Knowledge search for AI agents and their humans.

Diagram Maker transforms descriptions into visual diagrams across three formats: clean SVG for educational and process concepts, architecture SVG for software and infrastructure topology, and Excalidraw for editable collaborative sketches. The skill intelligently routes your request to the best output format and generates standalone, browser-ready files with semantic styling and clear layouts.

LEGO breaks the digital front-end chip-design flow — spec generation, RTL coding, testbench generation, simulation, debug — into six fixed steps and represents every reusable capability within those steps as a circuit skill : a seven-field unit (name, function, constraints, entry command, IO spec, schema, completion criteria) that any code agent can invoke like a subroutine. An automated three-stage pipeline builds the library from there — summarize capabilities, map to a workflow step, standardize the fields — mining more than 100 papers and 11 open-source EDA-agent projects (VerilogCoder, MAGE, Spec2RTL-Agent, AutoBench, the hierarchy-verilog line of work, RTLFixer, and others) down to 42 skills across 24 functional groups. A companion retrieval layer, Agent Skill RAG , skips embedding models entirely: it loads only short skill descriptions up front and pulls the full entry — symptom, root cause, fix — once a match is found, keeping lookup at sub-millisecond speed while letting new fixes get appended as plain text.

Tavily offers three complementary tools for AI-powered research: basic web search for quick lookups, advanced search with depth control and domain filtering, and content extraction from specific URLs. Choose the right tool based on your needs—start with simple search and escalate to extraction when you need full page content or targeted information.

Agent Skills — the filesystem-based packaging format that lets agents load domain-specific instructions and bundled scripts on demand — has never had a systematic security audit until now. This one splits a skill's life into four phases: Creation, Distribution, Deployment, Execution. Each opens a different door: authors get unrestricted control over SKILL.md and its scripts, marketplaces distribute without review, and the access a user actually grants at install time runs broader than what they think they're agreeing to. From that lifecycle view comes a threat taxonomy of seven categories and seventeen concrete attack scenarios, grouped into three layers: delivery and trust establishment, runtime attack, and persistent/lateral impact.

This skill connects to Trello's REST API to automate board, list, and card operations from the command line. Create cards, move tasks between lists, add comments, and archive items without leaving your terminal.

A nineteen-institution team spanning Zhejiang University, Alibaba, Tencent, Ant Group, and UCLA built SkillNet , infrastructure that creates, grades, and connects agent skills at scale instead of just hosting them. Skills get pulled from execution trajectories, GitHub repositories, PDFs and slide decks, and raw natural-language prompts through an LLM-driven pipeline, then run through deduplication (directory-structure and MD5 comparison) plus rule-based and model-based filtering before landing in one of ten functional categories. The distinguishing piece is a three-layer skill ontology : a taxonomy of categories and tags, a relation graph linking individual skills via similar_to, compose_with, belong_to, and depend_on edges, and a package library for modular deployment. Skills here aren't vetted one at a time and left to sit — they're wired into a queryable network.

Systematic Debugging enforces a disciplined four-phase approach to troubleshooting: investigate the root cause through error analysis and evidence gathering, analyze patterns by comparing working and broken code, form and test hypotheses scientifically, then implement fixes. The skill emphasizes that symptom-level repairs fail—you must trace data flow, instrument multi-component systems, and stop after three failed attempts to question architectural soundness.

COLLEAGUE.SKILL turns raw traces of a person — chat logs, work documents, email, screenshots, public research material, subtitles, plus a lightweight description — into a skill package through person-grounded trace-to-skill distillation : not a simulation of the person, but a bounded, editable artifact extracted from them. Each package splits into two coordinated tracks: a capability track for practices, mental models, and decision heuristics, and a behavior track for communication style, interaction rules, and correction history. The output is five content files — a combined SKILL.md plus independently invokable work.md/persona.md and work_skill.md/persona_skill.md — wrapped in manifest.json and meta.json for install and lifecycle state, on schema version 3. It installs across four agent hosts (Claude Code, OpenClaw, Codex, Hermes) and ships three domain presets : colleague for private workplace material, celebrity/public-figure for public evidence under source boundaries, and relationship for personal traces under consent and local-control rules.

Learn to organize Android and KMP projects using Clean Architecture principles, including module boundaries, dependency inversion, and layered data flow. Covers UseCase and Repository patterns, database integration with Room and SQLDelight, network clients with Ktor, and dependency injection setup with Koin or Hilt.

This skill captures architectural decisions made during coding sessions as structured ADR documents that live alongside your codebase. It detects decision moments, gathers context and alternatives, and maintains a searchable log so future developers understand the reasoning behind your architecture.