Description
Build an agent skill (SKILL.md + supporting reference files) that walks developers through integrating StuntDouble into an existing LangGraph agent. The skill should take a developer from zero to a fully wired, testable mocking setup in a single guided session.
Motivation
The existing adoption skill is internal and org-specific. A public-facing skill lowers the barrier to entry for open-source adopters who want to add tool mocking but aren't sure where to start or how the pieces fit together.
Outcome Requirements
The skill must:
Discover the agent's existing tools, graph structure, and ToolNode usage automatically.
Scaffold a mocking directory containing:
A MockToolsRegistry setup module
Mock factory functions with signatures that match the real tools
An init.py that wires the registry
Wire create_mockable_tool_wrapper into the agent's ToolNode via awrap_tool_call, with a feature flag to disable in production.
Generate sample mock scenarios in JSON covering:
Static output cases
Input-matched cases using operators ($contains, $regex, $gt, etc.)
Dynamic placeholders ({{now}}, {{uuid}}, {{input.field}})
Generate sample mock factory functions showing:
Simple return-output pattern
Conditional logic with scenario_metadata
Set up CallRecorder with example assertions for captured tool invocations.
Produce a working test suite — a pytest file that exercises the mocked agent graph and passes out of the box.
Include a manual smoke-test command (e.g. curl) demonstrating scenario metadata injection.
Description
Build an agent skill (SKILL.md + supporting reference files) that walks developers through integrating StuntDouble into an existing LangGraph agent. The skill should take a developer from zero to a fully wired, testable mocking setup in a single guided session.
Motivation
The existing adoption skill is internal and org-specific. A public-facing skill lowers the barrier to entry for open-source adopters who want to add tool mocking but aren't sure where to start or how the pieces fit together.
Outcome Requirements
The skill must:
Discover the agent's existing tools, graph structure, and ToolNode usage automatically.
Scaffold a mocking directory containing:
A MockToolsRegistry setup module
Mock factory functions with signatures that match the real tools
An init.py that wires the registry
Wire create_mockable_tool_wrapper into the agent's ToolNode via awrap_tool_call, with a feature flag to disable in production.
Generate sample mock scenarios in JSON covering:
Static output cases
Input-matched cases using operators ($contains, $regex, $gt, etc.)
Dynamic placeholders ({{now}}, {{uuid}}, {{input.field}})
Generate sample mock factory functions showing:
Simple return-output pattern
Conditional logic with scenario_metadata
Set up CallRecorder with example assertions for captured tool invocations.
Produce a working test suite — a pytest file that exercises the mocked agent graph and passes out of the box.
Include a manual smoke-test command (e.g. curl) demonstrating scenario metadata injection.