theclaymethod

theclaymethod / deep-plan

Public

Structured research → plan → annotate → implement workflow for AI-assisted development. Based on Boris Tane's workflow.

14
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100% credibility
Found Feb 18, 2026 at 14 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

Deep-plan is a workflow skill for AI coding agents that guides users through researching codebases, creating and refining detailed plans, and then executing implementations to ensure accurate development.

How It Works

1
🔍 Discover the planning helper

You hear about a smart tool that helps AI assistants plan software projects carefully before building anything.

2
➕ Add it to your AI buddy

With one simple command, you connect this planning skill to your favorite AI coding helper.

3
💭 Describe your project

You start a session and simply tell the AI what new feature or change you want to make.

4
📖 Review the detailed plan

The AI studies your existing code, creates a step-by-step plan, and you add notes to make it just right—repeat a few times until you're happy.

5
✅ Approve and build

Once the plan is perfect, you give the green light, and the AI builds everything exactly as planned, checking for issues along the way.

6
📁 Save your success

Use quick helper tools to check progress, wrap up, and archive the plan for your records.

🎉 Perfect project done

Your new software feature works flawlessly, and you have a record to learn from next time—no wasted effort!

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AI-Generated Review

What is deep-plan?

Deep-plan is a Python-based workflow skill for AI coding agents that enforces a research-plan-annotate-implement cycle, preventing agents from writing code until you've vetted a detailed plan. You kick it off with a simple /deep-plan command in tools like Claude Code or Cursor, and it outputs structured research on your codebase, a markdown plan with trade-offs, and a todo list for execution. It solves the chaos of prompt-and-pray AI dev by baking in human review loops, much like github llm structured output for reliable github structured plans.

Why is it gaining traction?

It stands out with optional interview phases for resolving ambiguities upfront, inline annotations you edit directly, and CLI scripts to check unaddressed notes or track progress—features that cut annotation cycles from 6 to 2-3. Developers hook on the uninterrupted implementation mode post-approval, plus archiving for project history, turning vague ideas into precise changes without the usual fix-everything-after loops. The Skills CLI install makes it dead simple across 35+ agents, echoing structured interview research vibes in deep plane planning.

Who should use this?

Fullstack devs maintaining mid-sized repos who use Cursor or Claude Code for features like API endpoints or migrations. Backend engineers tired of agents hallucinating schema changes. Solo makers prototyping with structured output needs, avoiding deep plane lift regrets on production code.

Verdict

Worth a spin for AI-heavy workflows—solid docs and agent integration punch above its 15 stars and 1.0% credibility score—but it's early-stage with no tests, so pair it with your own guardrails for non-trivial projects. Try the npx skills install; if structured plans click, it'll stick.

(178 words)

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