antipattern-catalog
Document technical debt, anti-patterns, and patterns to avoid from analyzed frameworks. Use when (1) creating a "Do Not Repeat" list from framework analysis, (2) categorizing observed code smells and issues, (3) assessing severity of architectural problems, (4) generating remediation suggestions, or (5) synthesizing lessons learned across multiple frameworks.
Best use case
antipattern-catalog is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Document technical debt, anti-patterns, and patterns to avoid from analyzed frameworks. Use when (1) creating a "Do Not Repeat" list from framework analysis, (2) categorizing observed code smells and issues, (3) assessing severity of architectural problems, (4) generating remediation suggestions, or (5) synthesizing lessons learned across multiple frameworks.
Document technical debt, anti-patterns, and patterns to avoid from analyzed frameworks. Use when (1) creating a "Do Not Repeat" list from framework analysis, (2) categorizing observed code smells and issues, (3) assessing severity of architectural problems, (4) generating remediation suggestions, or (5) synthesizing lessons learned across multiple frameworks.
Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.
Practical example
Example input
Use the "antipattern-catalog" skill to help with this workflow task. Context: Document technical debt, anti-patterns, and patterns to avoid from analyzed frameworks. Use when (1) creating a "Do Not Repeat" list from framework analysis, (2) categorizing observed code smells and issues, (3) assessing severity of architectural problems, (4) generating remediation suggestions, or (5) synthesizing lessons learned across multiple frameworks.
Example output
A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.
When to use this skill
- Use this skill when you want a reusable workflow rather than writing the same prompt again and again.
When not to use this skill
- Do not use this when you only need a one-off answer and do not need a reusable workflow.
- Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/antipattern-catalog/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How antipattern-catalog Compares
| Feature / Agent | antipattern-catalog | 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?
Document technical debt, anti-patterns, and patterns to avoid from analyzed frameworks. Use when (1) creating a "Do Not Repeat" list from framework analysis, (2) categorizing observed code smells and issues, (3) assessing severity of architectural problems, (4) generating remediation suggestions, or (5) synthesizing lessons learned across multiple frameworks.
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.
SKILL.md Source
# Anti-Pattern Catalog
Documents technical debt and patterns to avoid.
## Process
1. **Collect observations** — Gather issues from Phase 1 & 2 analyses
2. **Categorize** — Structural, behavioral, observability, performance
3. **Assess severity** — Critical, major, minor, cosmetic
4. **Generate remediation** — Suggest fixes for each pattern
5. **Create checklist** — "Do Not Repeat" guidelines
## Anti-Pattern Categories
### Structural Anti-Patterns
Issues with code organization, inheritance, and modularity.
| Pattern | Symptom | Example |
|---------|---------|---------|
| **Deep Inheritance** | 5+ levels of class hierarchy | `Agent → BaseAgent → RunnableAgent → ExecutableAgent → ...` |
| **God Class** | One class with 50+ methods | `AgentExecutor` doing routing, execution, memory, tools |
| **Circular Dependencies** | Module A imports B, B imports A | `agent.py ↔ tools.py` |
| **Leaky Abstraction** | Implementation details exposed | Base class assumes specific LLM response format |
| **Premature Abstraction** | Over-engineered for flexibility | 5 interfaces for a single implementation |
### Behavioral Anti-Patterns
Issues with runtime behavior and logic.
| Pattern | Symptom | Example |
|---------|---------|---------|
| **Hidden State Mutation** | State changes not obvious | `tool.run()` modifies agent's memory |
| **Implicit Contracts** | Undocumented assumptions | Tools assume specific message format |
| **Silent Failures** | Errors swallowed | `except: pass` in tool execution |
| **Infinite Loop Risk** | No termination guarantee | No step limit on agent loop |
| **Race Conditions** | Concurrent state access | Shared dict without locks |
### Observability Anti-Patterns
Issues with debugging and monitoring.
| Pattern | Symptom | Example |
|---------|---------|---------|
| **Hidden LLM Response** | Raw response not accessible | Token counts unavailable |
| **Opaque Errors** | Generic error messages | `"Something went wrong"` |
| **No Tracing** | Can't follow execution | No request IDs, no spans |
| **Swallowed Context** | Information lost | Tool error not fed back to LLM |
| **Missing Metrics** | No performance data | No latency, token, or cost tracking |
### Performance Anti-Patterns
Issues affecting speed and resource usage.
| Pattern | Symptom | Example |
|---------|---------|---------|
| **Sync in Async** | Blocking calls in async code | `requests.get()` in async function |
| **N+1 Queries** | Repeated similar operations | Loading each tool config separately |
| **Unbounded Memory** | History grows forever | No eviction policy |
| **Eager Loading** | Loading unused resources | All tools initialized at startup |
| **No Caching** | Repeated expensive operations | Re-parsing same schema each call |
## Severity Assessment
### Critical (P0)
- Security vulnerabilities
- Data loss risk
- Infinite loops without guards
- Production outage risk
### Major (P1)
- Performance issues >2x slowdown
- Poor error handling
- Difficult to extend
- Concurrency bugs
### Minor (P2)
- Code style issues
- Minor inefficiencies
- Documentation gaps
- Inconsistent patterns
### Cosmetic (P3)
- Naming conventions
- Formatting
- Minor redundancy
## Catalog Entry Template
```markdown
### [Pattern Name]
**Category**: [Structural/Behavioral/Observability/Performance]
**Severity**: [Critical/Major/Minor/Cosmetic]
**Framework(s)**: [Where observed]
#### Description
[Brief explanation of the anti-pattern]
#### Example
[Code snippet showing the problem]
#### Impact
- [Impact 1]
- [Impact 2]
#### Remediation
[How to fix or avoid this pattern]
#### Code Example (Fixed)
[Corrected code snippet]
```
## Common Anti-Patterns Deep Dive
### Deep Inheritance Hell
**Problem**:
```python
class Agent(BaseAgent):
pass
class BaseAgent(RunnableAgent):
pass
class RunnableAgent(ExecutableAgent):
pass
class ExecutableAgent(ConfigurableAgent):
pass
class ConfigurableAgent(LoggableAgent):
pass
class LoggableAgent(ABC):
pass
# 6 layers! Which method comes from where?
```
**Remediation**: Prefer composition over inheritance
```python
class Agent:
def __init__(
self,
executor: Executor,
config: Config,
logger: Logger
):
self.executor = executor
self.config = config
self.logger = logger
```
### Silent Tool Failures
**Problem**:
```python
def run_tool(self, tool, args):
try:
return tool.execute(args)
except Exception:
return None # Error lost forever!
```
**Remediation**: Capture and propagate errors
```python
def run_tool(self, tool, args) -> ToolResult:
try:
output = tool.execute(args)
return ToolResult(success=True, output=output)
except Exception as e:
return ToolResult(
success=False,
error=f"{type(e).__name__}: {e}",
traceback=traceback.format_exc()
)
```
### Hidden LLM Response
**Problem**:
```python
class LLMWrapper:
def generate(self, prompt: str) -> str:
response = self.client.chat(prompt)
return response.content # Token counts, model info lost!
```
**Remediation**: Expose full response
```python
class LLMWrapper:
def generate(self, prompt: str) -> LLMResponse:
response = self.client.chat(prompt)
return LLMResponse(
content=response.content,
model=response.model,
usage=TokenUsage(
prompt=response.usage.prompt_tokens,
completion=response.usage.completion_tokens
),
raw=response # Always keep raw
)
```
## Output Template
```markdown
# Anti-Pattern Catalog: [Analysis Name]
## Summary
| Severity | Count |
|----------|-------|
| Critical | 2 |
| Major | 5 |
| Minor | 8 |
| Cosmetic | 3 |
## Critical Issues
### 1. [Pattern Name]
[Full catalog entry]
### 2. [Pattern Name]
[Full catalog entry]
## Major Issues
### 1. [Pattern Name]
[Full catalog entry]
...
## Do Not Repeat Checklist
- [ ] Never use more than 3 levels of inheritance
- [ ] Always expose raw LLM response with token counts
- [ ] Never swallow exceptions without logging
- [ ] Always include step limits on agent loops
- [ ] Never mutate shared state without locks
- [ ] Always provide structured error feedback to LLM
...
```
## Integration
- **Inputs from**: All Phase 1 & 2 analysis skills
- **Feeds into**: `architecture-synthesis` for design decisions
- **Related**: `comparative-matrix` for pattern comparisonRelated Skills
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