Results for “multi-step workflows”

CHAIN multiple existing skills into custom multi-step workflows. Programmable skill combinations with automatic handoffs. Create composite skills from building blocks with conditional logic.

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Define, validate, run, resume, and audit repo-local multi-step automations with current `asc workflow` and `.asc/workflow.json`, including step outputs and safe release/TestFlight workflows.

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Design and write deterministic multi-agent workflow scripts (.js files in .claude/workflows/) for Claude Code's Workflow tool. Use when a user wants to build, create, author, scaffold, or run a custom Claude Code workflow, orchestrate sub-agents (fan-out, pipeline, loop, judge-panel), or automate a repeatable multi-step task across fresh-context agents.

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Design and operate multi-agent orchestration patterns (ReAct loops, evaluator-optimizer, orchestrator-workers, tool routing) for LLM systems. Use when building or debugging agent workflows, tool-use loops, or multi-step task delegation; triggers: agentic, multi-agent, orchestration, ReAct, evaluator-optimizer, tool-use, handoff.

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Transition from static LLM chats to autonomous agents that execute multi-step tasks. Use this when you need to automate cross-platform reports (e.g., Snowflake to Google Docs), build self-service tools for non-technical teams, or create "anticipatory" engineering workflows that draft PRs based on Slack discussions.

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Browser Use is an open source browser automation framework for AI agents that turns websites into controllable interfaces for multi-step tasks. It combines a Python SDK, browser orchestration, and model integrations so agents can navigate, extract data, and complete workflows in real browsers.

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Delegate complete browser tasks to qz, an autonomous agent in the user's browser. Use when a separate agent should plan and carry out the browser work, such as cross-site research, comparisons, structured data collection, or multi-step web workflows. Provide the goal and expected deliverable; qz executes the task and returns results or questions that need user input.

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Programmatic web automation using Playwright Python — data extraction, form filling, multi-step workflows, auth session management, screenshot/PDF capture, and anti-detection patterns. Use for scripted automation tasks, not interactive MCP-based browser control.

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Build AI prompt chains and multi-step workflows for complex tasks. TRIGGERS - Use when user needs help with ai-chain-builder related tasks.

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Design multi-step AI agent architectures with tools, memory, and orchestration. TRIGGERS - Use when user wants to design AI agents, multi-agent systems, or autonomous AI workflows.

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Prompt engineering and task-specification skill for improving prompts, agent instructions, system messages, evaluation prompts, coding-agent tasks, multi-step workflows, and LLM output reliability. Use when rewriting vague prompts, adding constraints, reducing ambiguity, improving safety, creating reusable prompt templates, or converting goals into actionable agent instructions.

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Build agents on OpenAI's platform patterns — assistants-style threads, function calling, structured outputs, and multi-step tool workflows. Use when working with OpenAI models as the agent engine.

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Identify and exploit flaws in the core business rules and logic of web applications. This skill focuses on manipulation of application workflows, pricing, inventory limitations, and multi-step processes. Use this skill when bug hunting or testing e-commerce, banking, or SaaS platforms where standard technical vulnerabilities (e.g., XSS/SQLi) are not present but the application's logic is flawed.

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Business logic flaws are application vulnerabilities where valid functions are abused in unintended ways: price manipulation via hidden field tampering, workflow step-skipping, function call limit bypass (coupon reuse), process timing exploitation (race conditions on balance updates), and request forging via guessable/predictable parameters. Detect using Burp Suite proxy interception, HTTP POST/GET parameter analysis, and misuse-case testing against multi-step workflows. Tools: Burp Suite, OWASP ZAP.

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Business logic vulnerability playbook. Use when reasoning about workflows, race conditions, price manipulation, coupon abuse, state machines, and multi-step authorization gaps.

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Test AI agent systems for tool abuse, unauthorized actions, privilege escalation through tool chaining, and safety bypass via agentic workflows. Use this skill when assessing autonomous AI agents that use tool-calling (function calling, plugins, actions) to interact with external systems. Covers multi-step attack chains, implicit trust exploitation, and capability boundary testing for AI agents.

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One-command pre-execution review pipeline. Runs scope review → architecture review → security review → design review (auto-detects which apply) before executing any workflow. Surfaces only taste decisions for user approval. Auto-fixes obvious issues. Use before executing multi-step workflows, deploying, or when user says "review this plan", "check before running", "pre-flight", or "is this ready?". Runs CEO → eng → security automatically with encoded decision principles.

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Deterministic AI development workflow orchestrator. Manages task boards (.jonggrang/jonggrang-tasks.json), runs 16-phase pipelines (BUGFIX/SMALL/MEDIUM/LARGE), installs lifecycle hooks for Claude Code and OpenCode, and coordinates five specialist roles (Lead/Developer/Reviewer/TestLead/Tester). Use when planning features, executing multi-step development workflows, or orchestrating AI coding agents. Run via npx jonggrang.

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Reddit compound content operations skill. Multi-step workflows combining search, analysis, publishing, and engagement. Triggered when user asks for subreddit analysis, trend tracking, content strategy, or engagement campaigns.

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Design production-grade multi-agent workflows with clear pattern choice (sequential, parallel, hierarchical), handoff contracts, failure handling, and cost/context controls. Use when architecting a multi-step agent pipeline, choosing between single-agent vs multi-agent approaches, or refactoring an LLM workflow that suffers from context bloat or unreliable handoffs.

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Build AI agents with Hugging Face's SmolAgents framework. Use when creating code-executing agents, tool-calling agents, multi-agent systems, agentic RAG, text-to-SQL pipelines, web browsing agents, or any multi-step AI workflows. Covers CodeAgent, ToolCallingAgent, custom tools, MCP integration, memory management, secure code execution (E2B, Docker, Blaxel), and model configuration (HF Inference, LiteLLM, Transformers, Ollama).

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Controle saída de LLM com regex e gramáticas, garanta geração válida de JSON/XML/código, aplique formatos estruturados e construa fluxos de trabalho multi-etapas com Guidance - framework de geração restrita da Microsoft Research

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Use when writing or changing a model-facing tool definition (name, description, parameter schema, or its system-prompt section), and when designing skills, context loading, or multi-step workflows, to make information discoverable and use context efficiently.

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Build AI-powered browser agents that autonomously navigate, scrape, fill forms, and extract data from dynamic websites using LLM reasoning + Playwright. Use when building a scraper that must survive site redesigns, an agent that completes multi-step web workflows, or a replacement for brittle Selenium/Puppeteer scripts.

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