Tapd Story Plan logo

Tapd Story Plan

Organization
TencentBlueKing
tapd-story-plan

迭代执行流水线开发计划阶段。基于 spec.md 调用 /speckit.plan 以测试驱动开发模式构建 开发计划、技术调研与数据模型,并在同一 subagent 内就地做文档级合规自检,产出 plan-report.md。 speckit 命令与报告模板详见 ../references/subagent-prompt-template.md §2.2。

Overview

PublisherTencentBlueKing
Repositorybk-bcs
Skill nametapd-story-plan
Stars
840
Forks
263
Bundled files
Instructions only
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 TencentBlueKing on GitHub. Read the source before you install it.

Installation

Install the Tapd Story Plan 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/TencentBlueKing/bk-bcs/tree/master/.agents/skills/tapd-story-pipeline/tapd-story-plan
  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/TencentBlueKing/bk-bcs.git /tmp/bk-bcs
mkdir -p .claude/skills
cp -r /tmp/bk-bcs/.agents/skills/tapd-story-pipeline/tapd-story-plan .claude/skills/tapd-story-plan
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tapd Story Plan 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 Tapd Story Plan 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 Tapd Story Plan 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.

需求实现计划

前置条件

  • 当前需求 phase 为 tech-clarified
  • ${WORKDIR}/spec.md 已存在
  • pipeline 主编排已按 references/context-and-meta-template.md 重写当前阶段的 context.md (白名单包含:spec.md + 项目宪章 + 架构/安全/编码规范引用文档)

输入

  • ${WORKDIR}/meta.yaml(读取 attempts)
  • 当前需求 ID(由 pipeline 主编排传入)
  • ${WORKDIR}/spec.md
  • ${WORKDIR}/context.md
  • ${WORKDIR}/iteration-patches/attempt-${N}.md(若为回退重入,必读)

执行流程

pipeline 主编排按 ../references/subagent-prompt-template.md §2.2 Plan 模板渲染 SUBAGENT_PROMPT(含 speckit.plan skill + 就地合规自检 + plan-report.md 产出), 通过 Task(subagent_name="tech-lead", ...) 拉起 subagent。

渲染填充:${ID} / ${VERSION} / ${WORK_DIR} / attempts / round; 若 attempts > 1,附上 iteration-patches/attempt-${attempts}.md 摘要。

subagent 内的两段工作(speckit-plan + 就地合规自检)、合规自检 checklist(3 个维度: 完整度 / research 合规 / 项目宪章)、plan-report.md 统一模板、Verdict 判定规则 均在 subagent-prompt-template.md §2.2 中定义,本子 skill 不重复。

内容门禁(主编排内联)

在消费 plan-report.md 的 verdict 前,主编排必须确认 plan 产物具备可实施性:

检查项通过标准
技术上下文plan.md 明确技术栈、受影响模块与项目结构;未知项已在 research.md 中消除或显式列出
架构与契约方案描述模块交互和依赖方向;存在外部接口时,contracts/ 或 plan.md 定义了请求、响应及错误行为
数据模型涉及数据时 data-model.md 定义实体、字段、校验与关系;不涉及时在 plan.md 显式说明
需求映射spec.md 的每个用户场景、功能需求和验收标准均能映射到实现方案或明确标注不适用原因
关键决策research.md 对每个选型记录 Decision、Rationale 和 Alternatives considered

不要求固定的文件章节标题,应根据 Speckit 模板的等效内容判断。任一项未满足时, 主编排将 compliance.verdict 视为 needs_fix,在 report 中列出缺失项并按同 attempt round 重试规则处理。

消费 subagent 回传 JSON

按 compliance.verdict 分支:

Verdict主会话动作
pass进入"更新状态"推进 phase
needs_fix同 attempt 内 round +1,重跑 plan(最多 3 个 round)
spec_insufficient整理 findings 摘要为 iteration-patches/attempt-${N+1}.md,target_phase_to_re_enter=specify,走回退重入

status=fail 按 references/error-handling.md 处理,一般升级为回退重入。

更新状态

compliance.verdict=pass 时:

  • 更新 meta.yaml.phase 为 researched
  • meta.yaml.history 追加成功记录,清空 meta.yaml.last_failure

可重入约定

通用规则见 ../references/shared-reentry-conventions.md。本阶段差异:

  • round 重试触发条件:compliance.verdict=needs_fix,同 attempt 内最多 3 个 round
  • 报告文件不做快照:plan-report.md 每轮覆盖重写,历史可在 process.log 中追溯
  • 读取最新 attempt-*.md(若有),在 SUBAGENT_PROMPT 中追加重入增量段到 /speckit.plan 提示词中 (subagent-prompt-template.md §2.2)

幂等性、产物保留、process.log 仅追加等通用约束见 ../references/reentry-protocol.md。

信息不足回退(回退重入入口)

当 verdict=spec_insufficient 或下游子 skill 反向追溯到本阶段信息不足时, attempt-${N+1}.md 必须明确:

  • failed_phase: plan
  • root_cause:可直接复用 plan-report.md 中对应 Finding 的根因字段
  • target_phase_to_re_enter: specify(spec 不足)或 plan(仅 plan 自身)

参考资料

  • SUBAGENT_PROMPT(plan 模板 + 合规自检 checklist + 报告模板):../references/subagent-prompt-template.md §2.2
  • 通用回传 JSON Schema:../references/subagent-prompt-template.md §1.3
  • 上下文与元数据模板:../references/context-and-meta-template.md
  • 可重入协议:../references/reentry-protocol.md
  • 错误处理:../references/error-handling.md

产出

  • ${WORKDIR}/plan.md — 开发计划
  • ${WORKDIR}/research.md — 技术调研报告
  • ${WORKDIR}/data-model.md — 数据模型(如有)
  • ${WORKDIR}/contracts/*.md — 通讯协议(如有)
  • ${WORKDIR}/plan-report.md — 文档级合规自检报告
  • ${WORKDIR}/process.log — stream-json 流式日志(追加)
  • ${WORKDIR}/meta.yaml — 更新后的 attempts / round / history
  • 需求 phase 为 researched

Frequently asked questions

What does the Tapd Story Plan AI skill do?

迭代执行流水线开发计划阶段。基于 spec.md 调用 /speckit.plan 以测试驱动开发模式构建 开发计划、技术调研与数据模型,并在同一 subagent 内就地做文档级合规自检,产出 plan-report.md。 speckit 命令与报告模板详见 ../references/subagent-prompt-template.md §2.2。

Why use Tapd Story Plan on TypingMind?

Because you install it once and use it with any model. Tapd Story Plan 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 Tapd Story Plan in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/TencentBlueKing/bk-bcs/tree/master/.agents/skills/tapd-story-pipeline/tapd-story-plan. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tapd Story Plan?

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 Tapd Story Plan?

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

Is the Tapd Story Plan AI skill free?

It is published on GitHub by TencentBlueKing. 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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