The definitive resource for Agent Skills - modular capabilities revolutionizing AI agent architecture
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Updated
May 14, 2026
The definitive resource for Agent Skills - modular capabilities revolutionizing AI agent architecture
An instruction layer for AI coding agent
A context harness for AI agents: all your scattered context — code, memory, docs, databases, SaaS — in one searchable, browsable, file-like interface.
Local-first persistent memory for OpenAI Codex: governed recall, progressive disclosure, no hosted vector database.
🌿 Prune your AI agent's context window. Reduce token usage by 70-95% with hierarchical memory. Replace flat MEMORY.md with a bonsai-shaped domain tree. Progressive disclosure, zero dependencies. Works with OpenClaw and any LLM agent framework.
Claude Code skill for optimizing oversized CLAUDE.md files using progressive disclosure
Palestra técnica sobre Spec-Driven Development (SDD) com GitHub Copilot
Persistent project-scoped knowledge base for Claude Code and Codex: quick and deep research depths, contrarian pass, source-independence rules measured on DeepResearch Bench II. Tested end to end on Haiku agents and a local 35B under noob-cli. Installs via npx skills add, /plugin install, codex marketplace, or git clone.
Progressive file disclosure for agentic AI - explore files hierarchically with plugin-based analyzers
Search 1,000 MCP tools through two interfaces in DeepSeek Harness. Load exact schemas on demand and keep structured results usable across tool calls.
Opt-in Codex Skill and plugin for reliable coding: reactive failure recovery, elastic agent teams, task DAGs, one canonical writer, and assured delivery.
Progressive tool discovery for DeepSeek Harness — tiny stable surface, searchable catalog, real pipeline execution, context cache intact.
Desktop app (Tauri 2 + SolidJS) to compose Claude Code workspaces with curated skill packages, MCP servers, hooks, commands & CORE-documenten — OpenAEC Foundation
Progressive disclosure retrieval for long-horizon AI agents over personal knowledge bases. Three-tier model: hot cache → FTS5 index → full read. Local-first, zero dependencies.
A shared, resumable AI development workflow backed by project-local specs, rules, and change records. Skills guide agents; the CLI handles deterministic steps.
PORTFOLIO / MAINTENANCE prototype for behavior-based tags, anonymous matching, and progressive identity unlock; Mock connectors, local only.
MCP server giving LLM agents a seven-verb API over papers, documents, code, state, patents, and cached web/Wolfram/YouTube tool calls
Progressive disclosure OS with 10 specialized routes, critical thinking, and verification gates. Supports Claude Code, Codex, Qoder.
🧭 Keep AGENTS.md lean. Route coding agents to task-specific docs with byte budgets and integrity checks.
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