LangGraph Workflow Development Skill
SkillDev toolsGuide for LangGraph workflows in AI-powered test automation
Use LangGraph Workflow Development Skill in Claude, ChatGPT or Ahel Desktop
Free. Sign in, add LangGraph Workflow Development Skill and connect your AI. About a minute.
Also: Claude Code · Cursor · Codex
Then ask your AI: use the LangGraph Workflow Development Skill
Details
Instructions available. Your AI can read the instructions. Execution depends on the setup they require.
Account requirements not reviewed. Check the skill instructions before use; Ahel provides instructions and does not run this skill.
No other account needed.
Add Ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
What this skill tells your AI
The instructions your AI receives, as published by aiqualitylab/ai-natural-language-tests in .github/skills/langgraph-workflow-development/SKILL.md and read by Ahel’s review.
Workflow Pipeline
Step2FetchData -> Step3SearchPatterns -> Step4GenerateTests -> Step5RunTests(optional) -> END
State Schema
@dataclass
class TestState:
requirements: List[str]
output_dir: str
use_prompt: bool
approve: bool = False
framework: str = "cypress"
url: Optional[str] = None
run_tests: bool
llm_provider: str = "openai"
test_data: Optional[Dict] = None
context: str = ""
similar_patterns: List = field(default_factory=list)
generated_tests: List = field(default_factory=list)
test_results: Optional[Dict] = None
run_id: str = ""
Building Workflow
from langgraph.graph import StateGraph, END
def create_workflow() -> StateGraph:
workflow = StateGraph(TestState)
workflow.add_node("step_2", step_2_fetch_test_data)
workflow.add_node("step_3", step_3_search_similar_patterns)
workflow.add_node("step_4", step_4_generate_tests)
workflow.add_node("step_5", step_5_run_tests)
workflow.set_entry_point("step_2")
workflow.add_edge("step_2", "step_3")
workflow.add_edge("step_3", "step_4")
workflow.add_conditional_edges("step_4", should_run_tests, {"run_tests": "step_5", END: END})
workflow.add_edge("step_5", END)
return workflow.compile(checkpointer=WORKFLOW_CHECKPOINTER)
Node Pattern
def step_4_generate_tests(state: TestState) -> TestState:
# Current implementation uses RunnableLambda for prompt generation
# and BaseOutputParser for code-fence extraction before file save.
return state
Mode Selection
# Prompt mode applies only when framework supports it (currently Cypress)
use_prompt_mode = state.use_prompt and FRAMEWORK_CONFIG[state.framework]["supports_prompt_mode"]
Context Sources
# URL analysis in step_2 builds test_data and context block
# Similar pattern retrieval in step_3 augments context
# Final context is consumed by step_4 prompt template rendering
Error Handling
def node_with_error_handling(state):
try:
result = process(state)
return result
except Exception as e:
state.error = str(e)
return state
Signals
- GitHub stars
- 23
- Forks
- 7
- Last commit
- Oct 2026
Advanced
- Item type
- skill
- Key
langgraph-workflow-development- Source
- github.com/aiqualitylab/ai-natural-language-tests
github.com/aiqualitylab/ai-natural-language-tests