Skill Codex
Critically compare a feature architecture with a simpler option and a more scalable option, checking responsibility boundaries, dependency direction, testability, operational cost, and premature abstraction. Use for architecture reviews, technology choices, ADRs, or “is there another way?” questions.
Skill Codex
Coach debugging through symptom isolation, user-authored hypotheses, minimal verification experiments, and evidence-backed narrowing, then record the attempt in Learning MCP. Use when a feature has an error, failing test, unexpected behavior, or the user wants to train debugging ability.
Skill Codex
Review a Learning MCP feature or staged commit using compact evidence, separating human and AI ownership, tracing code flow, challenging architecture choices, and recommending up to three study topics. Use for feature completion, commit review, learning review, or AI-dependence assessment.
Skill Codex
Automatically track substantive code-changing work as Learning MCP features, including goals, Git evidence, decisions, debugging, checkpoints, draft review, and completion. Use by default for feature work, bug fixes, refactors, performance changes, and test-system changes even when the user does not mention Learning…
Skill Codex
Turn verified Learning MCP feature reviews into evidence bundles for the existing book writer and read-only reviewer. Use when the user wants to convert development experience, architecture decisions, or debugging cases into an Obsidian book chapter candidate.
MCP server Claude CodeCodexCursor +2
MCP server "learning-mcp" as configured in giseungNoh/AI-learning-mcp. Runs locally from the learning-mcp Python package.
At most 3 mods per repository are shown here, and a mod shipped inside a plugin is left to that plugin's page — the rest are on their repository pages: