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    Claude CodeClaude CodeCursorCursorCodex CLICodex CLIGitHub CopilotGitHub CopilotGemini CLIGemini CLI+20 more

    agent handoff contract generator

    1

    Audit and harden data contracts between agents in OpenAI Agents SDK, CrewAI, and LangGraph to prevent silent corruption.

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    agent handoff contract generator

    agent handoff contract generator

    Example session with this skill installed

    I have a LangGraph setup where nodes update the state using Command(update=...). Sometimes downstream nodes get missing fields and crash. Can you audit my state handoff?

    • Read your context and instructions
    • Compiled the agent handoff contract
    • Generated the document

    I have audited the LangGraph state handoff in your provided graph definition. The primary issue is the use of TypedDict for your AgentState, which provides static type hints but zero runtime validation. When an upstream node omits a field in Command(update=...), the graph continues with a corrupted state, causing the downstream crashes you observed.

    Handoff Contract Audit

    LocationFrameworkFindingSeverity
    AgentStateLangGraphTypedDict used; no runtime enforcementHigh
    research_nodeLangGraphMissing schemaVersion in update payloadMedium
    writer_nodeLangGraphMissing status field for outcome trackingMedium

    Hardened State Contract

    I have converted your TypedDict to a Pydantic BaseModel and added versioning. This ensures the graph raises a ValidationError immediately at the handoff point if a node returns malformed data.

    from pydantic import BaseModel, Field
from typing import List, Optional

class AgentState(BaseModel):
    # Added versioning and status for contract safety
    schema_version: str = Field(default="1.0.0")
    status: str = Field(default="pending")
    
    # Your existing fields now with runtime enforcement
    query: str
    documents: List[str] = Field(default_factory=list)
    draft: Optional[str] = None

# Update your node logic to return a validated dict
# Example for research_node:
# return Command(update=AgentState(
#     schema_version="1.0.0",
#     status="success",
#     query=state.query,
#     documents=found_docs
# ).model_dump())

    Next steps

    1. Replace the TypedDict definition of AgentState with the Pydantic BaseModel provided above.
    2. Initialize your graph with the new AgentState model.
    3. Update node return statements to use .model_dump() to ensure the payload is validated before the state update occurs.

    agent-handoff-contract-generator.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    What you get

    Identify silent data corruption points in multi-agent workflows.Generate Pydantic validators for OpenAI Agents SDK input_type fields.Hardening LangGraph state updates by replacing TypedDict with Pydantic.Add versioning and status fields to agent-to-agent communication.

    About this skill

    In multi-agent systems, data corruption happens when an upstream agent changes its output shape and a downstream agent silently receives the garbage. This skill audits handoff points in OpenAI Agents SDK, CrewAI, and LangGraph codebases to find these silent failure vectors. It identifies missing schemas and provides the framework-specific code to enforce runtime validation at the exact moment data crosses between agents.

    What it does

    • Handoff auditing scans code for handoff() calls, CrewAI Task definitions, and LangGraph Command updates missing explicit contracts.
    • Contract generation creates Pydantic models or typed schemas that include mandatory status and schemaVersion fields.
    • Runtime enforcement provides code that fails loud on malformed payloads, preventing silent state corruption.
    • Framework-specific wiring generates diffs for the buyer's actual call sites using the native mechanisms of their chosen SDK.

    How it works

    1. Framework identification detects if the codebase uses OpenAI Agents SDK, CrewAI, or LangGraph.
    2. Handoff inventory maps every point where data passes between agents, tasks, or nodes.
    3. Gap reporting lists every handoff point missing runtime validation, versioning, or status fields.
    4. Validator implementation generates the missing framework-native code to harden the data contract.

    Frameworks & tools

    This skill supports OpenAI Agents SDK, CrewAI, and LangGraph. It uses Pydantic for runtime validation and targets the native handoff mechanisms of each framework.

    Why this beats prompting it yourself

    Generic prompts often suggest framework-agnostic JSON schemas that require custom boilerplate to implement. This skill understands the specific internal validation hooks of each SDK and identifies the specific risk of using TypedDict in LangGraph for runtime safety.

    Use cases

    • Auditing a CrewAI project where unstructured text is passed between tasks without a schema.
    • Hardening OpenAI Agents SDK handoffs to ensure every transfer includes a status code.
    • Converting LangGraph TypedDict states into Pydantic models for strict runtime enforcement.
    • Root-causing why a downstream agent is failing due to malformed upstream data.

    Known limitations

    Does not audit control flow for deadlocks or infinite loops. Only supports OpenAI Agents SDK, CrewAI, and LangGraph. Requires access to the buyer's actual call-site code for accurate diffs.

    How to install

    Works the same in every agent - Claude, Cursor, Codex, Copilot and 20+ more.

    ~30 seconds
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