MR
Mayur Rathi
@github
⭐ 34.1k GitHub stars

Codebase-Memory-Mcp

Codebase-Memory-Mcp is an code AI skill with a core value of Use when a configured codebase-memory-mcp server can assist with graph-backed code discovery, architecture orientation, symbol lookup, callers and callees, dependency or data-flow tracing, impact anal. It helps developers solve real-world problems in the code domain, boosting efficiency, automating repetitive tasks, and optimizing workflows.

Use when a configured codebase-memory-mcp server can assist with graph-backed code discovery, architecture orientation, symbol lookup, callers and callees, dependency or data-flow tracing, impact anal

Last verified on: 2026-08-02

Quick Facts

Category code
Works With Claude, GitHub Copilot
Source github/awesome-copilot
Stars ⭐ 34.1k
Last Verified 2026-08-02
Risk Level Low
mkdir -p ./skills/codebase-memory-mcp && curl -sfL https://raw.githubusercontent.com/github/awesome-copilot/main/skills/codebase-memory-mcp/SKILL.md -o ./skills/codebase-memory-mcp/SKILL.md

Run in terminal / PowerShell. Requires curl (Unix) or PowerShell 5+ (Windows).

Skill Content

# Codebase Memory MCP


Use the configured Codebase Memory graph as a discovery accelerator, not as the sole source of truth. Confirm graph-derived conclusions with source snippets or local files before editing code or making strong claims.


Workflow


1. Discover the Codebase Memory tools exposed by the current MCP client; clients may prefix or rename tool namespaces.

2. Call `list_projects` when available and use the exact indexed project name. If the repository is not indexed, continue with local exploration or ask before calling `index_repository` when graph access is important.

3. Before branch-sensitive or edit-sensitive conclusions, use `index_status` or `detect_changes` when available. After a branch switch, assume the index may be stale until checked. If freshness cannot be established, disclose that limitation and verify locally.

4. Use `get_architecture` once for orientation in an unfamiliar repository or subsystem. Do not repeat it for narrow follow-up questions.

5. Use `search_graph` for definitions, implementations, routes, classes, interfaces, callers, and related symbols. Prefer a natural-language query for discovery and a name or qualified-name pattern for known symbols. Narrow by label or path, set a result limit, and paginate or reduce scope when the response reports more results.

6. Use `search_code` or normal repository search for literal strings, configuration keys, test identifiers, error messages, and non-code files. Do not turn a precise text lookup into a broad graph query.

7. After graph search, use `get_code_snippet` with the returned qualified name. If source snippets are unavailable, open the local file before relying on the result.

8. Use `trace_path` for callers, callees, dependency paths, data flow, cross-service paths, and impact analysis. Include tests only when test coverage is part of the question.

9. Use `get_graph_schema` before `query_graph`. Reserve custom queries for multi-hop or aggregate questions that simpler tools cannot answer, and apply `LIMIT` or the tool's row limit.

10. When graph and checked-out source disagree, treat source as current and report likely index drift.


Safety and Fallbacks


- Do not install Codebase Memory or another third-party skill from this workflow.

- Do not call `delete_project`, ingest traces, update ADRs, or index a repository unless the user explicitly requested or approved the action; announce it before execution.

- Fall back to normal repository exploration when the MCP server, project, index, or required capability is unavailable; do not invent tool results or stop a task that can be completed safely without the graph.

🎯 Best For

  • Claude users
  • GitHub Copilot users
  • Software engineers
  • Development teams
  • Tech leads

💡 Use Cases

  • Code quality improvement
  • Best practice enforcement

📖 How to Use This Skill

  1. 1

    Install the Skill

    Copy the install command from the Terminal tab and run it. The SKILL.md file downloads to your local skills directory.

  2. 2

    Load into Your AI Assistant

    Open Claude or GitHub Copilot and reference the skill. Paste the SKILL.md content or use the system prompt tab.

  3. 3

    Apply Codebase-Memory-Mcp to Your Work

    Open your project in the AI assistant and ask it to apply the skill. Start with a small module to verify the output quality.

  4. 4

    Review and Refine

    Review AI suggestions before committing. Run tests, check for regressions, and iterate on the skill output.

❓ Frequently Asked Questions

Is Codebase-Memory-Mcp compatible with Cursor and VS Code?

Yes — this skill works with any AI coding assistant including Cursor, VS Code with Copilot, and JetBrains IDEs.

Do I need specific dependencies for Codebase-Memory-Mcp?

Check the install command and Works With section. Most code skills only require the AI assistant and your codebase.

How do I install Codebase-Memory-Mcp?

Copy the install command from the Terminal tab and run it. The skill downloads to ./skills/codebase-memory-mcp/SKILL.md, ready to use.

Can I customize this skill for my team?

Absolutely. Edit the SKILL.md file to add team-specific instructions, examples, or workflows.

⚠️ Common Mistakes to Avoid

Skipping validation

Always test AI-generated code changes, even for simple refactors.

Missing dependency updates

Check if the skill requires updated dependencies or new packages.

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