Results for “agent-workflow”

Use when a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.

Generate syntactically correct Claude Code configuration components (commands, subagents, skills, output styles) with optimal cross-component integration. Triggers on requests to create slash commands, agents, subagents, skills, output styles, Claude Code plugins, agentic workflows, or multi-agent pipelines. Ensures parameter validity per component type and semantic coherence across component references.

Orchestrates AI/ML development workflows covering LLM applications, RAG systems, AI agents, ML pipelines, and observability.

Inspect, plan, and invoke safe cleanup for runtime-manager-owned state without binding the workflow to a specific host orchestrator or agent provider.

Patterns and techniques for evaluating and improving AI agent outputs. Use this skill when: - Implementing self-critique and reflection loops - Building evaluator-optimizer pipelines for quality-critical generation - Creating test-driven code refinement workflows - Designing rubric-based or LLM-as-judge evaluation systems - Adding iterative improvement to agent outputs (code, reports, analysis) - Measuring and improving agent response quality

Design and operate bounded multi-agent workflows with task decomposition, dependency graphs, ownership, handoff contracts, shared-state controls, approvals, recovery, and synthesis. Use when a task contains genuinely independent workstreams, specialized roles, parallel research or implementation, reviewer-worker loops, or coordination problems that one agent should not execute sequentially.

Control cmux workspaces, panes, surfaces, and agent sessions safely from macOS terminal workflows.

Runs ComfyUI image-generation workflows from an AI agent via a CLI, handling imports, dependencies, execution, and history.

Coordinates four reasoning skills and an agent for structured thinking, problem solving, creative ideation, and learning.

Discover and install complementary skills from the inference.sh registry to expand AI agent capabilities.

Installs and configures Apache Airflow via pip or Docker, with guidance on constraints, requirements, and deployment options.

Authors and updates customization overrides for installed BMad skills. Use when the user says 'customize bmad', 'override a skill', 'change agent behavior', or 'customize a workflow'.

Install and operate Hermes Tweet, the native Hermes Agent X/Twitter plugin for endpoint discovery, read-only calls, and explicitly gated action-only workflows.

Plan, execute, document, and retest authorized security assessments of AI agents and multi-agent workflows using safe adversarial cases, synthetic identities, canaries, and evidence-based findings. Use when defining red-team rules of engagement, assessing prompt injection or excessive agency, testing tool and identity boundaries, evaluating memory or cross-agent attacks, scoring a campaign, or verifying remediation in an approved environment.

Handle files and binary data in n8n workflows correctly, covering the $binary vs $json split, reading/writing binary, preserving binary across transforms, and the agent-tool binary boundary.

This skill should be used when executing Bankr requests, submitting prompts to Bankr API, polling for job status, checking job progress, using Bankr MCP tools, or understanding the submit-poll-complete workflow pattern. Provides the core asynchronous job pattern for all Bankr API operations.

Publish Heroku apps into Salesforce and Agentforce with Heroku AppLink. Use when the agent needs AppLink publication workflows, OpenAPI publication preparation, publish a Heroku app to Salesforce, review AppLink publication state, inspect shared publication endpoints or shared objects, or reason about Agentforce, Flow, Apex, Data Cloud, service mesh, and AppLink user-mode behavior.

Threat-model and harden AI agents, RAG systems, assistants, and tool-using workflows against direct, indirect, stored, cross-agent, and multimodal prompt injection. Use when reviewing an agent architecture, isolating untrusted content, constraining tools and egress, protecting secrets, adding injection-focused tests, investigating a suspected injection incident, or documenting residual prompt-injection risk.

Automates git bisect workflows with custom test scripts and CI pipeline integration to identify regression-introducing commits.

Reference for building Tavily-powered search, extraction, crawling, and research into agentic workflows and RAG systems.

Build WinDAGs — the orchestration platform where AI agents accumulate genuine expertise through DAGs of skillful agents. Covers DAG design, execution engines, meta-DAG architecture, skill selection, dynamic mutation, visualization, and deployment. Activate on "windags", "agent DAG", "DAG of agents", "workflow orchestration", "agent pipeline", "dynamic DAG", "meta-DAG", "build windags", "implement windags". NOT for understanding WHY decisions were made (use windags-avatar), creating individual skills (use skill-architect), or managing skill libraries (use windags-librarian).

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Connects AI agents to MCP servers for tool discovery, invocation, and multi-server workflow orchestration.

Enforces a test-first workflow for features and bug fixes, guiding the red-green-refactor cycle with verification checklists.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Extend Hermes Agent by adding new tools (sync + async patterns), authoring in-repo skills, upgrading Hermes, and understanding s6 container supervision. Class-level umbrella for Hermes development workflows.

Protocol-level two-agent paper writing workflow. Use when the user wants Codex to plan and review from global context while DeepSeek writes or revises the manuscript through the global deepseek-agent skill, with muxing-style-review used for explicit prose style checks.

Provides a structured template for creating new agent skills, including workflow steps, input/output schemas, and quality gates.

Orchestrates AI/ML workflows for building LLM applications, RAG systems, AI agents, and ML pipelines, covering design, integration, observability, and security.

Auto-create new skills from repetitive workflows. When you notice yourself doing the same multi-step process repeatedly, extract it into a reusable SKILL.md that any agent can use.

Establishes a mandatory workflow for invoking relevant skills before any response, including clarifying questions, with priority ordering and red-flag guidance.

Defines quality standards and validation checklists for agent workflows, Python tools, and documentation, including automated checks and pre-commit gates.

Creative director and orchestrator for Pawbytes Creative Suites. Use when the user needs creative direction, campaign planning, brand setup, or help choosing the right creative specialist or workflow. Triggers: 'Aria', 'creative direction', 'campaign planning', 'brand setup', 'which creative agent should I use', 'creative suite'.

Triage issues through a state machine driven by triage roles. Use when user wants to create an issue, triage issues, review incoming bugs or feature requests, prepare issues for an AFK agent, or manage issue workflow.

Automate lead generation, web scraping, and social media data extraction workflows through PhantomBuster's cloud platform via Composio.