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Adoption

Make this repository AI-first

One endpoint to turn any repository into a spec-driven, agent-ready codebase. Install the skill, let an agent onboard the repo, then plan and execute structured work with any coding agent.

Copy into your agent

Copy the full init.md prompt and paste it into your AI coding agent — Claude Code, Cursor, Codex, or any other. It reads the methodology and specification, installs the skill, and onboards this repository for Deep Work Plans.

https://deepworkplan.com/init.md

Self-contained agent prompt → /init.md

What this does

Adoption changes the repository in two durable ways — the pillars of the methodology.

First, the repository becomes spec-driven: work begins from a written plan and specification, not from ad-hoc prompts. Second, the repository itself becomes the agent harness — an AGENTS.md, a docs/ knowledge base, per-module docs, and a .agents/ skill home (with the .claude → .agents and .cursor → .agents symlinks) give every agent the context and commands it needs.

The adoption sequence

  1. 01

    Verify before you install

    Treat the prompt and the skill as untrusted until you have checked them. Both are open source and MIT; the skill is Markdown-first with no network calls and no telemetry. Each release publishes a SHA256SUMS over the shipped skill, so you can confirm your copy matches before running it. Releases are checksummed, not signed (signing is a documented next step).

    agent 
    git clone https://github.com/DailybotHQ/deepworkplan-skill.git && cd deepworkplan-skill
curl -fsSL -o SHA256SUMS https://github.com/DailybotHQ/deepworkplan-skill/releases/download/vX.Y.Z/SHA256SUMS
./setup.sh --verify
  2. 02

    Install the skill

    Add the Deep Work Plan skill so any agent can plan and execute structured work. The skill ships a router plus eight sub-skills — create, execute, refine, resume, status, verify, onboard, and author.

    agent 
    npx skills add DailybotHQ/deepworkplan-skill

    or

    agent 
    openclaw skills install deepworkplan

    or

    agent 
    git clone https://github.com/DailybotHQ/deepworkplan-skill.git && cd deepworkplan-skill && ./setup.sh
  3. 03

    Run repository onboarding

    Invoke the onboard sub-skill and let the agent reason about the actual repo — its stack, package manager, and real validation commands. It generates AGENTS.md, a docs/ knowledge base, per-module docs, and a cross-agent .agents/ home (with the .claude → .agents and .cursor → .agents symlinks), wires thin dwp-* commands, and scaffolds a gitignored .dwp/ for plans and drafts. For large repos the onboard sub-skill uses a plan-driven path: it completes reconnaissance, then emits an onboarding Deep Work Plan. Nothing is templated; everything is adapted to your repository.

    agent 
    /deepworkplan-onboard
  4. 04

    Accept opt-in addons

    Onboarding offers five opt-in addons — devcontainer, Dailybot, dependency-upgrade, design-system, and AI Diff Reviewer — that you accept only when they fit. A repo is fully conformant with zero addons. Use /skill-create and /agent-create (the author sub-skill) to grow stack-appropriate skills, agents, and commands beyond the baseline.

  5. 05

    Plan and execute

    Generate Deep Work Plans with /dwp-create and run them with /dwp-execute, then /dwp-status, /dwp-refine, /dwp-resume, and /dwp-verify as work proceeds. Each plan carries numbered tasks, validation gates, and a completion protocol — ending with three mandatory final tasks: a Security Review, Skills & Agents Discovery, and an Executive Report.

  6. 06

    Verify conformance

    Run /dwp-verify for an objective pass/fail report against the specification. Confirm AGENTS.md, docs/ (with real content, not stubs), .agents/ (with thin dwp-* delegators and a catalog that matches disk), .dwp/, and tmp/ are in place — nothing templated, everything reasoned for this repo.

    agent 
    /dwp-verify

The outcome

The repository becomes spec-driven and the repository itself becomes the agent harness — context and commands travel with the code.

Read next

Quickstart Methodology Spec Kit