Spec-driven execution
The product installs a durable plan as the source of truth, with explicit acceptance criteria and validation gates so long tasks can be resumed and verified instead of abandoned midstream.
Deep Work Plan
Deep Work Plan is a repository-level workflow for AI coding agents that turns a codebase into a structured, resumable execution environment. It helps teams run long-horizon tasks with explicit plans, validation gates, and repository-specific instructions.
Overview
Deep Work Plan is a repository-level workflow for AI coding agents that turns a codebase into a structured execution environment. Its core idea is that context, guardrails, and a durable plan matter more than the model alone when work spans many files, many decisions, or many sessions.
The product combines a methodology, a specification, a kit, and agent adapters. In practice, you start with an `/init.md` prompt, let the agent inspect the repository’s real stack, and generate repository-specific artifacts such as `AGENTS.md`, categorized `docs/`, `.agents/`, and a gitignored `.dwp/` workspace for plans and drafts.
Deep Work Plan is aimed at long-horizon coding tasks such as migrations, refactors, and new subsystems, where a plan needs to survive context overflow and remain verifiable. The site also presents it as agent-agnostic: the same Markdown-based methodology can run in supported tools including Claude Code, Cursor, Codex, and others.
Features
Reasoned repository onboarding
Onboarding inspects the repository’s real manifests, folder layout, and CI to infer the stack and generate repository-specific artifacts instead of a generic template.
Repository-local harness
The kit creates or installs repository artifacts such as `AGENTS.md`, `docs/`, module-level docs, `.agents/`, and `.dwp/` so the repository itself carries the operating instructions.
Reusable skill and command pack
The skill pack includes the core router plus sub-skills such as create, execute, refine, resume, status, verify, onboard, and author, each exposed through thin commands.
Agent adapters and stack presets
Support for multiple agents is presented through adapters and presets, including Claude Code, Cursor, OpenAI Codex, GitHub Copilot, Gemini, OpenCode, Windsurf, and Cline.
Conformance verification
A verification command produces an objective pass/fail conformance report, which lets teams check whether a repository is Deep Work Plan-conformant after setup and after later plans.
Use cases
Long refactors and migrations
Use Deep Work Plan when an agent needs to carry a large change across many files, keep decisions explicit, and resume later without losing the plan.
Repository onboarding
Use it to onboard a repository into an AI-first workflow by generating repository-specific instructions, docs, and validation hooks from the real codebase.
Multi-agent repositories
Use it when a team wants the same planning and execution method across multiple agents, while still adapting to the repository’s actual stack and commands.
Conformance checks
Use the verification command after setup or after a plan to check whether the repository and its plans conform to the specification.
Extending the workflow
Use the kit’s author and addon concepts when a repository needs to grow its own reusable skills, agents, or dependency-upgrade routines.
Pros and Cons
Pros
- Designed for long-horizon repository work with explicit plans and validation gates.
- Agent-agnostic approach with multiple adapters and presets.
- Repository-local artifacts make the workflow resumable and auditable.
- Conformance can be checked with a verification command.
- The kit separates the reusable skill from the repository adaptation work.
Cons
- The source does not publish pricing information.
- Setup is repository-specific and depends on the agent reasoning about the actual stack, so it is not a simple one-click template.
FAQ
What problem does Deep Work Plan solve?
Deep Work Plan is designed to help AI coding agents handle long-horizon work inside a repository by keeping plans, acceptance criteria, and validation gates explicit and resumable.
Which agents does it support?
The source describes the product as agent-agnostic and shows adapters for Claude Code, Cursor, OpenAI Codex, GitHub Copilot, Gemini, OpenCode, Windsurf, Cline, and others.
How do you get started?
The homepage says you paste the /init.md prompt into an agent, which then reads the methodology and spec and adapts the repository by generating artifacts such as AGENTS.md, docs/, .agents/, and .dwp/.
How does it fit into a repository workflow?
The methodology and kit describe a repository-first workflow: the repo becomes the harness, plans live in gitignored .dwp/, and progress can be resumed from git-backed state.
Is pricing listed on the site?
The pricing page is not available from the collected sources, so pricing details are not published in the evidence provided.
Quick Facts
- Category
- Developer Tool
- Primary users
- AI coding agents and developers using them
- Workflow
- Spec-driven, repository-local execution
- Source domain
- deepworkplan.com
- Pricing
- Not published in the collected sources
- Core artifacts
- AGENTS.md, docs/, .agents/, .dwp/
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