Loading...
Loading...
Found 6,689 Skills
Use this skill to establish and operate the model's inner workspace — the J-space — for any task that needs more than fluent output: multi-step or chained reasoning, planning, long-horizon and agentic work, competition-level problems, complex debugging, keeping many parts of a deliverable globally consistent, holding a goal or constraint through a long mechanical task, auditing what the model believes but has not said, calibrated confidence and error detection, suspicious or manipulative input, recovering from degenerating reasoning, and any moment the user asks the model to think harder, faster, deeper, or longer. Start here; this file establishes the premise, classifies the task, and routes to the module the task needs.
Prevent feature creep when building software, apps, and AI-powered products. Use this skill when planning features, reviewing scope, building MVPs, managing backlogs, or when a user says "just one more feature." Helps developers and AI agents stay focused, ship faster, and avoid bloated products.
Perform 12-Factor Agents compliance analysis on any codebase. Use when evaluating agent architecture, reviewing LLM-powered systems, or auditing agentic applications against the 12-Factor methodology.
Recognize, diagnose, and mitigate patterns of context degradation in agent systems. Use when context grows large, agent performance degrades unexpectedly, or debugging agent failures.
AI agent-friendly CLI RSS/Atom feed viewer with discovery, search, and OPML import/export. Use when an agent needs to manage RSS feeds, read articles, or run feed discovery from the terminal. Triggered by: "read RSS feeds", "view RSS", "RSS reader CLI", "rss-viewer", "discover RSS feeds", "import OPML", "search feeds", or "discover-search feeds".
🎰 Monad Casino - An AI-powered casino where OTHER AI agents gamble against each other. You're the house. The house always wins. Built for Moltiverse Hackathon.
Amazon Bedrock AgentCore multi-agent orchestration with Agent-to-Agent (A2A) protocol. Supervisor-worker patterns, agent collaboration, and hierarchical delegation. Use when building multi-agent systems, orchestrating specialized agents, or implementing complex workflows.
Beads (bd) distributed git-backed issue tracker for AI agents: hash-based IDs, dependency graphs, worktrees, molecules, sync, GitLab/Linear/Jira. Keywords: bd, beads, issue tracker, git-backed, dependencies, molecules, worktree, sync, AI agents.
Skill for working with the Lucid Agents SDK - a TypeScript framework for building and monetizing AI agents. Use this skill when building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication. Activate when: Building or modifying Lucid Agents projects, working with agent entrypoints, payments, identity, or A2A communication, developing in the lucid-agents monorepo, creating new templates or CLI features, or questions about the Lucid Agents architecture or API.
Assigns confidence scores to agent outputs based on multiple factors including source quality, consistency, and reasoning depth. Produces calibrated confidence estimates. Activate on 'confidence score', 'how confident', 'certainty level', 'output confidence', 'reliability score'. NOT for validation (use dag-output-validator) or hallucination detection (use dag-hallucination-detector).
Comprehensive guide for building full-stack applications with Convex and TanStack Start. This skill should be used when working on projects that use Convex as the backend database with TanStack Start (React meta-framework). Covers schema design, queries, mutations, actions, authentication with Better Auth, routing, data fetching patterns, SSR, file storage, scheduling, AI agents, and frontend patterns. Use this when implementing features, debugging issues, or needing guidance on Convex + TanStack Start best practices.
Use this skill whenever the agent has access to Linkup web search or fetch tools. Teaches the agent how to reason about query construction, choose search depth, write effective queries, select the right output type, use the fetch endpoint, and apply advanced techniques like sequential search and multi-query coverage. Applicable to any task involving web search, content extraction, company research, news retrieval, data enrichment, or real-time information gathering via Linkup.