GitClaw – An AI assistant that runs in GitHub Actions

Executes AI-driven logic directly within GitHub Actions runners without external service dependencies, using the GitHub API to read workflow context (commits, PRs, issues) and trigger actions based on LLM reasoning. Integrates with GitHub's event system to respond to push, pull_request, and issue events, enabling inline decision-making without webhook overhead or external orchestration platforms.

Analyzes pull request diffs and code changes using an LLM to generate structured review feedback, identifying potential bugs, style violations, and architectural concerns. Operates on the full PR context (changed files, commit messages, base branch) and uses chain-of-thought reasoning to produce actionable review comments that map to specific line numbers in the diff.

Processes incoming GitHub issues using LLM-based semantic analysis to automatically assign labels, priority levels, and assignees based on issue content, title, and description. Uses natural language understanding to categorize issues (bug, feature request, documentation, etc.) and route them to appropriate team members without manual triage overhead.

Analyzes commit messages and code changes together to validate commit quality, detect incomplete commits, and suggest improvements using LLM reasoning. Checks for conventional commit format compliance, validates that commit messages accurately describe the changes, and flags commits that mix multiple concerns or lack proper documentation.

Retrieves relevant code context from the repository using GitHub API queries and file system access within the Actions runner, enabling LLM prompts to include semantically relevant code snippets, function definitions, and related files. Uses file path patterns, import analysis, and semantic similarity to surface context without full codebase embedding.

Orchestrates multi-step LLM reasoning chains within GitHub Actions by persisting intermediate results to workflow artifacts or repository state, enabling complex decision-making that spans multiple workflow jobs. Uses GitHub's artifact storage and environment variables to maintain reasoning state across steps without external databases.

Uses LLM reasoning to make decisions about which GitHub Actions to trigger, enabling conditional workflow execution based on AI analysis. Parses LLM output to determine next steps (deploy, notify, label, etc.) and invokes appropriate GitHub API calls or workflow dispatch events to execute those actions.

Interprets natural language commands in PR comments, issue comments, or commit messages to perform repository operations (create branches, merge PRs, apply labels, etc.). Uses LLM to parse user intent from natural language and map it to GitHub API operations, enabling conversational automation without explicit command syntax.