Devdocs CLI — AI skill for Claude Code
🚧 Structure your AI coding projects to effectively work with agents.
How to install Devdocs CLI
This entry records only its repository, not the path inside it, so there is no
exact command to give. Open mrexodia/devdocs-cli and copy the folder into
~/.claude/skills/, or the file into ~/.claude/agents/.
What Devdocs CLI does
🚧 Structure your AI coding projects to effectively work with agents. 🚧
Alternatives in AI
- AGENTS.md - Skill Seekers — Concise reference for AI coding agents 11.1k ★
- Claudecodeui — A desktop and mobile UI for Claude Code, Anthropic's official CLI for AI-assisted coding 9k ★
- Free AI Courses — Interactive course teaching Product Managers how to use Claude Code effectively 2.2k ★
README
devdocs-cli
**NOTE: This was just a (failed) experiment. Probably https://backlog.md is what you want instead. The [beads](https://github.com/steveyegge/beads) issue tracker is a massive PITA, do not recommend**
Structure your AI coding projects. Sets up issue tracking and documentation patterns that help agents maintain context across sessions.
Install
First, install the dependencies:
Then install devdocs-cli:
go install github.com/mrexodia/devdocs-cli@latest
Usage
cd your-project
devdocs init
This sets up:
- Beads (
bd) for issue tracking withno-git-opsconfig - devdocs/ directory for epics and reference documentation
- AGENTS.md with the methodology (appends if file exists)
- pi hooks for
bd primeinjection and slash commands
What is this?
A hybrid approach combining:
- bd (beads) - Dependency-aware issue tracking that lives in your repo
- devdocs/ - Epic tracking with design documents and archived learnings
When to Use What
| Scope | Approach |
|---|---|
| Single-session work | Just do it |
| Multi-session issue | bd create |
| Large initiative with phases | Epic (devdocs/ + bd epic) |
| Permanent reference knowledge | devdocs/.md |
The Methodology
1. Design First
Don't jump into coding. Research first in Claude/ChatGPT/Grok, then consolidate into `design.md`:
- Problem statement and goals
- API design or architecture
- Key decisions and rationale
- References to documentation
2. TDD for AI
Set up the test harness *before* implementation:
- Define what tests need to pass
- Use "golden master" testing where possible (compare against known-good output)
- Ensure
make testor equivalent produces actionable feedback - Then tell the agent: "work until the tests pass"
3. Scaf
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