A local MCP server that turns AI-assisted coding sessions, Git changes, decisions, debugging, and token usage into feature-based Obsidian learning reviews.
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
Start and finish feature-based developer learning sessions with explicit goals, success conditions, human-owned decisions, and AI-allowed work. Use when the user starts a feature, asks to track a coding task, or wants the Learning MCP to measure a development session.
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.
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: