Catches common bugs in LangGraph state management, graph structure, and async patterns.
Works with
Identifies 20+ critical issues including state mutations, missing reducers, invalid conditional edge returns, and checkpointer configuration errors
Covers state schema problems like improper use of add_messages , full-state returns, and Pydantic models without annotations
Detects graph structure issues: missing entry points, unreachable nodes, incomplete conditional paths, and undeclared Command d
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionlanggraph-code-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches langgraph-code-review from existential-birds/beagle and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate langgraph-code-review. Access via /langgraph-code-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
1
total installs
1
this week
46
GitHub stars
0
upvotes
Run in your terminal
1
installs
1
this week
46
stars
When reviewing LangGraph code, check for these categories of issues.
# BAD - mutates state directly
def my_node(state: State) -> None:
state["messages"].append(new_message) # Mutation!
# GOOD - returns partial update
def my_node(state: State) -> dict:
return {"messages": [new_message]} # Let reducer handle it
# BAD - no reducer, each node overwrites
class State(TypedDict):
messages: list # Will be overwritten, not appended!
# GOOD - reducer appends
class State(TypedDict):
messages: Annotated[list, operator.add]
# Or use add_messages for chat:
messages: Annotated[list, add_messages]
# BAD - returns invalid node name
def router(state) -> str:
return "nonexistent_node" # Runtime error!
# GOOD - use Literal type hint for safety
def router(state) -> Literal["agent", "tools", "__end__"]:
if condition:
return "agent"
return END # Use constant, not string
# BAD - interrupt without checkpointer
def my_node(state):
answer = interrupt("question") # Will fail!
return {"answer": answer}
graph = builder.compile() # No checkpointer!
# GOOD - checkpointer required for interrupts
graph = builder.compile(checkpointer=InMemorySaver())
# BAD - no thread_id
graph.invoke({"messages": [...]}) # Error with checkpointer!
# GOOD - always provide thread_id
config = {"configurable": {"thread_id": "user-123"}}
graph.invoke({"messages": [...]}, config)
# BAD - add_messages expects message-like objects
class State(TypedDict):
messages: Annotated[list, add_messages]
def node(state):
return {"messages": ["plain string"]} # May fail!
# GOOD - use proper message types or tuples
def node(state):
return {"messages": [("assistant", "response")]}
# Or: [AIMessage(content="response")]
# BAD - returns entire state (may reset other fields)
def my_node(state: State) -> State:
return {
"counter": state["counter"] + 1,
"messages": state["messages"], # Unnecessary!
"other": state["other"] # Unnecessary!
}
# GOOD - return only changed fields
def my_node(state: State) -> dict:
return {"counter": state["counter"] + 1}
# BAD - Pydantic model without reducer loses append behavior
class State(BaseModel):
messages: list # No reducer!
# GOOD - use Annotated even with Pydantic
class State(BaseModel):
messages: Annotated[list, add_messages]
# BAD - no edge from START
builder.add_node("process", process_fn)
builder.add_edge("process", END)
graph = builder.compile() # Error: no entrypoint!
# GOOD - connect START
builder.add_edge(START, "process")
# BAD - orphan node
builder.add_node("main", main_fn)
builder.add_node("orphan", orphan_fn) # Never reached!
builder.add_edge(START, "main")
builder.add_edge("main", END)
# Check with visualization
print(graph.get_graph().draw_mermaid())
# BAD - missing path in conditional
def router(state) -> Literal["a", "b", "c"]:
...
builder.add_conditional_edges("node", router, {"a": "a", "b": "b"})
# "c" path missing!
# GOOD - include all possible returns
builder.add_conditional_edges("node", router, {"a": "a", "b": "b", "c": "c"})
# Or omit path_map to use return values as node names
# BAD - Command return without destinations (breaks visualization)
def dynamic(state) -> Command[Literal["next", "__end__"]]:
return Command(goto="next")
builder.add_node("dynamic", dynamic) # Graph viz won't show edges
# GOOD - declare destinations
builder.add_node("dynamic"Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
asyrafhussin/agent-skills
shadcn/improve
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
Keeps context tight: langgraph-code-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
langgraph-code-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
langgraph-code-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
langgraph-code-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
langgraph-code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
langgraph-code-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
We added langgraph-code-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: langgraph-code-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
langgraph-code-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend langgraph-code-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
showing 1-10 of 61