skills/llm-wiki/SKILL.md
Compiles and maintains a persistent LLM-written markdown wiki between immutable raw sources and answers—the Karpathy LLM Knowledge Base pattern. The agent writes and maintains the wiki; the human curates sources and reads it. Knowledge compounds instead of being re-derived each query. Triggers: llm wiki, persistent wiki, personal knowledge base, wiki maintenance, ingest sources, compound knowledge, index.md, log.md, Obsidian wiki, cross-references, Karpathy wiki pattern, compile wiki, knowledge base. Uses: Read, Glob, Grep, file edits, optional WebSearch, AskUserQuestion when schema or goals are ambiguous. Outputs: updated wiki pages, index, append-only log, citations on query, filed answers, visual outputs. Do NOT use for: mutating raw sources, one-off chat answers with no wiki artifact, or replacing a user-defined wiki schema without reading it first.
npx skillsauth add kvokov/oh-my-ai llm-wikiInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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Build and maintain a structured, interlinked markdown wiki that sits between curated raw sources and questions. The agent "compiles" raw source material into wiki pages—summaries, concept articles, entity pages, cross-links—so knowledge compounds rather than being rediscovered on every query. The human curates sources and reads the wiki; the agent writes and maintains it.
Directory layout is defined by the user's schema (often a file such as AGENTS.md, CLAUDE.md, or a dedicated wiki config). This skill defines behaviors, not fixed paths—always read the schema first.
Companion rules (detail): index-log conventions, optional tooling.
AskUserQuestion whether to create a minimal one (paths for raw sources, wiki root, index and log filenames, conventions). Do not invent large directory trees without confirmation.index.md first before reading any topic pages—use it to discover which pages to read. Then drill into linked pages. Answer with wiki-backed citations (page paths or section anchors). Always file the answer as a durable wiki page (new page or section) so the exploration compounds, in addition to any standalone output the user requested.ingest, query, or lint—never synonyms like "add", "update", "answer", "comparison", "audit", "check", or "review". Format:
## [YYYY-MM-DD] ingest | <title>## [YYYY-MM-DD] query | <title>## [YYYY-MM-DD] lint | <title>| Layer | Role | Who edits | |-------|------|-----------| | Raw sources | Articles, papers, repos, datasets, images—evidence | Human curates; agent reads only | | Wiki | Summaries, entity/topic pages, concept articles, synthesis, backlinks, visualizations | Agent writes and maintains; human reads and reviews | | Schema | Where things live, naming, categories, workflows | Human and agent co-evolve |
wiki/log.md using keyword ingest: ## [YYYY-MM-DD] ingest | <short title> followed by bullets listing touched files.wiki/index.md first—always start here to discover which topic pages exist and where they are. Do not jump directly to topic files.wiki/topics/foo.md) and raw source paths when citing evidence.wiki/topics/ or wiki/comparisons/). Update wiki/index.md with a link and one-line description for the new page. If the user also requested a standalone output file, produce that too—but always create the wiki page regardless.wiki/log.md using keyword query: ## [YYYY-MM-DD] query | <short title>WebSearch to impute missing data or suggest sources—do not fabricate citations).wiki/log.md using keyword lint: ## [YYYY-MM-DD] lint | <short title>Actual paths and categories come from the schema. For heading patterns, grep-friendly log lines, and index shape, see index-log-conventions.
Schema says: Wiki at notes/wiki/, raw at notes/raw/, index notes/wiki/index.md, log notes/wiki/log.md.
User: "Ingest notes/raw/2026-04-06-neural-plasticity.md."
Agent actions:
notes/wiki/topics/neural-plasticity.md, update notes/wiki/people/<researcher>.md if mentioned, and add a "See also" from notes/wiki/overview.md if that page exists.index.md with links and one-line descriptions for new or changed pages.log.md: ## [2026-04-06] ingest | neural-plasticity plus bullets listing touched files.notes/wiki/topics/sleep-and-memory.md.This example satisfies: raw immutability, multi-page integration, index + log updates, and contradiction handling.
User: "Compare neural plasticity vs. synaptic pruning for my review. Save the comparison as comparison.md."
Agent actions:
notes/wiki/index.md first to find relevant topic pages.notes/wiki/topics/plasticity-vs-pruning.md (durable wiki page). Also create comparison.md at the workspace root as the user requested.notes/wiki/index.md with a link and one-line description for the new wiki page.notes/wiki/log.md: ## [2026-04-06] query | plasticity vs pruning plus bullets listing the new page and pages consulted.This example satisfies: index-first file access, wiki-backed citations, durable answer filed into wiki, standalone output also produced, index updated, and correct query log keyword.
See optional-tooling for local search (e.g. qmd), Obsidian-adjacent options, and git—never assumed unless the user says so.
At small scale (~100 articles, ~400K words), the index + grep approach works well: the agent reads the index to navigate, follows links, and synthesizes answers without needing vector search or RAG. As the wiki grows, consider layering in additional tools (see optional tooling): local search engines over the wiki, CLI tools the agent can invoke for larger queries, or even synthetic data generation and finetuning to embed wiki knowledge into model weights. But always start simple—the index-first pattern is surprisingly effective.
AGENTS.md or product docs (use a repo-specific agent-init skill), or to fabricate sources—mark gaps and suggest real sources.tools
NestJS (Nest.js) production patterns for modules, controllers, providers, guards, interceptors, pipes, middleware, JWT, ValidationPipe, microservices, GraphQL, Bull queues, Prisma, and TypeORM. Triggers: NestJS, Nest.js, Nest module, dependency injection, class-validator DTO, exception filter, testing module, GraphQL resolver, Bull queue, microservice client. Uses: Read, Grep, Glob, Bash, WebSearch. Outputs: tier-ordered review checklists and/or concrete code edits with cited rule filenames. Do NOT use for: non-Nest backends (Express/Fastify only with no Nest integration), frontend-only frameworks, generating AGENTS.md, or toolchain setup unrelated to Nest.
development
Professional UI/UX design skill for React, Next.js, Tailwind CSS, React Native, and Flutter. Use when the user asks to create or polish UI components (modals, forms, tables, charts, navbars, sidebars, cards), design landing pages, build dashboards or admin panels, set up SaaS or mobile app screens, review or fix layout and accessibility issues, configure dark mode or responsive breakpoints, or establish a design system with tokens and component specs. Capabilities include: creating design-system token files and MASTER.md artifacts, generating responsive Tailwind layouts, scaffolding page-level component hierarchies, reviewing and fixing UI accessibility (a11y, WCAG), implementing React Native safe-area screens, and configuring Flutter ThemeData. Outputs design-system files (MASTER, page overrides, tokens, component specs) plus stack-faithful code. Do NOT use for: pure backend-only work with no UI impact, or inventing branding assets you do not have rights to use.
tools
Use this skill any time someone wants to create, scaffold, build, fix, improve, benchmark, or optimize a Tessl/Claude skill — even if they don't say 'tessl' explicitly. If the request involves making a new skill ('create a skill for X', 'build me a skill that does Y', 'scaffold a skill called Z'), fixing or completing an existing one (missing tile.json, broken repo integration, low eval scores, description not triggering), or running and iterating on evals, invoke this skill. The full workflow covers: structured interview → SKILL.md + tile.json + rules/ scaffolding → README/CI repo integration → tessl tile lint → optional Tessl CLI pipeline (skill review, scenario generate/download, eval run) → hand-authored evals or LLM-as-judge fallback → benchmark logging. Do NOT use for: editing application code, debugging, refactoring, writing general documentation, or creating presentations.
tools
Rigorous thirteen-part synthesis of a text or talk: deep summary, insights, structure, critique, framework rebuild, and CEO-level takeaways. Triggers: reading synthesis, synthesize this, deep dive, rigorous analysis, deconstruct, book analysis, article analysis, essay breakdown, intellectual synthesis, multi-dimensional analysis, executive summary of ideas, framework extraction. Uses: Read (and related file tools) for attached sources; WebSearch or WebFetch when comparands are missing or context is thin. Outputs: single structured markdown message with fixed section headers per rules/output-sections.md.