npx agentspec-cli install 0589ac2f-4f75-4346-8754-1b287e69cac4 --file-type agents-md→ .agents/
# AI Agent Rules & Guidelines for Worker > **Purpose:** Centralized rules and guidelines for AI agents working on the Worker project --- ## ⚠️ CRITICAL: Read the Detailed Rules **This document is a QUICK REFERENCE ONLY.** For comprehensive, enforceable rules that you **MUST** follow, see: 📁 **[docs/agentRules/](docs/agentRules/)** - Detailed agent rules directory **Start here:** - **[docs/agentRules/README.md](docs/agentRules/README.md)** - Complete index and navigation - **[docs/agentRules/commitRules.md](docs/agentRules/commitRules.md)** - MANDATORY pre-commit checklist - **[docs/agentRules/documentationRules.md](docs/agentRules/documentationRules.md)** - **CRITICAL:** Documentation requirements including **NO CODE SNIPPETS IN MARKDOWN** (Rule 7) **The detailed rules include:** - ✅ Specific checklists you MUST follow before every commit - ✅ Architecture patterns (links to actual code, not duplicated examples) - ✅ Testing requirements and utilities - ✅ Common mistakes and how to avoid them - ✅ Enforcement criteria for all standards - ⚠️ **Rule 7: NO code snippets in .md files - link to actual code instead (Mermaid diagrams allowed)** ## Agent Configuration For agent structure and usage, see: - `docs/agents/overview.md` - `docs/agents/guidelines.md` - `agents/registry.yaml` --- ## Overview This document provides essential context, conventions, and guidelines for AI agents (like Codex, Claude, GitHub Copilot, etc.) contributing to the Worker repository. Worker is a reusable, repo-agnostic agentic workflow skeleton for consistent collaboration. ## Project Context ### What is Worker? Worker provides a structured way to run multi-step delivery flows with named agents, dependency-aware steps, and clear documentation rules. ## Workflow Diagram (Quick Reference) Grouped steps indicate dependency joins (parallel-ready), while correctness-first runs them sequentially. - Delivery flow: `plan -> supervise -> architectureReview -> implement -> (productReview + supervisorReview)` `-> (review + qa + docs) -> commit -> push` - Product flow: `research -> requirements -> sprintPlan` - Feature breakdown flow: `requirements -> epics -> stories -> tasks` - Agile delivery flow: `requirements -> epics -> stories -> tasks -> plan -> supervise -> architectureReview -> implement` `-> (productReview + supervisorReview) -> (review + qa + docs) -> commit -> push` - Tech debt flow: `plan -> supervise -> architectureReview -> implement -> supervisorReview` `-> (review + qa + docs) -> commit -> push` - Research spike flow: `framing -> research -> architectureReview -> recommendation -> nextSteps` ## Repository Structure - `agents/` - Agent registry, profiles, workflows. - `.firebender/` - Firebender agent definitions. - `scripts/agents/` - Runner scripts and wrappers. - `docs/` - Documentation, including `docs/agents/` and `docs/agentRules/`. - `tests/agents/` - Placeholder for agent tests and evals. ### Tech Stack Worker is language-agnostic. The consuming repository defines its own stack and tooling. ## Development Guidelines Follow the rules in `docs/agentRules/` and keep changes minimal and focused. Use the workflows in `agents/workflows/` as the source of truth for role sequencing. ## Commit Guidelines See `docs/agentRules/commitRules.md` for comprehensive commit message format and rules. ## Quality Standards - Code follows project conventions - Formatting/linting checks pass - Tests are written and passing - Public APIs have documentation ## Documentation Standards ### 1. Code Documentation - **Public APIs:** Must have KDoc comments - **Complex logic:** Inline comments explaining why, not what - **TODOs:** Include issue reference or explanation ### 2. Component Documentation Each component should document: - Purpose and use case - Parameters and their effects - Any special considerations - Example usage in KDoc (if complex) ### 3. Markdown Documentation - Use camelCase for markdown files (e.g., `agentRules.md`) - Include table of contents for long documents - Use code blocks with language specification for allowed blocks (commands/config) - Keep line length reasonable (80-120 chars) ## Related Documentation ⚠️ **IMPORTANT:** This document provides a high-level overview. For detailed, comprehensive rules that **MUST** be followed, see the `docs/agentRules/` directory: ### Essential Reading (READ THESE FIRST!) - **[README.md](docs/agentRules/README.md)** - Index and quick reference for all agent rules - **[workflowRules.md](docs/agentRules/workflowRules.md)** - **CRITICAL:** Work planning, self-review, implementation summaries, library versions, and documentation preferences - **[commitRules.md](docs/agentRules/commitRules.md)** - **CRITICAL:** Git commit message guidelines, pre-commit checklist, and atomic commit rules - **[documentationRules.md](docs/agentRules/documentationRules.md)** - **CRITICAL:** Documentation location, size limits, and naming conventions ### Architecture & Design - **[projectOverview.md](docs/agentRules/projectOverview.md)** - Project overview and constraints - **[architectureRules.md](docs/agentRules/architectureRules.md)** - Clean Architecture layers, module dependencies, and data flow patterns ### Development Standards - **[codingStandards.md](docs/agentRules/codingStandards.md)** - Code style guide, naming conventions, and documentation requirements - **[testingRules.md](docs/agentRules/testingRules.md)** - Testing frameworks, patterns, coverage requirements (70%+), and test utilities - **[featureDevelopmentRules.md](docs/agentRules/featureDevelopmentRules.md)** - Feature structure and state management guidance ### Why These Rules Matter These detailed rules provide: 1. **Specific enforcement criteria** - Clear do's and don'ts with examples 2. **Pre-commit checklists** - Mandatory steps before creating any commit 3. **Architecture patterns** - Proven patterns for maintainable systems 4. **Testing strategies** - Comprehensive testing approaches with code examples 5. **Common mistakes** - Anti-patterns to avoid with corrections **Before making any commit, you MUST:** - Review the relevant sections in `docs/agentRules/` - Follow the pre-commit checklist in `commitRules.md` - Verify documentation compliance per `documentationRules.md` - Ensure code quality per `codingStandards.md` - Check test coverage per `testingRules.md` ## Quick Reference Checklist Before completing any task, verify: ### Work Planning & Review (see workflowRules.md) - [ ] Discussion before implementation (for questions/suggestions) - [ ] Work planned before execution - [ ] Self-review checklist completed - [ ] Implementation summary provided - [ ] Only stable library versions (no alpha/beta) - [ ] Inline documentation preferred over README code snippets ### Code Quality - [ ] Code follows project conventions - [ ] Formatting/linting checks pass - [ ] Tests are written and passing - [ ] Public APIs have documentation ### Commit Standards - [ ] Commit message follows conventional format - [ ] No TODO comments without explanation or issue reference ## Getting Help When uncertain about: - **Architecture decisions:** Check `docs/agentRules/architectureRules.md` - **Code style:** Check `docs/agentRules/codingStandards.md` - **Testing:** Check `docs/agentRules/testingRules.md` ## Project Philosophy 1. **Reusability First** - Components should be flexible and reusable 2. **Consistency** - Follow established patterns and conventions 3. **Quality** - Never compromise on code quality or testing 4. **Documentation** - Well-documented code is maintainable code 5. **Simplicity** - Prefer simple, clear solutions over clever ones 6. **Performance** - Write efficient code 7. **Accessibility** - Build inclusive user interfaces ## Notes for AI Agents - Use workflows in `agents/workflows/` as the source of truth for sequencing. - Keep docs indexed and follow the documentation rules in `docs/agentRules/`. - When in doubt, **look at existing repo patterns** before introducing new ones. --- **Repository:** Worker
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You are an expert autonomous AI developer tasked with building a local Code Intelligence Engine and Model Context Protocol (MCP) server. Your goal is to parse a massive, legacy Python codebase into an Abstract Syntax Tree (AST), convert it into an in-memory Knowledge Graph, and expose tools for AI agents to query.
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