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Notion Spec To Implementation

Organization
beep-effect
notion-spec-to-implementation

Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.

Overview

Publisherbeep-effect
Repositorybeep-effect
Skill namenotion-spec-to-implementation
Stars
74
Forks
14
Bundled files
16
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • 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.

  • 16 bundled files

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

  • Open source

    Published by beep-effect on GitHub. Read the source before you install it.

Installation

Install the Notion Spec To Implementation 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/beep-effect/beep-effect.git /tmp/beep-effect
mkdir -p .claude/skills
cp -r /tmp/beep-effect/plugins/notion/skills/notion-spec-to-implementation .claude/skills/notion-spec-to-implementation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Notion Spec To Implementation 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 Notion Spec To Implementation 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 Notion Spec To Implementation 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 to Implementation

Convert a Notion spec into linked implementation plans, tasks, and ongoing status updates.

Quick start

  1. Locate the spec with Notion:search, then fetch it with Notion:fetch.
  2. Parse requirements and ambiguities using reference/spec-parsing.md.
  3. Create a plan page with Notion:notion-create-pages (pick a template: quick vs. full).
  4. Find the task database, confirm schema, then create tasks with Notion:notion-create-pages.
  5. Link spec ↔ plan ↔ tasks; keep status current with Notion:notion-update-page.

Tool-call guardrails

  • Notion tool availability can vary by workspace. If a Notion MCP call returns Tool <name> not found, treat that tool as unavailable for the rest of the current task. Do not retry it with different arguments or call it again later; use Notion:search and Notion:fetch where sufficient.
  • Use one literal search query per Notion:search call and include filters: {} when no narrower filter is needed. Run separate searches for alternate phrasings instead of putting or in a single query string.
  • Only send Notion page, database, or data-source URLs/IDs to Notion:fetch; external connected-source search results are not fetch targets.
  • Create plans and task pages with explicit parent and pages fields. For task databases, fetch first and use the returned collection://... data source ID.
  • To append an implementation section to a spec, fetch the current section text first, then use Notion:notion-update-page with command: "update_content", properties: {}, and exact old_str / new_str content. For property-only edits, use command: "update_properties" with content_updates: []; the current deployed schema expects both top-level fields even when one is unused.

Workflow

0) If Notion tools are unavailable, pause and ask the user to connect the Notion app:

  1. Enable the bundled Notion app for this plugin or session.
  2. Complete the Notion auth flow if Codex prompts for it.
  3. Restart Codex or the current session if the tools still do not appear.

After the app is connected, finish your answer and tell the user to retry so they can continue with Step 1.

1) Locate and read the spec

  • Search first (Notion:search); if multiple hits, ask the user which to use.
  • Fetch the page (Notion:fetch) and scan for requirements, acceptance criteria, constraints, and priorities. See reference/spec-parsing.md for extraction patterns.
  • Capture gaps/assumptions in a clarifications block before proceeding.

2) Choose plan depth

  • Simple change → use reference/quick-implementation-plan.md.
  • Multi-phase feature/migration → use reference/standard-implementation-plan.md.
  • Create the plan via Notion:notion-create-pages, include: overview, linked spec, requirements summary, phases, dependencies/risks, and success criteria. Link back to the spec.

3) Create tasks

  • Find the task database (Notion:search → Notion:fetch to confirm the data source and required properties). Patterns in reference/task-creation.md.
  • Size tasks to 1–2 days. Use reference/task-creation-template.md for content (context, objective, acceptance criteria, dependencies, resources).
  • Set properties: title/action verb, status, priority, relations to spec + plan, due date/story points/assignee if provided.
  • Create pages with Notion:notion-create-pages using the database’s data_source_id.

4) Link artifacts

  • Plan links to spec; tasks link to both plan and spec.
  • Optionally update the spec with a short “Implementation” section pointing to the plan and tasks using Notion:notion-update-page.

5) Track progress

  • Use the cadence in reference/progress-tracking.md.
  • Post updates with reference/progress-update-template.md; close phases with reference/milestone-summary-template.md.
  • Keep checklists and status fields in plan/tasks in sync; note blockers and decisions.

References and examples

  • reference/ — parsing patterns, plan/task templates, progress cadence (e.g., spec-parsing.md, standard-implementation-plan.md, task-creation.md, progress-tracking.md).
  • examples/ — end-to-end walkthroughs (e.g., ui-component.md, api-feature.md, database-migration.md).

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 Notion Spec To Implementation AI skill do?

Turn Notion specs into implementation plans, tasks, and progress tracking; use when implementing PRDs/feature specs and creating Notion plans + tasks from them.

Why use Notion Spec To Implementation on TypingMind?

Because you install it once and use it with any model. Notion Spec To Implementation 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 Notion Spec To Implementation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/beep-effect/beep-effect/tree/main/plugins/notion/skills/notion-spec-to-implementation. 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 Notion Spec To Implementation?

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 Notion Spec To Implementation?

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

Is the Notion Spec To Implementation AI skill free?

Yes. It is published on GitHub by beep-effect under the Apache-2.0 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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