Agent-first documentation system that keeps project context compact, current, and routed for coding agents.
DocDriven is easy to set up with trust notes worth reviewing. Check agent compatibility and use-case fit before adding it to your workflow.
Repository setup guidance
npx skills add D3nnis72/DocDrivenRun the command in your terminal.
Confirm that the skill files were added to your agent workspace.
Check the README requirements before invoking the skill in your agent.
DocDriven helps AI coding agents understand your codebase by maintaining a compact, always-updated documentation map. It ensures agents read only the relevant context, make changes, update docs, and leave validation evidence, reducing guesswork and errors.
DocDriven is an agent-first documentation system designed to keep project context compact, current, and routed so that coding agents can understand, change, and maintain codebases with less guesswork. It treats documentation as project infrastructure, providing a shared context layer that agents continuously maintain. The system includes three main commands: docdriven-init scans the repository and creates a project-specific skill; docdriven-build creates a documentation graph with route files and canonical knowledge docs; docdriven-audit checks the graph for staleness, missing docs, and drift. DocDriven is built for long-lived projects and works with any coding agent that can execute shell commands and read Markdown files. It is installed via npx skills and operates entirely within the project's local files.
Looks usable, but maintenance, license, or security notes deserve a closer look.
Last commit was about 75 days ago.
0 GitHub stars indicate community interest.
0 open issues signal maintenance load.
MIT license detected.
Onboarding a new AI agent to a large codebase by providing a compact, up-to-date documentation map.
Maintaining documentation automatically as code changes, ensuring agents always have accurate context.
Auditing documentation health before major merges to catch stale routes and missing docs.
Reducing agent errors and guesswork in complex projects with many dependencies and configurations.
Enabling multiple agents to share a consistent understanding of project architecture and conventions.
Agent skills can change coding-agent behavior; review every SKILL.md and referenced script before installing.
Check shell, network, file-system, credential, and API-key requirements before running skill workflows.
0
Stars
0
Forks
0
Issues
MIT
License
A public repository of example skills for Claude, demonstrating how to create reusable instructions and resources for specialized tasks.
Adversarial AI bug hunter with auto-fix skill for Claude Code, Cursor, Codex CLI, GitHub Copilot CLI, Kiro CLI, Opencode, Pi Coding Agent, and more. Multi-agent pipeline finds security vulnerabilities, logic errors, and runtime bugs — then fixes them autonomously on a safe branch.
Agent-first documentation system that keeps project context compact, current, and routed for coding agents.
Developers using AI coding agents (e.g., Claude Code, Cursor) who want to improve agent accuracy., Tech leads managing long-lived projects with high complexity and frequent changes., DevOps engineers integrating AI agents into CI/CD pipelines for automated code maintenance.
The setup section provides repository-level starting guidance, not a guarantee of an independently verified installation. Check the official README and release notes.
Agent skills can change coding-agent behavior; review every SKILL.md and referenced script before installing. Check shell, network, file-system, credential, and API-key requirements before running skill workflows.
Similar or complementary options to evaluate include anthropic-skills, bug-hunter, context7.
Jun 7, 2026
2 security/trust notes recorded.
Setup difficulty is 2/5.
Up-to-date code documentation for LLMs and AI code editors, eliminating outdated or hallucinated API references.