ID="abc123" 4 5# Mark PLAN stage as in_progress at the start 6python -m tools.session_progress --session-id "$SESSION_ID" --stage PLAN --status in_progress 2>/dev/null || true

After the plan is committed and tracking issue is linked:

bash
1# Mark PLAN stage complete and set links 2python -m tools.session_progress --session-id "$SESSION_ID" --stage PLAN --status completed --issue-url "$ISSUE_URL" --plan-url "$PLAN_URL" 2>/dev/null || true

Where:

  • $ISSUE_URL is the full GitHub issue URL (e.g., https://github.com/owner/repo/issues/42)
  • $PLAN_URL is the full plan document URL (e.g., https://github.com/owner/repo/blob/main/docs/plans/{slug}.md)

Quick Start Workflow

Phase 1: Flesh Out at High Level

  1. Understand the request - What's being asked?

  2. Narrow the problem - Challenge vague requests (see SCOPING.md if needed)

  3. Blast radius analysis - If the change involves code modifications, run the code impact finder:

    bash
    1.venv/bin/python -m tools.code_impact_finder "PROBLEM_STATEMENT_HERE"

    Use results to inform plan sections:

    • impact_type="modify" -> Solution section
    • impact_type="dependency" -> Risks section
    • impact_type="test" -> Success Criteria section
    • impact_type="config" -> Solution section
    • impact_type="docs" -> Documentation section
    • Tangentially coupled files (< 0.5 relevance) -> Rabbit Holes section Skip if the change is purely documentation or process-related.
  4. Prior art search - Search closed issues and merged PRs for related work. This prevents proposing solutions that have already been tried (and failed) or re-solving problems that already have working implementations.

    bash
    1# Search closed issues for related keywords 2gh issue list --state closed --search "KEYWORDS_HERE" --limit 10 --json number,title,closedAt,url 3# Search merged PRs for related work 4gh pr list --state merged --search "KEYWORDS_HERE" --limit 10 --json number,title,mergedAt,url

    Use results to fill the Prior Art section in the plan. If multiple prior attempts addressed the same problem, also fill the Why Previous Fixes Failed section. Skip if: Small appetite AND greenfield work (no existing code being modified).

4.5. xfail test search - For bug fixes, search the test suite for xfail markers related to the bug. These represent tests that document the bug but are marked as expected failures.

bash
1# Search for xfail markers in tests (both decorator and runtime forms) 2grep -rn 'pytest.mark.xfail\|pytest.xfail(' tests/ --include="*.py" | head -20

For each xfail found that relates to the bug being fixed:

  • Add a task to the plan's Step by Step Tasks: "Convert TC{N} xfail to hard assertion"
  • Document the test location in the Success Criteria section
  • When the fix lands, the test should pass and the xfail marker must be removed IMPORTANT: Pay special attention to runtime pytest.xfail() calls inside test bodies. Unlike @pytest.mark.xfail decorators, runtime xfails short-circuit the test before reaching assertions — so they silently pass even after the bug is fixed. These are invisible to pytest's XPASS detection and MUST be explicitly listed as conversion targets in the plan. Skip if: Not a bug fix, or no xfail tests found related to this bug.
  1. Data flow trace - For changes involving multi-component interactions, trace the data flow end-to-end through the system. Start from the entry point (user action, API call, event trigger) and follow through each component, transformation, and storage layer.

    bash
    1# Read the entry point file 2# Follow imports and function calls through the chain 3# Document each transformation and handoff between components

    Use results to fill the Data Flow section in the plan. This is critical for changes that span multiple modules -- it prevents fixes applied at the wrong layer. Skip if: Change is isolated to a single file/function, or is purely documentation/config.

  2. Failure analysis - If the prior art search (step 4) found previous attempts to fix the same problem, analyze why each attempt failed or was incomplete. Look for patterns:

    • Was the root cause correctly identified?
    • Was the fix applied at the right architectural layer?
    • Did it address a symptom instead of the underlying cause?
    • Did it introduce new problems while fixing the original? Use results to fill the Why Previous Fixes Failed section (conditional -- only include in the plan if prior failed fixes exist). Skip if: No prior fixes found, or this is greenfield work.
  3. Set appetite - Small / Medium / Large (see SCOPING.md for sizing guidance)

  4. Rough out solution - Key components and flow, stay abstract

  5. Race condition analysis - If the solution involves async operations, shared mutable state, or cross-process data flows, identify timing hazards. For each: specify what data/state must be established before dependent operations read it, and how the implementation prevents races. Skip if the change is purely synchronous and single-threaded.

Phase 2: Write Initial Plan

Classification is mandatory - every plan MUST include a type: field (bug, feature, or chore).

Auto-Classification: When a message arrives via Telegram, the bridge auto-classifies it. Check if classification_type is available from the session context. If available, use it as the default type: value. The user can always override.

Create docs/plans/{slug}.md using the template from PLAN_TEMPLATE.md.

After writing the plan, push it to main first, then link it to an existing issue OR create a new one.

bash
1# Resolve repo identity from git 2REPO=$(gh repo view --json nameWithOwner -q .nameWithOwner) 3 4# Push plan to main before linking 5git add docs/plans/{slug}.md && git commit -m "Plan: {Feature Name}" && git push

Check for existing issue first! If the plan was created in response to an existing GitHub issue (e.g., "make a plan for issue #42"), do NOT create a new issue. Instead:

  1. Add the "plan" label and prepend the plan link to the issue body
  2. Update the plan frontmatter tracking: field with the existing issue URL
bash
1EXISTING_ISSUE=42 2gh issue edit $EXISTING_ISSUE --add-label "plan" 3EXISTING_BODY=$(gh issue view $EXISTING_ISSUE --json body -q .body) 4PLAN_LINK="https://github.com/${REPO}/blob/main/docs/plans/{slug}.md" 5gh issue edit $EXISTING_ISSUE --body "**Plan:** ${PLAN_LINK} 6 7${EXISTING_BODY}"

Only create a NEW issue if the plan was initiated from scratch (not from an existing issue).

Before creating, check config/projects.json for the current project:

  • If notion key exists -> create a Notion task (use Notion MCP tools)
  • If only github key exists -> create a GitHub issue (use gh CLI)
  • If neither -> skip tracking, just use the plan doc

Extract type: from plan frontmatter before creating:

bash
1TYPE=$(grep '^type:' docs/plans/{slug}.md | sed 's/type: *//' | tr -d ' ') 2if [ -z "$TYPE" ]; then 3 echo "ERROR: Plan must have a 'type:' field in frontmatter (bug, feature, or chore)" 4 exit 1 5fi 6 7gh issue create \ 8 --repo ${REPO} \ 9 --title "[Plan] {Feature Name}" \ 10 --label "plan" \ 11 --label "$TYPE" \ 12 --body "$(cat <<EOF 13**Plan:** https://github.com/${REPO}/blob/main/docs/plans/{slug}.md 14 15**Type:** {type} | **Appetite:** {appetite} | **Status:** Planning 16 17--- 18This issue is for tracking and discussion. The plan document is the source of truth. 19EOF 20)"

For Notion tasks, use MCP tools to create a page with Title, Status, Type, and link to the plan document.

After linking or creating: update the plan's tracking: field and commit.

Phase 2.7: Sync Issue Comments into Plan

Before finalizing, check the tracking issue for comments that contain feedback, scope changes, or new context that should be incorporated into the plan.

bash
1# Extract issue number from plan frontmatter tracking URL 2ISSUE_NUM=$(grep '^tracking:' docs/plans/{slug}.md | grep -oP '/issues/\K\d+') 3 4if [ -n "$ISSUE_NUM" ]; then 5 # Get all comments with IDs, sorted chronologically 6 gh api repos/{owner}/{repo}/issues/${ISSUE_NUM}/comments \ 7 --jq '.[] | {id: .id, author: .user.login, created: .created_at, body: .body}' 2>/dev/null 8 9 # Get the latest comment ID 10 LATEST_COMMENT_ID=$(gh api repos/{owner}/{repo}/issues/${ISSUE_NUM}/comments \ 11 --jq '.[-1].id // empty' 2>/dev/null) 12 13 # Get the plan's recorded last_comment_id 14 PLAN_COMMENT_ID=$(grep '^last_comment_id:' docs/plans/{slug}.md | sed 's/last_comment_id: *//') 15fi

If new comments exist (LATEST_COMMENT_ID != PLAN_COMMENT_ID):

  1. Read each comment since the plan's last_comment_id
  2. Incorporate relevant feedback into the plan (scope changes, new requirements, corrections)
  3. Update last_comment_id: in the plan frontmatter to LATEST_COMMENT_ID
  4. Commit the updated plan

If no tracking issue or no comments: Skip this step.

Phase 3: Critique and Enumerate Questions

  1. Review assumptions - What did I assume that might be wrong?
  2. Identify gaps - What's unclear or risky?
  3. Enumerate questions - List all questions needing supervisor input
  4. Add questions to plan - Append to "Open Questions" section
  5. Pre-send checklist:
    • Plan committed AND pushed to main
    • GitHub issue has **Plan:** https://github.com/${REPO}/blob/main/docs/plans/{slug}.md
    • Plan frontmatter has tracking: set to the issue URL
  6. Send reply:
Plan draft created: docs/plans/{slug}.md

Tracking: {GitHub issue URL or Notion page URL}

I've made the following key assumptions:
- [Assumption 1]
- [Assumption 2]

Please review the Open Questions section at the end of the plan and provide answers so I can finalize it.

Phase 4: Finalize Plan

After receiving answers:

  1. Update plan - Incorporate feedback, remove Open Questions section
  2. Mark as finalized - Update frontmatter: status: Ready
  3. Suggest implementation prompt:
Plan finalized: docs/plans/{slug}.md

When you're ready to implement, use this prompt:

---
Implement the plan in docs/plans/{slug}.md

Follow the solution approach, stay within the appetite, and avoid the identified rabbit holes. Check off success criteria as you complete them.
---

Output Location

All plans go to: docs/plans/{slug}.md

Use snake_case for slugs: async_meeting_reschedule.md, dark_mode_toggle.md, api_response_caching.md

Branch Workflow

Plans are written directly on the main branch. The plan document itself is just a document -- no feature branch needed.

When the plan is executed (via /do-build), the build skill creates a feature branch, does the work there, and opens a PR.

Status Tracking

Status and classification are tracked in the plan document's YAML frontmatter.

Required Frontmatter Fields:

  • status: - Current state of the plan
  • type: - Classification (bug, feature, or chore) - MANDATORY

Status Values:

  • Planning - Initial draft being created
  • Ready - Finalized and ready for implementation
  • In Progress - Being implemented
  • Complete - Shipped to production
  • Cancelled - Not pursuing this

Update status as work progresses. Keep all tracking in the plan document itself.

Tracking issue lifecycle:

  • When plan status changes to Ready or In Progress, update the GitHub issue / Notion task status accordingly
  • When plan status changes to Complete, close the GitHub issue (gh issue close) or mark the Notion task as done
  • When plan status changes to Cancelled, close the issue with a comment explaining why

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