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 skills add darellchua2/opencode-config-template --skill python-docstring-generatorgit clone --depth 1 https://github.com/darellchua2/opencode-config-templateWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/darellchua2/opencode-config-template/python-docstring-generator)<a href="https://agentmods.dev/skills/darellchua2/opencode-config-template/python-docstring-generator"><img src="https://agentmods.dev/badge/skills/darellchua2/opencode-config-template/python-docstring-generator/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/darellchua2/opencode-config-template/python-docstring-generator"><img src="https://agentmods.dev/badge/skills/darellchua2/opencode-config-template/python-docstring-generator.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00033 | $0.04374 |
| Opus 5.5 | $0.00013 | $0.01750 |
| Sonnet 5.5 | $0.00007 | $0.00875 |
| Haiku 4.5 | $0.00003 | $0.00437 |
Grade A, and why
python-docstring-generator scanned grade A with 1 finding 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 1mo 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = await fetch(f"/api/users/{user_id}") How it starts
The opening of the file, as written. The whole thing — 852 lines — stays where its author put it; the contents beside it link to each section on GitHub.
What this skill does
- Adds docstrings to all Python functions and methods
- Ensures all functions with parameters have proper parameter documentation with type information
- Documents return values with
Returns:sections - Handles async functions, classes, and utility functions
- Maintains existing docstring style conventions in codebase (Google, NumPy, or Sphinx)
- Follows PEP 257 standards for Python code documentation
- Integrates with Python's type hints for better IDE support
When to use
Use this when:
- Implementing new functions, classes, or utility modules in Python projects
- Refactoring code and adding missing documentation
- Preparing for code review and ensuring proper docstring standards
- Functions lack docstrings or have incomplete parameter documentation
- You need to enforce docstring presence as part of quality checks
Common signals:
- Python functions without docstrings
- Functions with parameters but missing parameter documentation
- Return values not documented
- Async functions without proper return type documentation
- Python code lacking type-aware documentation
Python Docstring Standard Formats
Google Style (Recommended for most projects)
Basic Function
def calculate_sum(a: int, b: int) -> int:
"""Calculate the sum of two numbers.
Args:
a: The first number to add.
b: The second number to add.
Returns:
The sum of a and b.
"""
return a + b
Async Function
async def fetch_user(user_id: str) -> User:
"""Fetch user data from API.
Args:
user_id: The unique identifier of user.
Returns:
The user data object.
Raises:
APIError: When the API request fails.
"""
response = await fetch(f"/api/users/{user_id}")
if not response.ok:
raise APIError('Failed to fetch user')
return response.json()
Class Method
class UserProfile:
"""User profile display component.
This class manages user profile data and display logic.
Attributes:
user_id: The user's unique identifier.
on_edit: Callback when edit button is clicked.
"""
def __init__(self, user_id: str, on_edit: Callable):
"""Initialize user profile.
Args:
user_id: The user's unique identifier.
on_edit: Callback when edit button is clicked.
"""
self.user_id = user_id
self.on_edit = on_edit
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.
- 1mo ago First seen · 852 lines · 33 tokens per session scan A 62fbe3fc2813
python-docstring-generator is a skill published in the GitHub repository darellchua2/opencode-config-template (6 stars, last pushed 28d ago), licensed Apache-2.0. It adds 33 tokens to every session and 4,374 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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