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Found 7,433 Skills
Linear GraphQL patterns for Symphony agents. Use `linear_graphql` for all operations — comments, state transitions, PR attachments, file uploads, and issue creation. Never use schema introspection.
Set up a persistent AI agent gateway on macOS with Redis event bridge, heartbeat monitoring, and multi-session routing. Interactive Q&A to match your intent — from minimal (Redis + extension) to full (embedded daemon + Telegram + watchdog). Use when: 'set up a gateway', 'I want my agent always on', 'event bridge', 'heartbeat monitoring', 'agent notifications', or any request to make an AI agent persistent and reachable.
Register AI agents on Ethereum mainnet using ERC-8004 (Trustless Agents). Use when the user wants to register their agent identity on-chain, create an agent profile, claim an agent NFT, set up agent reputation, or make their agent discoverable. Handles bridging ETH to mainnet, IPFS upload, and on-chain registration.
Automated vulnerability scanner for agent platforms. Performs dependency scanning (npm audit, pip-audit), multi-database CVE lookup (OSV, NVD, GitHub Advisory), SAST analysis (Semgrep, Bandit), and agent-specific DAST hook execution testing for OpenClaw hooks.
Interact with GitLab via the glab CLI. Primary use case is MR review — fetches the diff, runs parallel code review + security review via specialist agents, then posts the result as a Thai comment on the MR. Also supports listing MRs, viewing MR status, checking CI/CD pipelines, approving MRs, and other glab operations. Trigger whenever the user provides a GitLab MR URL or says anything like "review MR", "ช่วย review MR นี้", "ดู MR ให้หน่อย", "review https://gitlab.../merge_requests/42", "check pipeline", "list open MRs", or any GitLab-related task.
Orchestrate a specialized software development agent team. Receive user requests, classify task type, select the matching workflow, delegate each step to specialist agents via the Agent tool, and assemble the final output. Use when the user needs multi-step software development involving architecture, implementation, testing, security review, or code review. Also use for production incident investigation — when the user reports a live system issue, service outage, pod crash, data anomaly, or needs root cause analysis using kubectl, psql, argocd, or docker. Trigger this skill whenever a task involves more than one concern (e.g., "add a new endpoint" needs BA + Architect + Developer + QA + Security), when the user mentions team coordination, agent delegation, or when the work clearly benefits from multiple specialist perspectives rather than a single implementation pass.
ALWAYS invoke this skill at the START of every session before doing any other work. Validates project health: governance rules, tool availability, memory directory, settings files, script permissions, .agents directory, and .beads/.gitignore hygiene. Remediates issues across all swain skills. Idempotent — safe to run every session.
Use when asked to trace existing codepaths or explicitly asked to run the code-explorer subagent.
This skill adds data(like resources) to OpenViking Context Database (aka. ov). Use when an agent needs to add files, data from URLs, or external knowledge during interactions. Trigger this tool when 1. is explicitly requested adding files or knowledge; 2. identifies valuable resources worth importing; 3. the user mentioned adding to OV/OpenViking/Context Database. This skill helps how to use CLI like `ov add-resource`, `ov add-skill` and `ov add-memory` to add resource data, skill files, memory files to OpenViking.
Enhance a plan with parallel research agents for each section to add depth, best practices, and implementation details
Deep dive into a book. Collect information from six dimensions including chapter structure, background key points, problem impacts, solutions, term index, and further reading through parallel sub-agents, then output a Markdown deep learning note after cross-analysis. Trigger words: Analyze the book XX, Study XX, Reading notes for XX, book analysis.
Build and deploy agentic finance applications on the Alva platform. Access 250+ financial data sources (crypto, equities, macro, on-chain, social), run cloud-side analytics, backtest trading strategies, and release interactive playbooks -- all from your AI agents.