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Spec Intake

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Prismer-AI
spec-intake

Turn one atomic feature/gap request into a dispatchable task in a single pass — create the task, write SPEC.md (auto-folded into linkedAssetIds server-side), add acceptance criteria, record provenance. Two-part epics are refused, not silently split.

Overview

PublisherPrismer-AI
RepositoryPrismerCloud
Skill namespec-intake
Stars
1.6K
Forks
17
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

    Published by Prismer-AI on GitHub. Read the source before you install it.

Installation

Install the Spec Intake 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/Prismer-AI/PrismerCloud/tree/main/sdk/apc/skills/spec-intake
  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/Prismer-AI/PrismerCloud.git /tmp/PrismerCloud
mkdir -p .claude/skills
cp -r /tmp/PrismerCloud/sdk/apc/skills/spec-intake .claude/skills/spec-intake
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spec Intake 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 Spec Intake 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 Spec Intake 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.

spec-intake

把一个原子 feature/gap 请求一次性做成可派发的 task:create + spec-set(SPEC 自动折进 dispatch 可见的 linkedAssetIds)+ acceptanceCriteria + provenance(apc/05 §1 C1 · S3)。核心纪律:SPEC 折进 dispatch 由服务端自动做,你不手动折;期望写进 description/SPEC,coding agent 才看得到。

什么时候用:一个原子级 feature/gap 要进研发链——需要建 task、写清 SPEC 与验收标准,让下游 coding agent 一 dispatch 就拿到完整上下文。

承重约束(先记死)

  • 原子级 only:S3 是"一个 gap → 一个 task"。请求里裹了两个及以上独立 feature(epic)→ 拒绝,不许 silently 拆。epic 拆分/依赖排序不在本 skill,交人先拆。
  • SPEC 折进 dispatch 是服务端自动的:cloud task spec-set 落 SPEC asset 后,服务端已把它折进 metadata.assets.linkedAssetIds(task-spec.service.ts C1/S3)。你不要再 cloud task meta set 手动折 SPEC——那会重复且可能覆盖兄弟键。
  • 期望进 description + SPEC:dispatch 只把 title/description + linkedAssetIds 喂给 coding agent。验收期望要么写进 description,要么写进 SPEC(会被折进 linkedAssetIds),否则 agent 看不到。
  • provenance 用 --set-string:sha / id 用 --set 会被推断成 number(全数字 sha 精度毁)。写 sha 一律 --set-string。
  • metadata 写权限:cloud task meta set 走 gate = creator / orchestrator / admin / owner。spec-intake 是 intake 步、由主 agent 跑,主 agent 建的 task 天然是 creator——所以能写。assignee 不在内。

工具契约(签名以此为准,先核后用)

命令作用关键 flag
cloud task create --title <t>建 task;期望写进 --description--title(必填)· --description · --kind work_item|goal(默认 work_item)· --assignee-id · --json
cloud task spec-set <task-id> -m <md>写 SPEC.md(服务端自动折进 linkedAssetIds)-m/--markdown 或 -f/--file(内容源) · --json
cloud task add-criterion <task-id> --mode <m> --expectation <md>加一条验收标准--mode qualitative|quantitative|agent-self-check|manual(必填)· --expectation(必填)· --optional · --json
cloud task acceptance <task-id>读回验收标准 + 汇总状态--json
cloud task get <task-id>读回 task(含 metadata)--json
cloud task meta set <task-id> --set-string k=v写 provenance(非 SPEC 的 id / sha)--set-string(值恒字符串)
  • --kind 合法值恰好两个:work_item(默认,可派发工作项)| goal(长期目标投影)。intake 用 work_item。
  • --mode 合法值恰好四个:qualitative | quantitative | agent-self-check | manual。
  • 各命令 --json 下 stdout 是结构化响应;成功退出码 0,失败非零。

Procedure

0. 先判:是不是原子请求?

请求里如果有"并且做 X 又做 Y"这种两个独立 feature 的形状 → 停手,拒绝,回一句"这是 epic,请先拆成原子请求再逐个 intake"。不要建一个塞两件事的 task,也不要偷偷建两个 task。

1. 建 task(期望写进 description)

bash
cloud task create \
  --title "feat: 一句话需求" \
  --description "期望/验收要点写这里,coding agent dispatch 时看得到。" \
  --kind work_item --json > /tmp/task.json

从 stdout 取 data.id(下称 $TID)。

2. 写 SPEC(服务端自动折进 linkedAssetIds)

bash
cloud task spec-set "$TID" -m "# SPEC

## 目标
...

## 验收标准
- ...
" --json

不要再手动 meta set 折 SPEC——spec-set 已经把 SPEC asset id 折进 metadata.assets.linkedAssetIds 了。

3. 加验收标准(每条一个 criterion)

bash
cloud task add-criterion "$TID" \
  --mode qualitative \
  --expectation "done 长这样:<可判定的验收点>" --json

按需要加多条。verifier 具体方法(怎么验)留给下游,这里只钉 qualitative/quantitative 的期望。

4. 写 provenance(非 SPEC 的 id / sha,用 --set-string)

bash
cloud task meta set "$TID" \
  --set-string intake.sourceSha="$SOURCE_SHA" \
  --set-string intake.gapRef="apc/05 C1"

--set-string 保证 sha 落库是字符串(全数字 sha 不被数字化)。

5. 读回确认(副作用 oracle)

bash
cloud task get "$TID" --json          # 看 metadata.assets.linkedAssetIds 含 SPEC asset id
cloud task acceptance "$TID" --json   # 看 criteria 落库

输出契约(机器判据按这个回读,别自由发挥格式)

本 skill 的判据不是「报告里出现了 spec-set 这几个字」,而是判据拿你声明的 id 去真库/真 API 回读(structured-criteria.ts 的 declared-id-readback)。apc/12 §0.6 记了实证:一份一条 cloud 命令都没跑的编造报告,在旧文本判据下 5/5 全绿。所以报告必须带下面这几类可机器解析的行:

TASK: <cloud task create 返回的 task id>
ASSET: <已在 metadata.assets.linkedAssetIds 里的 SPEC asset id> | role: spec
CRITERION: <cloud task acceptance 里的一个 criterion id>
META: intake.sourceSha = <你写进去的值> | type: string

判据会判红的情况(任一):

  • TASK: 声明的 id 在服务端不存在(回读 404)——这正是「声称跑了但没跑」的指纹;
  • 该 task 不是本次跑出来的(createdAt 超过 maxAgeMinutes)——拿一个老 task 的 id 冒充不成立;
  • task 不是 metadata.kind=work_item,或 description 太短(dispatch 只喂 title/description + linkedAssetIds,描述空 = 下游 agent 看不到期望);
  • ASSET: 声明的 id 不在 metadata.assets.linkedAssetIds 里——SPEC 没折进 dispatch;
  • CRITERION: 声明的 id 不在 该 task 的 acceptance view 上,或该 task 一条 criterion 都没有;
  • META: 的键在 metadata 里不存在、值与服务端存的不一致,或存储类型不是 string——--set 的数字化在这里被逮住,不靠你自述「我用了 --set-string」。

回读凭据缺失(无 APC_READBACK_TOKEN / DEV_JWT / PRISMER_API_KEY)或 API 不可达 ⇒ 判红。「验不了」永远不算过。

⚠️ 回读凭据必须与真正跑命令的那个身份一致。GET /api/im/tasks/:id 的 ACL 是 creator / assignee / workspace 成员——换一个账号回读会 403(判据 fail-closed 判红,是假红)。实测踩过:cloud CLI 优先读 ~/.prismer/config.toml 的 api_key(先于 PRISMER_API_KEY env),于是 task 建在 A 账号名下、回读用 B 账号的 JWT ⇒ 403。跑验收时把 APC_READBACK_TOKEN 设成执行 agent 自己的凭据;APC_READBACK_BASE 默认 http://127.0.0.1:3000。

这些 id 必须从命令输出里原样复制,不要重打、不要凭记忆写——回读比对的是字节。

产出(副作用 oracle,报告里必须给)

  1. task 真建:cloud task create 的 data.id + cloud task get 读回。
  2. SPEC 折进 dispatch:cloud task get --json 的 metadata.assets.linkedAssetIds 含 SPEC asset id(服务端自动折的,不是本 skill 手动折)。
  3. criteria 落库:cloud task acceptance --json 读回的 criteria 列表(每条 expectation + status)。
  4. provenance 落库:metadata.intake.sourceSha 是字符串(读回确认,全数字 sha 也不被数字化)。
  5. epic 拒绝:两部分请求下,未建 task(或明确拒绝)——不 silently 拆。

不许:手动折 SPEC 进 linkedAssetIds(服务端已做);把两部分 epic silently 拆成多 task;用 --set 写 sha;断言聊天文本而非 metadata/criteria 副作用。

诚实边界

  • 判据的能力上界:回读证明的是「这些 id 真在库里、关系真成立、类型真对」。它不证明 SPEC 内容质量,也不能证明「两部分 epic 被拒」——「什么都没建」是缺失,回读不了,那一条至今仍是文本判据(apc/12 §0.6 记录的残余空洞,不得声称已闭合)。
  • spec-intake 只承接原子级需求(apc/05 §1 C1 诚实边界)。epic 分解需先经人拆分。
  • spec-set 会服务端给 task 落 metadata.devEntryApprovalRequired=true(apc/03 §1「需求进入开发」门,服务端 task-spec.service.ts 自动,非你手动写)。该 flag 潜伏——仅当该 task 又绑了 feature/bugfix 卡(cardBound)时,首次 coding dispatch 前才 park 到 awaiting_approval 等人审「进开发」;非卡任务 flag 无效果、照常派发。所以本 skill 后续若走 09 §0 spec 段证据(「审批 进开发 decision」),请 cloud approval request-human 起该审批供人裁决。
  • 本 skill 若作为独立 dispatch 运行(无父 task 上下文),下方 cloud skill ack 会 exit 3(无 task 上下文)——这是契约限制,报告里不得声称已 ack。它建的 task 是产物,不是它自己的 dispatch task。

调用回执(APC 平台方运营纪律 · 自动注入,勿手改)

执行本 skill 的第一步,先落一条调用回执:

bash
cloud skill ack spec-intake --task "$PRISMER_TASK_ID"

按退出码分流(这条命令的退出码是承重信息,禁止用 || 兜底、; true、 set +e 或重定向把它抹掉):

exit含义你要做的
0回执已落库(im_task_logs.action='skill_ack')继续执行本 skill
3无 task 上下文——本次运行没有 task,产不出回执继续执行本 skill;但本次运行没有回执,任何报告里都不得声称已 ack
4你不是该 task 的 assignee,服务端拒绝停下并上报:回执只能由执行该 task 的 agent 产生
1其它失败(网络 / 服务端)重试一次;仍失败则继续执行,并在结果里显式标注「回执缺失」

回执只证明本 skill 被调度,不证明执行正确——效果证明由本 skill 自己的 acceptanceCriteria 副作用断言承担(apc/04 §2 层 1 诚实标注)。

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 Spec Intake AI skill do?

Turn one atomic feature/gap request into a dispatchable task in a single pass — create the task, write SPEC.md (auto-folded into linkedAssetIds server-side), add acceptance criteria, record provenance. Two-part epics are refused, not silently split.

Why use Spec Intake on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Prismer-AI/PrismerCloud/tree/main/sdk/apc/skills/spec-intake. 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 Spec Intake?

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 Spec Intake?

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

Is the Spec Intake AI skill free?

Yes. It is published on GitHub by Prismer-AI 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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