Agent skill
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.
Install this agent skill to your Project
npx add-skill https://github.com/davila7/claude-code-templates/tree/main/cli-tool/components/skills/productivity/notion-spec-to-implementation
Metadata
Additional technical details for this skill
- short description
- Turn Notion specs into implementation plans, tasks, and progress tracking
SKILL.md
Spec to Implementation
Convert a Notion spec into linked implementation plans, tasks, and ongoing status updates.
Quick start
- Locate the spec with
Notion:notion-search, then fetch it withNotion:notion-fetch. - Parse requirements and ambiguities using
reference/spec-parsing.md. - Create a plan page with
Notion:notion-create-pages(pick a template: quick vs. full). - Find the task database, confirm schema, then create tasks with
Notion:notion-create-pages. - Link spec ↔ plan ↔ tasks; keep status current with
Notion:notion-update-page.
Workflow
0) If any MCP call fails because Notion MCP is not connected, pause and set it up:
- Add the Notion MCP:
codex mcp add notion --url https://mcp.notion.com/mcp
- Enable remote MCP client:
- Set
[features].rmcp_client = trueinconfig.tomlor runcodex --enable rmcp_client
- Set
- Log in with OAuth:
codex mcp login notion
After successful login, the user will have to restart codex. You should finish your answer and tell them so when they try again they can continue with Step 1.
1) Locate and read the spec
- Search first (
Notion:notion-search); if multiple hits, ask the user which to use. - Fetch the page (
Notion:notion-fetch) and scan for requirements, acceptance criteria, constraints, and priorities. Seereference/spec-parsing.mdfor 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:notion-search→Notion:notion-fetchto confirm the data source and required properties). Patterns inreference/task-creation.md. - Size tasks to 1–2 days. Use
reference/task-creation-template.mdfor 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-pagesusing the database'sdata_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 withreference/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).
Recommended Agent Skills
Expand your agent's capabilities with these related and highly-rated skills.
verl-rl-training
Provides guidance for training LLMs with reinforcement learning using verl (Volcano Engine RL). Use when implementing RLHF, GRPO, PPO, or other RL algorithms for LLM post-training at scale with flexible infrastructure backends.
openrlhf-training
High-performance RLHF framework with Ray+vLLM acceleration. Use for PPO, GRPO, RLOO, DPO training of large models (7B-70B+). Built on Ray, vLLM, ZeRO-3. 2× faster than DeepSpeedChat with distributed architecture and GPU resource sharing.
gguf-quantization
GGUF format and llama.cpp quantization for efficient CPU/GPU inference. Use when deploying models on consumer hardware, Apple Silicon, or when needing flexible quantization from 2-8 bit without GPU requirements.
Claude Code Guide
Master guide for using Claude Code effectively. Includes configuration templates, prompting strategies "Thinking" keywords, debugging techniques, and best practices for interacting with the agent.
qdrant-vector-search
High-performance vector similarity search engine for RAG and semantic search. Use when building production RAG systems requiring fast nearest neighbor search, hybrid search with filtering, or scalable vector storage with Rust-powered performance.
behavioral-modes
AI operational modes (brainstorm, implement, debug, review, teach, ship, orchestrate). Use to adapt behavior based on task type.
Didn't find tool you were looking for?