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
Quick Facts
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
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
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
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
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.