Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/jartan-llc/grimoire/python-error-handlingnpx skills add Jartan-LLC/grimoire --skill python-error-handlinggit clone --depth 1 https://github.com/Jartan-LLC/grimoireWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00023 | $0.01317 |
| Opus 5 | $0.00012 | $0.00659 |
| Sonnet 5 | $0.00005 | $0.00263 |
| Haiku 4.5 | $0.00002 | $0.00132 |
Grade A, and why
python-error-handling scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
This is a copy
91% identical to python-error-handling — 13 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 187 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Error Handling
Build robust Python applications with proper input validation, meaningful exceptions, and graceful failure handling. Good error handling makes debugging easier and systems more reliable.
Core Concepts
1. Fail Fast
Validate inputs early, before expensive operations. Report all validation errors at once when possible.
2. Meaningful Exceptions
Use appropriate exception types with context. Messages should explain what failed, why, and how to fix it.
3. Partial Failures
In batch operations, don't let one failure abort everything. Track successes and failures separately.
4. Preserve Context
Chain exceptions to maintain the full error trail for debugging.
Quick Start
def fetch_page(url: str, page_size: int) -> Page:
if not url:
raise ValueError("'url' is required")
if not 1 <= page_size <= 100:
raise ValueError(f"'page_size' must be 1-100, got {page_size}")
# Now safe to proceed...
Fundamental Patterns
Pattern 1: Early Input Validation
Validate all inputs at API boundaries before any processing begins.
def process_order(
order_id: str,
quantity: int,
discount_percent: float,
) -> OrderResult:
"""Process an order with validation."""
# Validate required fields
if not order_id:
raise ValueError("'order_id' is required")
# Validate ranges
if quantity <= 0:
raise ValueError(f"'quantity' must be positive, got {quantity}")
if not 0 <= discount_percent <= 100:
raise ValueError(
f"'discount_percent' must be 0-100, got {discount_percent}"
)
# Validation passed, proceed with processing
return _process_validated_order(order_id, quantity, discount_percent)
Pattern 2: Convert to Domain Types Early
Parse strings and external data into typed domain objects at system boundaries.
from enum import Enum
class OutputFormat(Enum):
JSON = "json"
CSV = "csv"
PARQUET = "parquet"
def parse_output_format(value: str) -> OutputFormat:
"""Parse string to OutputFormat enum.
Args:
value: Format string from user input.
Returns:
Validated OutputFormat enum member.
Raises:
ValueError: If format is not recognized.
"""
try:
return OutputFormat(value.lower())
except ValueError:
valid_formats = [f.value for f in OutputFormat]
raise ValueError(
f"Invalid format '{value}'. "
f"Valid options: {', '.join(valid_formats)}"
)
# Usage at API boundary
def export_data(data: list[dict], format_str: str) -> bytes:
output_format = parse_output_format(format_str) # Fail fast
# Rest of function uses typed OutputFormat
...
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 187 lines · 23 tokens per session scan A 3739590d97a3
python-error-handling is a skill published in the GitHub repository Jartan-LLC/grimoire (2 stars, last pushed 14d ago), licensed MIT. It adds 23 tokens to every session and 1,317 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 91% identical to python-error-handling, differing in 13 lines, and is treated as a copy.
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