Total 56,899 skills, AI & Machine Learning has 9462 skills
Showing 12 of 9462 skills
Store and retrieve agent memories across jobs. Enables long-term context, learning from past interactions, and building agent knowledge bases. Based on OpenClaw's memory-core architecture.
Persistent key-value memory storage for agents. Store and recall information across conversations and sessions. Use when you need the agent to remember facts, preferences, or data between interactions.
Periodic self-monitoring and health check system for autonomous agents. Runs scheduled health diagnostics, reports system status, and performs proactive maintenance tasks.
Ingest Hermes agent history into the Obsidian wiki. Use this skill when the user wants to mine their past Hermes sessions for knowledge, import their ~/.hermes folder, extract insights from previous Hermes conversations, or says things like "process my Hermes history", "add my Hermes memories to the wiki", "ingest ~/.hermes", or "what have I worked on in Hermes". Also triggers when the user mentions Hermes memories, Hermes sessions, ~/.hermes/memories, or Hermes skill logs.
Reviewer-gated iterative fleet for headless `claude -p` or `codex exec` workers that run in cycles until a designated reviewer approves the output. Use when the work needs multiple rounds of iteration with a quality gate — a reviewer worker reads all worker logs, writes a verdict (lgtm | iterate | escalate), and the orchestrator decides whether to continue, pause, or stop. NEVER kills or restarts workers automatically; the operator owns all kill/pause decisions.
Orchestrates multi-day execution of complex tasks through milestones. Each milestone goes through plan-crafting, run-plan (worker-validator), and review-work phases with checkpoint/recovery. Triggers when the user says "long run", "start long run", "execute milestones", or "run all milestones".
Structured 8-factor vendor evaluation framework for AI marketing tools, based on Venkatesan & Lecinski's The AI Marketing Canvas (2nd ed., Stanford Business Books, 2026). Scores each tool against EA market accessibility, data requirements, integration compatibility, team capability, and total cost in UGX, then produces a shortlist with 30-day experiment briefs. Invoke when a client has completed the ai-readiness-diagnostic and is at Canvas Step 2 (Experimentation) and is ready to select specific AI tools for structured trials. Also invoke when a client wants to compare 2–4 named tools before purchasing or committing budget.
Run Megatron-LM (MLM) and Megatron Bridge training with mock or real data. Covers correlation testing, available recipes, and multi-GPU examples.
Configure AI Config targeting rules to control which variations serve to different users. Enable percentage rollouts, attribute-based rules, segment targeting, and guarded rollouts.
Validate and use selective and full activation recompute in Megatron Bridge to reduce GPU memory usage at the cost of extra compute.
Create and configure configs in LaunchDarkly. Helps you choose between agent vs completion mode, create the config, add variations with models and prompts, and verify the setup.
Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics