Total 31,795 skills, AI & Machine Learning has 5133 skills
Showing 12 of 5133 skills
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.
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
Let's Enhance integration. Manage data, records, and automate workflows. Use when the user wants to interact with Let's Enhance data.
Use this skill when crafting, iterating, or optimizing prompts for LLMs including zero-shot, few-shot, chain-of-thought, role prompting, structured output, and prompt chaining. Not for fine-tuning or training models. Not for evaluating model quality across benchmarks.
Real-time sports & events data for AI agents via Shipp. Use when the user wants live scores, schedules, or game events for NBA, NFL, NCAA Football, MLB, or Soccer — especially to power prediction market trading strategies on Polymarket or Kalshi using a MoonPay wallet.
Fleet orchestration for distributed coding agents across Azure VMs. Invoked as `/fleet <command>`. Covers all fleet operations: status, scout, advance, adopt, watch, snapshot, dry-run, start, add-task, queue, auth, dashboard, tui, and more. Use when: user mentions fleet, agents, VMs, sessions, or asks "what are my agents doing".
This skill should be used when the user needs to customize a resume for a specific job posting while maintaining truthfulness. Use when adapting an existing resume to match a job description, repositioning experience for a new role, or aligning resume language with target role keywords and requirements.
Autonomous co-pilot — agent formulates goal from natural language, enables lock mode with SessionCopilot reasoning, works until goal is achieved.
Encodes a continuous improvement loop for goal-seeking agents: EVAL, ANALYZE, RESEARCH (hypothesis + evidence + counter-arguments), IMPROVE, RE-EVAL, DECIDE. Auto-commits improvements (+2% net, no regression >5%) and reverts failures. Works with all 4 SDK implementations. Auto-activates on "improve agent", "self-improving loop", "agent eval loop", "benchmark agents", "run improvement cycle".