Loading...
Loading...
Found 127 Skills
Used when you need to perform Discover (reverse engineering) on legacy projects with existing code, consolidate repository facts into `.aisdlc/project/`, and you find that AI or teams frequently guess entry points and boundaries, have duplicate writing of indexes and details, or lack evidence chains leading to repeated rework.
Transforms knowledge sources into an Obsidian StudyVault. Two modes: (1) Document Mode — PDF/text/web sources → study notes with practice questions. (2) Codebase Mode — source code project → onboarding vault for new developers. Mode is auto-detected based on project markers in CWD.
Use when building a managed team skills library for a real stack. Map work to shelves, browse before curating, write meaningful `whyHere` notes, and create a starter pack once the first pass is solid.
Shared optimization guidance plus CuTe Python DSL overlays. Use when: (1) selecting optimizations for a CuTe Python DSL kernel, (2) deciding whether a finding is shared or cute-dsl-specific, (3) recording CuTe Python DSL implementation notes, (4) reviewing the knowledge layout for cute-dsl work, (5) mapping shared patterns to a CuTe Python DSL implementation surface.
Build and maintain an LLM-curated personal knowledge base — the "LLM Wiki" pattern from Andrej Karpathy's April 2026 gist. Use this skill whenever the user wants to ingest a source (paper, article, transcript, PDF, notes) into a persistent compounding knowledge base, ask a question against accumulated notes, lint or audit such a base, or initialize a new one. Trigger on phrases like "add this to my wiki", "ingest this paper", "compile this into the knowledge base", "what does my wiki say about X", "lint the wiki", "build a knowledge base from these documents", "research notes", "second brain", "personal knowledge base", or any reference to LLM Wiki / OmegaWiki. Trigger even when the user does not say "wiki" — if they are accumulating sources over time and want them organized, this applies. The skill scales — sharded indexes, atomic pages, YAML frontmatter, and a bundled search script keep the wiki from becoming a context bottleneck at hundreds or thousands of pages.
Use when working with Obsidian vaults, markdown notes with [[wikilinks]], ![[embeds]], callouts (> [!type]), YAML frontmatter/properties, #tags, block IDs (^id), ==highlights==, %%comments%%, Obsidian CLI commands (obsidian create/read/append/search/move/tags/daily/etc.), vault organization (PARA, MOC, flat+tags, Johnny Decimal), folder restructuring, daily notes, templates, task management, backlink analysis, or any file operations in an Obsidian vault directory. Trigger this skill whenever the user mentions Obsidian, .md files inside an Obsidian vault, knowledge base organization, or note-taking workflows — even if they don't explicitly say "Obsidian".
Karpathy LLM Wiki 패턴 기반 지식 관리 스킬. 코드 프로젝트와 옵시디언 노트 모두 지원. Raw Source(코드·문서)를 읽어 docs/wiki/에 누적형 지식베이스를 구축·유지한다. "wiki", "위키", "ingest", "인제스트", "wiki 점검", "wiki lint", "wiki 업데이트", "문서화해줘", "아키텍처 설명해줘", "어떻게 동작해?" 키워드로 트리거. qmd 검색 도구와 연동하여 토큰 절약 + 높은 검색 정확도 제공.
Li — Knowledge Manager for Ane's library and MEL Wiki. Use when Ane needs to catalog, retrieve, or reorganize documents in the personal knowledge library, or query/maintain the MEL Wiki. Handles INGEST, QUERY, and LINT operations. Does not answer domain questions — retrieves and organizes knowledge for other agents and Ane.
- **Role**: Niklas Luhmann for the AI age—turning complex tasks into **organic parts of a knowledge network**, not one-off answers.
Knowledge-base steward in the spirit of Niklas Luhmann's Zettelkasten. Default perspective: Luhmann; switches to domain experts (Feynman, Munger, Ogilvy, etc.) by task. Enforces atomic notes, connectivity, and validation loops. Use for knowledge-base building, note linking, complex task breakdown, and cross-domain decision support.
Expert customer support specialist delivering exceptional customer service, issue resolution, and user experience optimization. Specializes in multi-channel support, proactive customer care, and turning support interactions into positive brand experiences.
Brain knowledge base operations. The core read/write cycle: brain-first lookup, read-enrich-write loop, source attribution, ambient enrichment, back-linking. Read this before any brain interaction.