langchain-1-error-handling
Sub-skill of langchain: 1. Error Handling (+2).
Best use case
langchain-1-error-handling is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Sub-skill of langchain: 1. Error Handling (+2).
Teams using langchain-1-error-handling 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/1-error-handling/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How langchain-1-error-handling Compares
| Feature / Agent | langchain-1-error-handling | 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?
Sub-skill of langchain: 1. Error Handling (+2).
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
# 1. Error Handling (+2)
## 1. Error Handling
```python
from langchain_core.runnables import RunnableConfig
from langchain_core.callbacks import CallbackManager
import logging
logger = logging.getLogger(__name__)
def safe_invoke(chain, input_data, max_retries=3):
"""Invoke chain with retry logic."""
for attempt in range(max_retries):
try:
return chain.invoke(input_data)
except Exception as e:
logger.warning(f"Attempt {attempt + 1} failed: {e}")
if attempt == max_retries - 1:
raise
time.sleep(2 ** attempt) # Exponential backoff
```
## 2. Prompt Versioning
```python
from pathlib import Path
import yaml
def load_prompt_template(version: str = "v1"):
"""Load versioned prompt template."""
prompt_path = Path(f"prompts/{version}.yaml")
with open(prompt_path) as f:
config = yaml.safe_load(f)
return ChatPromptTemplate.from_template(config["template"])
```
## 3. Cost Monitoring
```python
from langchain_community.callbacks import get_openai_callback
def track_costs(chain, input_data):
"""Track API costs for chain invocation."""
with get_openai_callback() as cb:
result = chain.invoke(input_data)
print(f"Total Tokens: {cb.total_tokens}")
print(f"Prompt Tokens: {cb.prompt_tokens}")
print(f"Completion Tokens: {cb.completion_tokens}")
print(f"Total Cost: ${cb.total_cost:.4f}")
return result, cb
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