356건 · 관련도순
Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
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Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.
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This skill channels the reasoning of Yann LeCun, Chief AI Scientist at Meta and Turing Award winner. Use this skill whenever you are evaluating AI architectures, discussing the limitations of Large Language Models (LLMs), debating AI safety and regulation (anti-doomerism), or designing autonomous machine intelligence. It is highly relevant for topics involving self-supervised learning, open-source AI strategy, world models, physical grounding versus text-based learning, and objective-driven AI systems. Trigger this skill to apply his frameworks on abstract representation learning (JEPA) and energy-based models, even if the user doesn't explicitly name him.
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Applies the reasoning, principles, and frameworks of Jürgen Schmidhuber (LSTM co-inventor and deep learning pioneer). Reach for this skill whenever tackling problems involving sequence learning, artificial curiosity, intrinsic motivation, reinforcement learning architectures, or predicting long-term technological and cosmic evolution. Use this when discussing AGI timelines, the history and attribution of AI breakthroughs, data compression as learning, or when designing autonomous agents that must set their own goals. Trigger this skill for topics like recurrent neural networks, algorithmic information theory, open-source AI democratization, and evaluating true existential risks versus media hype.
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Analyze and optimize tweets for maximum reach using Twitter's open-source algorithm insights. Rewrite and edit user tweets to improve engagement and visibility based on how the recommendation system ranks content.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Systematic external reference searching across documentation, open-source repositories, and web resources. Use when working with unfamiliar libraries, APIs, or when needing production-quality implementation examples.
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Installs and configures ktx, the open-source context layer for data agents — runs ktx setup non-interactively with hidden CLI flags, configures database connections and embeddings, installs agent integration, and verifies readiness. Use when the user asks an agent to add ktx to a project, connect data sources, install agent rules, ingest schema, or troubleshoot a local ktx install.
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Generate, refine, compare, and when needed validate distinctive names for startups, AI products, developer tools, protocols, open-source projects, apps, product families, local businesses, services, companies, nonprofits, and other organizations. Use when asked to name or rename a business, brand, product, venture, shop, studio, or organization; derive a name from an existing project or concept; create a company and product naming system; compare candidates; or check company, domain, package, repository, marketplace, or trademark conflicts. Default to a fast no-browse creative pass; use current validation when the user requests availability or research, or is choosing a final name. Do not claim legal clearance, purchase domains, or present stale or unverified availability as fact.
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Analyze open-source license compatibility, obligations, and compliance risks across project dependencies. Use when the user requests license analysis or provides relevant inputs for this workflow.
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Full enhancement pipeline for an existing Lattice skill — rewrites a molecule or atom to modern-capable-model grade: restores degraded grammar, deduplicates, hardens gates and branching to one-unambiguous-action precision, verifies zero behavioral loss against a pre-capture diff, then runs the combined QA gate (skill-review, skill-tighten, skill-validate) and an independent fresh-context verifier, fixing everything found. Consumer-first: optimized for first-time open-source users on fresh repos with minimal .lattice/ setup, running capable models. Use when the user says 'enhance this skill', 'upgrade this molecule', 'modernize this skill', 'optimize this skill', 'make this skill production grade', 'full enhancement', 'polish this molecule like design-blueprint', or names a molecule/atom to overhaul. For gap-finding only use skill-review; for conventions only use skill-validate — this skill runs the whole loop end to end.
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Research GitHub user profiles including repositories, contributions, languages, stars, and activity patterns. Useful for developer talent sourcing, open-source community research, and technical due diligence on engineering teams.
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Add fast, local, typed decisions to any project with Laya, an open-source non-generative decision model (pip install laya). It classifies, routes, scores and answers yes/no questions about a piece of text, returning calibrated probabilities in roughly 20-35 ms on a laptop GPU, with no LLM call and no data leaving the machine. Use this skill whenever the user wants to classify or route text, triage tickets or emails, detect spam, phishing, toxicity or intent, put a guardrail in front of an agent or LLM, score something against a rubric, or replace an LLM-based classifier to cut latency or cost. Also use it when they mention Laya, Jev, a "System 1 model", "typed decisions" or a "decision model", even if they never name Laya.
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This skill should be used when the user asks to "find references", "참고할 만한 거", "similar implementation", "오픈소스 있나", "how others do this", "레퍼런스 찾아줘", or needs to find existing patterns (internal) and open-source examples (external) for implementing a feature.
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Hoyeon implementation reference discovery workflow for Codex. Use when the user invokes "$hoyeon-reference-seek" or asks for internal patterns, open-source references, GitHub examples, official docs, or comparable implementations. This adapter loads the canonical reference-seek skill and follows its Codex runtime surface.
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Analyze an open-source project from What/Why perspective (not how-it's-implemented). Use when the user says "/analyze-oss", "분석해줘 이 오픈소스", "이 레포 뭐하는거야", "analyze this repo", "what does X do", "이거 왜 쓰는거야", "이 라이브러리 분석", provides a GitHub URL and wants understanding, or asks to deeply understand an OSS project's purpose, value, target users, and usage flow. Clones the repo to ~/opensource-analysis/<repo-name>/ (git pull if already exists), dispatches parallel subagents per analysis lens, then synthesizes a What/Why-focused report in chat. Supports optional user-specific follow-up questions.
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Expert guidance for Excalidraw, the open-source virtual whiteboard library for creating hand-drawn style diagrams and sketches. Helps developers embed Excalidraw in React applications, build custom integrations, and leverage the API for programmatic diagram creation.
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Creates interactive data visualizations with Apache ECharts, the open-source JavaScript charting library. Use when a user asks to build charts, dashboards, or data-driven graphics using ECharts in React, Vue, or vanilla JavaScript applications, render a chart to SVG on the server, or upgrade from ECharts 5 to 6.
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dlt (data load tool) is an open-source Python library that loads data from APIs, files and databases into warehouses and lakes, inferring the schema and tracking incremental state for you. Use when a user asks to build a data pipeline in Python, load a REST API into DuckDB, BigQuery, Snowflake or Postgres, set up incremental or merge loading, enforce a schema contract, or replace a hand-written ETL script or a hosted ELT connector.
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Expert guidance for Evidence, the open-source BI framework that generates beautiful, interactive dashboards from SQL queries and Markdown. Helps developers build data reports as code, deploy them as static sites, and create self-service analytics without heavy BI tools.
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Dokploy is an open-source, self-hosted platform (an alternative to Heroku, Vercel and Netlify) that deploys applications, Docker Compose stacks and databases on your own server with Docker Swarm and Traefik, a web dashboard, automatic Let's Encrypt certificates and an HTTP API. Use when a user asks to "install Dokploy on a VPS", "deploy an app with Dokploy", "deploy a docker-compose project to Dokploy", "add a domain with HTTPS", "trigger a Dokploy deployment from CI", "create a Postgres database in Dokploy" or "set up zero-downtime deploys".
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Dokku is an open-source, single-server platform-as-a-service: it builds an app from a git push (buildpacks or a Dockerfile), runs it in Docker containers behind nginx, and manages domains, TLS, config and datastores through one CLI. Use when a user asks to "self-host a Heroku alternative", "set up git push deploys on a VPS", "deploy my app with Dokku", "add Postgres or Redis to a Dokku app", "get Let's Encrypt on Dokku", or "fix ports for a Dockerfile app on Dokku". Covers installation from the apt repository, deploys, environment variables, plugins, zero-downtime checks, storage and the k3s scheduler.
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Parse complex documents with IBM's open-source Docling library, which turns PDFs and Office files into structured Markdown or JSON. Use when a user asks to parse a document with tables, extract figures from a document, handle multi-column layouts, convert a complex PDF to structured data, extract content from academic papers, or process documents with mixed layouts. Handles tables, figures, headers, footers, and multi-column text.
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Dify is an open-source platform for building LLM apps such as chatbots, agents, workflows and RAG knowledge bases, and it exposes every published app as a REST API. Use when a user asks to self-host Dify with Docker Compose, configure Dify environment variables, call a Dify app or workflow API, upload documents to a Dify knowledge base, export or import a Dify DSL file, run a Dify app from the terminal with difyctl, or upgrade and back up a Dify instance.
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Cube is an open-source semantic layer that sits between a data warehouse and analytics applications and serves governed metrics over REST, GraphQL and SQL APIs. Use when a user asks to define cubes, measures and dimensions, add pre-aggregations, build a metrics API or embedded analytics, set up multi-tenant row-level security, or query Cube from React or the REST API.
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