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Found 9 Skills
Interactive hypothesis-driven debugging with documented exploration, understanding evolution, and analysis-assisted correction.
Append project fragment knowledge that is "too short to warrant a separate file but needs to be known by AI every time" to fixed sections of AGENTS.md / CLAUDE.md — such as special compilation flags, services that must be started before running, path pitfalls, command aliases, and environment variable conventions. Triggers: When the user says "make a note", "add to AGENTS", "save to CLAUDE.md", "the project requires X to compile", "must do Y every time from now on", or just encountered a project-specific setting that can be explained in one sentence.
Working memory management, context prioritization, and knowledge retention patterns for AI agents. Use when you need to maintain relevant context and avoid information loss during long tasks.
Creates an Architecture Decision Record following the Nygard format to document significant technical decisions, their context, and consequences. Use when making technical choices that affect system architecture, technology selection, or development patterns.
Persistent memory that survives across conversations. Automatically remembers important context (API specs, decisions, quirks, preferences) and saves session summaries. Searches past knowledge before starting new tasks. Responds naturally to phrases like "remember this", "what do you know about...", "save this session", or "what did we do last time". All local, zero network.
Document the pitfalls encountered or good practices discovered during this work into searchable learning documents, so that both AI and humans can look them up when similar tasks arise in the future. Two tracks: The pitfall track records experiences where "things should have worked but didn't" — bugs, configuration traps, environment issues, integration failures; The knowledge track records findings that "should be the default approach going forward" — best practices, workflow improvements, reusable patterns. Trigger scenarios: Proactively prompt for input when wrapping up feature-acceptance or issue-fix, or when the user says phrases like "document knowledge", "learning", "document learnings", "record this experience". Spec documents record what was done and how it was done, while learning documents record what pitfalls were encountered / what was learned — the two complement each other and are not interchangeable.
This skill should be used when engineering decisions are being made during code implementation. The Archivist enforces decision documentation as a standard practice, ensuring every engineering choice includes rationale and integrates with Architecture Decision Records (ADRs). Use when writing code that involves choosing between alternatives, selecting technologies, designing architectures, or making trade-offs.
Review the current session for errors, issues, snags, and hard-won knowledge, then update the rules/ files (or AGENTS.md if no suitable rule file exists) with actionable learnings.
Teach Claude ANY topic - code libraries, APIs, concepts, tools, methodologies, or domains. Researches via web and docs, then retains knowledge as a permanent skill. Use when user says "/learn <topic>", "learn about X", "teach yourself Y", "become an expert on Z". Examples - "/learn stripe" for payments, "/learn GTD" for productivity, "/learn israeli-tax-law" for domain knowledge.