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/x-cmd/skill/tree/main/data/openai/.curated/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.
pufferlib
High-performance reinforcement learning framework optimized for speed and scale. Use when you need fast parallel training, vectorized environments, multi-agent systems, or integration with game environments (Atari, Procgen, NetHack). Achieves 2-10x speedups over standard implementations. For quick prototyping or standard algorithm implementations with extensive documentation, use stable-baselines3 instead.
fluidsim
Framework for computational fluid dynamics simulations using Python. Use when running fluid dynamics simulations including Navier-Stokes equations (2D/3D), shallow water equations, stratified flows, or when analyzing turbulence, vortex dynamics, or geophysical flows. Provides pseudospectral methods with FFT, HPC support, and comprehensive output analysis.
metabolomics-workbench-database
Access NIH Metabolomics Workbench via REST API (4,200+ studies). Query metabolites, RefMet nomenclature, MS/NMR data, m/z searches, study metadata, for metabolomics and biomarker discovery.
geniml
This skill should be used when working with genomic interval data (BED files) for machine learning tasks. Use for training region embeddings (Region2Vec, BEDspace), single-cell ATAC-seq analysis (scEmbed), building consensus peaks (universes), or any ML-based analysis of genomic regions. Applies to BED file collections, scATAC-seq data, chromatin accessibility datasets, and region-based genomic feature learning.
zinc-database
Access ZINC (230M+ purchasable compounds). Search by ZINC ID/SMILES, similarity searches, 3D-ready structures for docking, analog discovery, for virtual screening and drug discovery.
astropy
Comprehensive Python library for astronomy and astrophysics. This skill should be used when working with astronomical data including celestial coordinates, physical units, FITS files, cosmological calculations, time systems, tables, world coordinate systems (WCS), and astronomical data analysis. Use when tasks involve coordinate transformations, unit conversions, FITS file manipulation, cosmological distance calculations, time scale conversions, or astronomical data processing.
Didn't find tool you were looking for?