deepagents-code-review
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.
Best use case
deepagents-code-review is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.
Teams using deepagents-code-review should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/deepagents-code-review/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How deepagents-code-review Compares
| Feature / Agent | deepagents-code-review | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Reviews Deep Agents code for bugs, anti-patterns, and improvements. Use when reviewing code that uses create_deep_agent, backends, subagents, middleware, or human-in-the-loop patterns. Catches common configuration and usage mistakes.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
Related Guides
Best AI Skills for Claude
Explore the best AI skills for Claude and Claude Code across coding, research, workflow automation, documentation, and agent operations.
AI Agents for Coding
Browse AI agent skills for coding, debugging, testing, refactoring, code review, and developer workflows across Claude, Cursor, and Codex.
Cursor vs Codex for AI Workflows
Compare Cursor and Codex for AI coding workflows, repository assistance, debugging, refactoring, and reusable developer skills.
SKILL.md Source
# Deep Agents Code Review
When reviewing Deep Agents code, check for these categories of issues.
## Critical Issues
### 1. Missing Checkpointer with interrupt_on
```python
# BAD - interrupt_on without checkpointer
agent = create_deep_agent(
tools=[send_email],
interrupt_on={"send_email": True},
# No checkpointer! Interrupts will fail
)
# GOOD - checkpointer required for interrupts
from langgraph.checkpoint.memory import InMemorySaver
agent = create_deep_agent(
tools=[send_email],
interrupt_on={"send_email": True},
checkpointer=InMemorySaver(),
)
```
### 2. Missing Store with StoreBackend
```python
# BAD - StoreBackend without store
from deepagents.backends import StoreBackend
agent = create_deep_agent(
backend=lambda rt: StoreBackend(rt),
# No store! Will raise ValueError at runtime
)
# GOOD - provide store
from langgraph.store.memory import InMemoryStore
store = InMemoryStore()
agent = create_deep_agent(
backend=lambda rt: StoreBackend(rt),
store=store,
)
```
### 3. Missing thread_id with Checkpointer
```python
# BAD - no thread_id when using checkpointer
agent = create_deep_agent(checkpointer=InMemorySaver())
agent.invoke({"messages": [...]}) # Error!
# GOOD - always provide thread_id
config = {"configurable": {"thread_id": "user-123"}}
agent.invoke({"messages": [...]}, config)
```
### 4. Relative Paths in Filesystem Tools
```python
# BAD - relative paths not supported
read_file(path="src/main.py")
read_file(path="./config.json")
# GOOD - absolute paths required
read_file(path="/workspace/src/main.py")
read_file(path="/config.json")
```
### 5. Windows Paths in Virtual Filesystem
```python
# BAD - Windows paths rejected
read_file(path="C:\\Users\\file.txt")
write_file(path="D:/projects/code.py", content="...")
# GOOD - Unix-style virtual paths
read_file(path="/workspace/file.txt")
write_file(path="/projects/code.py", content="...")
```
## Backend Issues
### 6. StateBackend Expecting Persistence
```python
# BAD - expecting files to persist across threads
agent = create_deep_agent() # Uses StateBackend by default
# Thread 1
agent.invoke({"messages": [...]}, {"configurable": {"thread_id": "a"}})
# Agent writes to /data/report.txt
# Thread 2 - file won't exist!
agent.invoke({"messages": [...]}, {"configurable": {"thread_id": "b"}})
# Agent tries to read /data/report.txt - NOT FOUND
# GOOD - use StoreBackend or CompositeBackend for cross-thread persistence
agent = create_deep_agent(
backend=CompositeBackend(
default=StateBackend(),
routes={"/data/": StoreBackend(store=store)},
),
store=store,
)
```
### 7. FilesystemBackend Without root_dir Restriction
```python
# BAD - unrestricted filesystem access
agent = create_deep_agent(
backend=FilesystemBackend(root_dir="/"), # Full system access!
)
# GOOD - scope to project directory
agent = create_deep_agent(
backend=FilesystemBackend(root_dir="/home/user/project"),
)
```
### 8. CompositeBackend Route Order Confusion
```python
# BAD - shorter prefix shadows longer prefix
agent = create_deep_agent(
backend=CompositeBackend(
default=StateBackend(),
routes={
"/mem/": backend_a, # This catches /mem/long-term/ too!
"/mem/long-term/": backend_b, # Never reached
},
),
)
# GOOD - CompositeBackend sorts by length automatically
# But be explicit about your intent:
agent = create_deep_agent(
backend=CompositeBackend(
default=StateBackend(),
routes={
"/memories/": persistent_backend,
"/workspace/": ephemeral_backend,
},
),
)
```
### 9. Expecting execute Tool Without SandboxBackend
```python
# BAD - execute tool won't work with StateBackend
agent = create_deep_agent() # Default StateBackend
# Agent calls execute("ls -la") → Error: not supported
# GOOD - use FilesystemBackend for shell execution
agent = create_deep_agent(
backend=FilesystemBackend(root_dir="/project"),
)
# Agent calls execute("ls -la") → Works
```
## Subagent Issues
### 10. Subagent Missing Required Fields
```python
# BAD - missing required fields
agent = create_deep_agent(
subagents=[{
"name": "helper",
# Missing: description, system_prompt, tools
}]
)
# GOOD - all required fields present
agent = create_deep_agent(
subagents=[{
"name": "helper",
"description": "General helper for misc tasks",
"system_prompt": "You are a helpful assistant.",
"tools": [], # Can be empty but must be present
}]
)
```
### 11. Subagent Name Collision
```python
# BAD - duplicate subagent names
agent = create_deep_agent(
subagents=[
{"name": "research", "description": "A", ...},
{"name": "research", "description": "B", ...}, # Collision!
]
)
# GOOD - unique names
agent = create_deep_agent(
subagents=[
{"name": "web-research", "description": "Web-based research", ...},
{"name": "doc-research", "description": "Document research", ...},
]
)
```
### 12. Overusing Subagents for Simple Tasks
```python
# BAD - subagent overhead for trivial task
# In system prompt or agent behavior:
"Use the task tool to check the current time"
"Delegate file reading to a subagent"
# GOOD - use subagents for complex, isolated work
"Use the task tool for multi-step research that requires many searches"
"Delegate the full analysis workflow to a subagent"
```
### 13. CompiledSubAgent Without Proper State
```python
# BAD - subgraph with incompatible state schema
from langgraph.graph import StateGraph
class CustomState(TypedDict):
custom_field: str # No messages field!
sub_builder = StateGraph(CustomState)
# ... build graph
subgraph = sub_builder.compile()
agent = create_deep_agent(
subagents=[CompiledSubAgent(
name="custom",
description="Custom workflow",
runnable=subgraph, # State mismatch!
)]
)
# GOOD - ensure compatible state or use message-based interface
class CompatibleState(TypedDict):
messages: Annotated[list, add_messages]
custom_field: str
```
## Middleware Issues
### 14. Middleware Order Misunderstanding
```python
# BAD - expecting custom middleware to run first
class PreProcessMiddleware(AgentMiddleware):
def transform_request(self, request):
# Expecting this runs before built-in middleware
return request
agent = create_deep_agent(middleware=[PreProcessMiddleware()])
# Actually runs AFTER TodoList, Filesystem, SubAgent, etc.
# GOOD - understand middleware runs after built-in stack
# Built-in order:
# 1. TodoListMiddleware
# 2. FilesystemMiddleware
# 3. SubAgentMiddleware
# 4. SummarizationMiddleware
# 5. AnthropicPromptCachingMiddleware
# 6. PatchToolCallsMiddleware
# 7. YOUR MIDDLEWARE HERE
# 8. HumanInTheLoopMiddleware (if interrupt_on set)
```
### 15. Middleware Mutating Request/Response
```python
# BAD - mutating instead of returning new object
class BadMiddleware(AgentMiddleware):
def transform_request(self, request):
request.messages.append(extra_message) # Mutation!
return request
# GOOD - return modified copy
class GoodMiddleware(AgentMiddleware):
def transform_request(self, request):
return ModelRequest(
messages=[*request.messages, extra_message],
**other_fields
)
```
### 16. Middleware Tools Without Descriptions
```python
# BAD - tool without docstring
@tool
def my_tool(arg: str) -> str:
return process(arg)
class MyMiddleware(AgentMiddleware):
tools = [my_tool] # LLM won't know how to use it!
# GOOD - descriptive docstring
@tool
def my_tool(arg: str) -> str:
"""Process the input string and return formatted result.
Args:
arg: The string to process
Returns:
Formatted result string
"""
return process(arg)
```
## System Prompt Issues
### 17. Duplicating Built-in Tool Instructions
```python
# BAD - re-explaining what middleware already covers
agent = create_deep_agent(
system_prompt="""You have access to these tools:
- write_todos: Create task lists
- read_file: Read files from the filesystem
- task: Delegate to subagents
When using files, always use absolute paths..."""
)
# This duplicates what FilesystemMiddleware and TodoListMiddleware inject!
# GOOD - focus on domain-specific guidance
agent = create_deep_agent(
system_prompt="""You are a code review assistant.
Workflow:
1. Read the files to review
2. Create a todo list of issues found
3. Delegate deep analysis to subagents if needed
4. Compile findings into a report"""
)
```
### 18. Contradicting Built-in Instructions
```python
# BAD - contradicting default behavior
agent = create_deep_agent(
system_prompt="""Never use the task tool.
Always process everything in the main thread.
Don't use todos, just remember everything."""
)
# Fighting against the framework!
# GOOD - work with the framework
agent = create_deep_agent(
system_prompt="""For simple tasks, handle directly.
For complex multi-step research, use subagents.
Track progress with todos for tasks with 3+ steps."""
)
```
### 19. Missing Stopping Criteria
```python
# BAD - no guidance on when to stop
agent = create_deep_agent(
system_prompt="Research everything about the topic thoroughly."
)
# Agent may run indefinitely!
# GOOD - define completion criteria
agent = create_deep_agent(
system_prompt="""Research the topic with these constraints:
- Maximum 5 web searches
- Stop when you have 3 reliable sources
- Limit subagent delegations to 2 parallel tasks
- Summarize findings within 500 words"""
)
```
## Performance Issues
### 20. Not Parallelizing Independent Subagents
```python
# BAD - sequential subagent calls (in agent behavior)
# Agent calls: task(research topic A) → wait → task(research topic B) → wait
# GOOD - parallel subagent calls
# Agent calls in single turn:
# task(research topic A)
# task(research topic B)
# task(research topic C)
# All run concurrently!
# Guide via system prompt:
agent = create_deep_agent(
system_prompt="""When researching multiple topics,
launch all research subagents in parallel in a single response."""
)
```
### 21. Large Files in State
```python
# BAD - writing large files to StateBackend
# Agent writes 10MB log file to /output/full_log.txt
# This bloats every checkpoint!
# GOOD - use FilesystemBackend for large files or paginate
agent = create_deep_agent(
backend=CompositeBackend(
default=StateBackend(), # Small files
routes={
"/large_files/": FilesystemBackend(root_dir="/tmp/agent"),
},
),
)
```
### 22. InMemorySaver in Production
```python
# BAD - ephemeral checkpointer in production
agent = create_deep_agent(
checkpointer=InMemorySaver(), # Lost on restart!
)
# GOOD - persistent checkpointer
from langgraph.checkpoint.postgres import PostgresSaver
agent = create_deep_agent(
checkpointer=PostgresSaver.from_conn_string(DATABASE_URL),
)
```
### 23. Missing Recursion Awareness
```python
# BAD - no guard against long-running loops
agent = create_deep_agent(
system_prompt="Keep improving the solution until it's perfect."
)
# May hit recursion limit (default 1000)
# GOOD - explicit iteration limits
agent = create_deep_agent(
system_prompt="""Improve the solution iteratively:
- Maximum 3 revision cycles
- Stop if quality score > 90%
- Stop if no improvement after 2 iterations"""
)
```
## Code Review Checklist
### Configuration
- [ ] Checkpointer provided if using `interrupt_on`
- [ ] Store provided if using `StoreBackend`
- [ ] Thread ID provided in config when using checkpointer
- [ ] Backend appropriate for use case (ephemeral vs persistent)
### Backends
- [ ] FilesystemBackend scoped to safe `root_dir`
- [ ] StoreBackend has corresponding `store` parameter
- [ ] CompositeBackend routes don't shadow each other unintentionally
- [ ] Not expecting persistence from StateBackend across threads
### Subagents
- [ ] All required fields present (name, description, system_prompt, tools)
- [ ] Unique subagent names
- [ ] CompiledSubAgent has compatible state schema
- [ ] Subagents used for complex tasks, not trivial operations
### Middleware
- [ ] Custom middleware added after built-in stack (expected behavior)
- [ ] Tools have descriptive docstrings
- [ ] Not mutating request/response objects
### System Prompt
- [ ] Not duplicating built-in tool instructions
- [ ] Not contradicting framework defaults
- [ ] Stopping criteria defined for open-ended tasks
- [ ] Parallelization guidance for independent tasks
### Performance
- [ ] Large files routed to appropriate backend
- [ ] Production uses persistent checkpointer
- [ ] Recursion/iteration limits considered
- [ ] Independent subagents parallelizedRelated Skills
Post-Mortem & Incident Review Framework
Run structured post-mortems that actually prevent repeat failures. Blameless analysis, root cause identification, and action tracking.
Pitch Deck Reviewer
Reviews pitch decks and provides investor-ready feedback with scoring
Performance Review Engine
> Your AI-powered performance management system. Write reviews that develop people, not just evaluate them. From self-assessments to 360° feedback to calibration — complete frameworks for every review cycle.
Deal Desk — Structured Deal Review & Approval
Run every non-standard deal through a repeatable review process. Catch margin leaks, enforce discount guardrails, and close faster with pre-approved terms.
Contract Review Assistant
Analyze business contracts for risks, unfavorable terms, and missing clauses. Get a plain-English summary of what you're signing.
afrexai-code-reviewer
Enterprise-grade code review agent. Reviews PRs, diffs, or code files for security vulnerabilities, performance issues, error handling gaps, architecture smells, and test coverage. Works with any language, any repo, no dependencies required.
performance-review-cn
绩效面谈报告、OKR对齐度检测、校准辅助
clawdtm-review
Review and rate OpenClaw skills on ClawdTM. See what humans and AI agents recommend.
cyber-owasp-review
Map application security findings to OWASP Top 10 categories and generate remediation checklists. Use for normalized AppSec review outputs and category-level prioritization.
Contract Reviewer - AI Legal Document Risk Scanner
Upload any contract or legal document and get a structured risk analysis with flagged clauses, plain-language explanations, and negotiation suggestions.
serde-code-review
Reviews serde serialization code for derive patterns, enum representations, custom implementations, and common serialization bugs. Use when reviewing Rust code that uses serde, serde_json, toml, or any serde-based serialization format. Covers attribute macros, field renaming, and format-specific pitfalls.
rust-testing-code-review
Reviews Rust test code for unit test patterns, integration test structure, async testing, mocking approaches, and property-based testing. Use when reviewing _test.rs files,