Total 30,439 skills, AI & Machine Learning has 4915 skills
Showing 7 of 4915 skills
List available large language models and send chat completion requests programmatically. Use this skill when you need to call an LLM within a snippet, including model comparison, visual understanding, batch inference, and model performance testing.
Qualify groups for non-recourse stock/crypto loans and institutional block trades based on Ovadiya criteria. Maintains provider anonymity during qualification. Notifies Erik @ Volume Finance upon qualification.
Overrides default LLM truncation behavior. Enforces complete HTML generation with zero placeholder patterns. Every landing page must be delivered as a complete, production-ready file. No shortcuts, no skeletons, no "add more as needed" patterns.
Delegate coding tasks to Codex, Claude Code, or Pi agents via background process. Use when: (1) building/creating new features or apps, (2) reviewing PRs (spawn in temp dir), (3) refactoring large codebases, (4) iterative coding that needs file exploration. NOT for: simple one-liner fixes (just edit), reading code (use read tool), thread-bound ACP harness requests in chat (for example spawn/run Codex or Claude Code in a Discord thread; use sessions_spawn with runtime:"acp"), or any work in ~/clawd workspace (never spawn agents here). Claude Code: use --print --permission-mode bypassPermissions (no PTY). Codex/Pi/OpenCode: pty:true required.
Use when executing or continuing a spec plan interactively. Triggers on: "spec go", "execute plan", "run plan", "continue plan", "work on plan", "start plan", "run the spec". Runs tasks with configurable breakpoints for review. Pass --bg for fully autonomous background execution.
Use when researching technical approaches before building. Triggers on: "explore options", "what are my options for", "research approaches", "compare solutions", "dev explore", "generate proposals", "help me decide between". Runs parallel proposal generation via subagents and outputs to .codevoyant/explore/.
Add Olakai monitoring to existing AI code — wrap your LLM client, configure custom KPIs, and validate the integration end-to-end