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Found 1,444 Skills
Security advisory feed with automated NVD CVE polling for OpenClaw-related vulnerabilities. Updated daily.
Generate and manage tmux sessions for parallel sub-coordinators using git worktrees. Used when all sections are ready for execution.
Launch and manage Codex Cloud tasks from the CLI, including detached background watchers that track completion. Use when users ask to run coding work in cloud/background agents, queue multiple cloud tasks, poll task status, fetch cloud diffs, apply cloud outputs locally, or pair cloud kickoff with `$cas` orchestration.
Analyse Datadog observability data including metrics, logs, monitors, incidents, SLOs, APM traces, RUM, security signals, and more. Use when asked to investigate infrastructure health, query metrics, search logs, check monitors, diagnose errors, or analyse any Datadog data.
Monitor and manage your OpenCode tasks using helper scripts.
Query GMGN on-chain tracking data — follow-wallet trade records, KOL trades, and Smart Money trades. Supports sol / bsc / base.
Distributed traces, spans, service dependencies, performance analysis, and failure detection. Query trace data, analyze request flows, and investigate span-level details.
한강홍수통제소 기반 현재 수위/유량을 관측소명 또는 관측소코드로 조회한다. 기본 경로는 k-skill-proxy의 han-river water-level endpoint다.
MetricFire integration. Manage data, records, and automate workflows. Use when the user wants to interact with MetricFire data.
Manage Railway deployments - view logs, redeploy, restart, or remove deployments. Use for deployment lifecycle (remove, stop, redeploy, restart), deployment visibility (list, status, history), and troubleshooting (logs, errors, failures, crashes). NOT for deleting services - use railway-environment skill with isDeleted for that.
Use when a user wants to add MCPCat analytics to their Python MCP server, install the mcpcat Python package, or integrate mcpcat.track() into an existing Python MCP server codebase.
Build trading systems in the style of Two Sigma, the systematic investment manager pioneering machine learning at scale. Emphasizes alternative data, distributed computing, feature engineering, and rigorous ML infrastructure. Use when building ML pipelines for alpha research, feature stores, or large-scale backtesting systems.