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Project researcher

Skill GulajavaMinistudio/awesome-copilot-id/.codex/skills/project-researcher

A curated collection of custom agents, skills, rules, and prompts for GitHub Copilot, Google Antigravity, OpenCode, ChatGPT Codex, and Oh My Pi. Tailored for Indonesian developers to streamline SDLC workflows with AI.

Install
npx -y skills add GulajavaMinistudio/awesome-copilot-id --skill project-researcher

Assembled from the repository path, not quoted from the project. Check it against their README if it does not work.

What its author says it does

Copied from the file, not written here

Scans, analyzes, and documents the existing repository architecture, directories, and file purposes into docs/ARCHITECTURE.md.

The file declares its own license as MIT. That is the author’s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

5.3 KB, as published. Nobody here has run it

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Project Researcher Skill

Core Directives

  1. Language: Follow the language policy defined in the project's AGENTS.md.
  2. Strict Scope Boundary: You are an analyst and documentarian. Regardless of your active persona (e.g., GodModeDev, BrainstormingExplorer), while executing this specific skill, you are strictly forbidden from modifying application source code or tests. Your ONLY authorized outputs for this workflow are writing documentation in the /docs/ directory and updating AGENTS.md (or equivalent configuration files) to integrate references.
  3. No Session Lock: This is a Utility Skill. It does not have a standalone persona and does not trigger Session Lock. Any active agent can adopt and execute this workflow without losing their primary identity.

Overview

This skill outlines the workflow to explore an existing codebase, analyze its architectural structure, map out file/folder purposes, and generate a comprehensive ARCHITECTURE.md document. This is critical for context-sharing among other AI agents in the Spec-Driven Development ecosystem.

When to Use

  • When onboarding AI agents to an existing or legacy codebase.
  • When the directory structure has undergone significant refactoring.
  • When a user explicitly requests a breakdown of the repository's architecture.

When NOT to Use

  • Do NOT use this skill to generate Technical Specifications (use @SpecificationArchitect instead).
  • Do NOT use this skill for code implementation, debugging, or bug fixing.

Phase 1: Repository Exploration & Analysis Workflow

  1. High-Level Scan:
    • Priority Read: Read README.md first to understand the project's core purpose, tech stack, and setup instructions.
    • Context Gathering: Search for and read CONTEXT.md, memory.instructions.md, and any files in docs/adr/ to absorb existing architectural decisions and domain knowledge.
    • Configuration Scan: Read root-level configuration files (package.json, build.gradle, pom.xml, docker-compose.yml, tsconfig.json, .gitignore, etc.). This reveals the tech stack, entry points, and dependencies.
    • Monorepo Detection: Check for multiple package.json files, lerna.json, or a packages/ directory. If detected, analyze the architecture considering the monorepo structure.
    • Prior Work Scan: Read existing files in docs/ and spec/ directories to understand previously established architectures, API contracts, and business logic. Also, read any formatting rules in .codex/standards/ if you need to generate new documentation.
  2. Deep Directory Traversal:
    • List the root directories.
    • Dive into key source directories (e.g., src/, app/, lib/), traversing up to 3 levels deep. Only read individual files when their purpose cannot be inferred from directory structure alone.
    • Identify architectural patterns (e.g., MVC, Clean Architecture, Feature-Sliced Design).
  3. Purpose Inference:
    • Analyze what each specific folder does based on its contents and naming conventions.
    • Identify where core business logic, UI components, utilities, and assets reside.
  4. VERIFY: Present a brief summary of your findings to the user.
  5. APPROVAL: Wait for explicit user confirmation before generating the formal document.

Phase 2: Documentation Generation Workflow

  1. Check for the existence of ARCHITECTURE.md inside the /docs/ directory. (If /docs/ does not exist, create it). If ARCHITECTURE.md already exists, read its content first and ask the user whether to fully regenerate the document or update only the affected sections.
  2. Content: The file's content MUST adhere to the Mandatory Architecture Template. You MUST read this template from .codex/skills/project-researcher/references/ARCHITECTURE-TEMPLATE.md before generating the document.
  3. Post-Generation Offer: Once the file is successfully created or updated, you MUST explicitly ask the user (in the language specified by AGENTS.md): "The project architecture document has been successfully created at /docs/ARCHITECTURE.md. Would you like me to add a reference link to this document inside AGENTS.md (or other agent index files) so other agents can read it?"
  4. APPROVAL: Wait for user confirmation before proceeding to Phase 3.

Phase 3: Agent Index Integration Workflow (Conditional)

Execute this phase ONLY if the user approved the offer in Phase 2, Step 4.

  1. Locate the AGENTS.md file (or the primary agent configuration/index file in the .codex/ or root directory).
  2. Read the current contents of AGENTS.md.
  3. Inject a reference link to the newly created documentation. Add it under a relevant section (e.g., "Context Files", "Reference Documents", or "Project Map").
    • Format example: - **Project Architecture Map:** Read [/docs/ARCHITECTURE.md](/docs/ARCHITECTURE.md) to understand the directory layout and architectural constraints before suggesting code changes.
  4. Save the changes to AGENTS.md.
  5. Notify the user that the integration is complete and the project is now ready to be navigated by other agents.

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