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Found 6,510 Skills
This skill installs and configures the **Tablestore Mem0** plugin for OpenClaw. Tablestore Mem0 uses Alibaba Cloud Tablestore as the vector store backend for mem0, providing persistent long-term memory for AI agents. Use this skill when the user wants OpenClaw to persist or manage long-term memory using Alibaba Cloud Tablestore as the backend. Triggers: "set up tablestore memory", "install tablestore mem0 plugin", "configure long-term memory with tablestore", "remember this".
Vercel Workflow DevKit (WDK) expert guidance. Use when building durable workflows, long-running tasks, API routes or agents that need pause/resume, retries, step-based execution, or crash-safe orchestration with Vercel Workflow.
Find the most relevant external agent skills for the current task, then submit grounded feedback about which skills were actually used and useful in the same session. Whenever you start a task, use this skill first.
Execute deep research on every item in a research outline, producing structured JSON per item and a final markdown report. Use after running /research to generate an outline. Reads outline.yaml and fields.yaml, launches parallel research agents in batches, validates output, generates a consolidated report, and supports resume on interruption. Trigger when the user says "start deep research", "research these items", "run the deep phase", "fill in the fields for each item", or "generate the research report".
Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports.
A skill that implements the SDD-RIPER methodology into strictly executable processes. It is applied in code/architecture tasks for "function-level and project-level CodeMap generation, full-modal requirement context bundling, Spec-driven R&D, and RIPER phase gate advancement", and is suitable for multi-round collaborative development with Claude/Codex/other CLI Agents.
PokeClaw (PocketClaw) — on-device Android AI phone agent using Gemma 4 via LiteRT-LM with tool calling, accessibility automation, and optional cloud models.
Review the current conversation and propose structured improvements to skills, documentation, and agent rules.
Optimizer that refines and professionalizes AI agent skills through real usage — saves tokens, eliminates redundancy, and tightens instructions so skills cost less to run. Learns from mistakes, reviews quality, and improves over time. Observes skill execution in the current conversation, analyzes up to four sources (conversation friction, file diffs, user feedback, static diagnostic) plus accumulated lessons, and proposes concrete improvements to the target skill's SKILL.md. Works with Claude Code and compatible SKILL.md-based agent frameworks. Use after executing any skill: `/skill-optimizer [name]` or `/skill-optimizer` to auto-detect. `--review` processes accumulated lessons.
Use this skill whenever calling agent-uml MCP tools (design_create, diagram_upsert, design_feedback, design_export) to render PlantUML diagrams on the collaborative canvas. Covers three tiers — rendering safety (syntax that prevents HTTP 400 blank canvas), conversation mechanics (when to push a version vs ask a question, what to write in the message parameter), and design effectiveness (decomposition thresholds, cross-diagram traceability, export readiness). Trigger even when the task seems simple — a missing `as alias` makes elements un-annotatable, and a skinparam mismatch makes diagrams unreadable on the warm
Artifact status + multi-phase orchestration. Scan what exists, check freshness, compose and track complex workflows across sessions. Not for skill routing (the agent does that proactively).
Deploys swarms of sub-agents for massive parallel data processing tasks. Unlike agent-army (which is for code changes), this is for DATA tasks -- processing 1000 documents, analyzing datasets, bulk content generation. Configurable swarm size, task distribution, result aggregation, progress tracking, and error recovery.