AI Agent Frameworks

Software development skill, available on Zeplik

AI Agent Frameworks is a ready-to-run software development skill on Zeplik. Not for authoring a Zeplik skill (use skill-creator). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.

The AI Agent Frameworks skill loads automatically when your request matches it, or you can invoke it directly by typing /ai-agent-frameworks in any chat. It works with attachments, connectors, and any model that supports the task, so you get the same expert method every time without setting anything up.

What the AI Agent Frameworks skill can do

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How the AI Agent Frameworks skill works

/ai-agent-frameworks

Umbrella skill for building multi-agent systems and agent orchestration: LLM application architecture with LangChain/LangGraph, composing and sizing agent teams, decomposing tasks and coordinating parallel work, communication protocols between agents, context-driven development artifacts, track and workflow management for orchestrated pipelines, and quality guards for AI-generated work. The body of this skill is a dispatch table; the depth lives in references/. Pick the reference(s) matching the user's intent, read them, then answer. This lane covers designing and running agent systems in any framework or harness -- it is not the lane for authoring a Zeplik skill itself (that is skill-creator).

Dispatch table

IntentReference
LangChain 1.x / LangGraph application architecture: agents, memory, tools, chains, state graphsreferences/langchain-architecture.md (+ --details)
How many agents, which types, preset team configurations, sizing heuristicsreferences/team-composition-patterns.md (+ --preset-teams, --agent-type-selection)
Breaking work into tasks, dependency graphs, workload balancing, task descriptions for agentsreferences/task-coordination-strategies.md (+ --dependency-graphs, --task-decomposition)
Message protocols between agents: message types, plan approval, shutdown, anti-patternsreferences/team-communication-protocols.md (+ --messaging-patterns)
Parallel feature development: file ownership, conflict avoidance, integration/merge strategiesreferences/parallel-feature-development.md (+ --file-ownership, --merge-strategies)
Parallel code review across quality dimensions, dedup, severity calibration, consolidated reportsreferences/multi-reviewer-patterns.md (+ --review-dimensions)
Competing-hypothesis debugging with parallel investigators and root-cause arbitrationreferences/parallel-debugging.md (+ --hypothesis-testing)
Project context artifacts agents read (product.md, tech-stack.md, workflow.md, tracks.md), scaffolding, syncreferences/context-driven-development.md (+ --details, --artifact-templates)
Tracks as work units: spec.md, plan.md, track lifecycle for features/bugs/refactorsreferences/track-management.md (+ --details)
TDD workflow for orchestrated tasks: phase checkpoints, commits per task, verification protocolreferences/workflow-patterns.md (+ --details)
Auditing AI-generated code for hidden debt: thin error handling, orphaned resources, hallucinated packagesreferences/ai-debt-detector.md
Long-session drift, context compaction amnesia, behavioral self-enforcement for agentsreferences/session-guard.md
Precise UI edits from visual context: element selections, annotations, screenshotsreferences/visual-edit-precision.md

How to work

  1. Identify the intent and read the matching reference file(s) before answering; load the -- companion files when the main reference points to them. Orchestration questions usually chain references -- team sizing feeds task decomposition feeds communication protocol.
  2. Ask ONE clarifying question when the harness or framework is ambiguous: LangChain/LangGraph vs CrewAI vs a bespoke orchestrator, and whether agents share a workspace or run isolated.
  3. Produce concrete artifacts: architecture diagrams in text, LangGraph/LangChain code, task decomposition tables with dependency edges and file-ownership maps, message schemas, and context artifact templates the user can drop into their repo.
  4. Apply the quality guards proactively: when reviewing agent output, run the ai-debt-detector checklist; when designing long-running agents, bake in session-guard practices.
  5. Redirect out-of-lane requests: writing a Zeplik skill goes to skill-creator; general LLM app concerns like RAG or evals go to rag-implementation, llm-engineering, or llm-evaluation; dispatching parallel subagents inside THIS harness has dispatching-parallel-agents and subagent-driven-development as sibling lanes.

Usage

/ai-agent-frameworks $ARGUMENTS

How to use the AI Agent Frameworks skill

  1. Sign in to Zeplik

    Create a free Zeplik account or sign in. New accounts start with free credits, so you can try the AI Agent Frameworks skill right away.

  2. Describe your software development task

    Ask in plain language, or type /ai-agent-frameworks to invoke the skill directly. Zeplik recognizes the AI Agent Frameworks skill and applies its method.

  3. Review and refine the result

    Zeplik returns a clear, structured answer. Ask follow-ups in the same chat to refine it or take the next step.

Source and credit

Author
anthropic
License
MIT

Adapted from the open-source wshobson/agents project and tuned to run natively on Zeplik. View source on GitHub.

Frequently asked questions

What is the AI Agent Frameworks skill?
AI Agent Frameworks is a ready-to-run software development skill on Zeplik. Not for authoring a Zeplik skill (use skill-creator). Ask in plain language and Zeplik applies the skill's method for you inside the conversation, on whichever AI model you prefer.
How do I use AI Agent Frameworks on Zeplik?
Sign in to Zeplik and ask in plain language, or type /ai-agent-frameworks in any chat to invoke it directly. The skill applies its method and returns a result you can refine in the same conversation.
Which AI model does the AI Agent Frameworks skill use?
Any model you choose. Zeplik works across every model in one chat, so the AI Agent Frameworks skill runs on your preferred model for the task.
Where does the AI Agent Frameworks skill come from?
The AI Agent Frameworks skill is adapted from the open-source wshobson/agents project (MIT) and tuned to run natively on Zeplik. The original source is linked on this page.
How much does the AI Agent Frameworks skill cost?
Using the skill is free to start. You only spend Zeplik credits when the assistant runs, and new accounts begin with free credits.

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