Results for “agent-actions”

Discovers, audits, repairs, adapts, and designs bounded AI-agent loops with explicit triggers, actions, verification, stopping conditions, guardrails, and handoffs.

Design and verify auditable human oversight, approval gates, escalation paths, and safe state transitions for AI agent workflows. Use when deciding which agent actions require review, adding approve/reject or dual-control flows, preventing unauthorized autonomous effects, creating decision records, reducing rubber-stamping, or recovering safely from rejected, expired, or failed actions.

Discover, audit, repair, adapt, and design bounded, verifiable AI-agent loops with explicit triggers, actions, stopping conditions, and guardrails.

Make a web app agent-ready — propose a WebMCP tool manifest, integrate, verify in a real browser, heal; unrelated code stays untouched. Use for "webmcpify", "add WebMCP", or "expose app actions to AI agents".

Build a fully automated AI-powered data collection agent for any public source — job boards, prices, news, GitHub, sports, anything. Scrapes on a schedule, enriches data with a free LLM (Gemini Flash), stores results in Notion/Sheets/Supabase, and learns from user feedback. Runs 100% free on GitHub Actions.

Builds a scheduled, AI-powered data collection agent that scrapes public sources, enriches results with Gemini Flash, and stores them in Notion, Sheets, or Supabase.

Skilled agent architecture replacing multi-agent system for RL training. Trigger when: (1) planning agent-guided training, (2) implementing tool-augmented LLM consultations, (3) comparing skilled vs multi-agent approaches, (4) designing simulate-verify loops for training, (5) implementing prompt evolution / learnable parameters, (6) understanding Claude Agent SDK integration in training, (7) debugging SkilledTrainer consultations or tool calls, (8) configuring agent safety bounds for training actions.

Fundamental patterns for effective agentic behavior. Teaches decomposition, tool orchestration, error recovery, context management, quality self-assessment, and knowing when to stop. Model-agnostic principles that make any agent more effective regardless of domain. Activate on: "how should I structure this agent", "agentic workflow", "agent patterns", "multi-step task", "tool orchestration", "/agentic-patterns", "decompose this", "agent best practices", "chain of actions", "when should the agent stop", "agent loop design". NOT for: creating agent infrastructure (use agent-creator), building DAGs (use windags-architect), specific tool implementation.

Agent skill for agent - invoke with $agent-agent

Optimizes long-running agent sessions with structured context compression, summarization, and durable handoff summaries that preserve decisions, files, risks, and next actions.

`analysis-agent`: use when actors, goals, preconditions, triggers, paths, guarantees, postconditions, or acceptance traces need modeling; skip when no use-case decision exists.

Add governance, safety, and trust controls to AI agent systems with policy enforcement, intent classification, and audit trails.

`analysis-agent`/`task-agent`: use when a use case coordinates authorization, domain work, transactions, or external effects; skip transport, storage, and rule-only work.

Use when creating, maintaining, or reviewing records of actions, decisions, changes, and events for compliance, accountability, and investigation purposes. This skill provides procedures for managing audit trails across all agent activities.

Agent skill for payments - invoke with $agent-payments

Use `analysis-agent` when engineering intent lacks desired behavior, boundaries, constraints, or completion signals. Skip requests with an accepted Engineering Brief and no-repo direct-answer work.

Agent skill for goal-planner - invoke with $agent-goal-planner

Decision-theoretic framework for agents embedded within the environments they model and act upon

Browse available agent personas and compose ad-hoc teams of up to 5 agents to work in parallel on a task, then synthesize their results.

Agent tooling for Motion Canvas — seek, screenshot, scene graph inspection, settings control, and rendering via HTTP API. Requires a browser with the editor open.

Orchestrates multi-agent project builds with persistent state, parallel execution, and atomic git commits.

Expert en agent loops (ARIS, Ralph, recherche autonome, build-score-budget, guardrails)

Automatic agent selection and intelligent task routing. Analyzes user requests and automatically selects the best specialist agent(s) without requiring explicit user mentions.

Rates an agent's own output on five axes — accuracy, completeness, clarity, actionability, conciseness — producing a structured scorecard with evidence and improvement suggestions.

Use with analysis-agent or task-agent for task-local transaction, isolation, and conflict decisions. Do not use without a transaction decision or as task owner.

This skill should be used when the user asks to "/do-agent", asks for "Execute complex tasks using multi-agent architecture with context protection", or needs the workflow previously provided by the /do-agent slash command.

Agent skill for planner - invoke with $agent-planner

Agent skill for architecture - invoke with $agent-architecture

This skill should be used when the user asks to "create an agent", "add an agent", "write a subagent", "agent frontmatter", "when to use description", "agent examples", "agent tools", "agent colors", "autonomous agent", or needs guidance on agent structure, system prompts, triggering conditions, or agent development best practices for Claude Code plugins.

Multi-Agent group chat collaboration system inspired by DingTalk/Lark. Enables AI agents to chat in groups, @mention each other, assign tasks, make decisions via voting, and collaborate. Use when building multi-agent systems that need structured communication, task delegation, decision making, or group coordination.