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/datenoio/iterabledata/iterabledata-developmentnpx skills add datenoio/iterabledata --skill iterabledata-developmentgit clone --depth 1 https://github.com/datenoio/iterabledataWrote 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/datenoio/iterabledata/iterabledata-development)<a href="https://agentmods.dev/skills/datenoio/iterabledata/iterabledata-development"><img src="https://agentmods.dev/badge/skills/datenoio/iterabledata/iterabledata-development.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00033 | $0.00695 |
| Opus 5 | $0.00016 | $0.00347 |
| Sonnet 5 | $0.00007 | $0.00139 |
| Haiku 4.5 | $0.00003 | $0.00069 |
Grade A, and why
iterabledata-development 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 5d 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.
How it starts
The opening of the file, as written. The whole thing — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
IterableData Development
Quick Setup
pip install -e ".[dev]"
pytest --verbose
ruff check iterable tests
ruff format iterable tests
Code Style
- Python 3.10+ with type hints where appropriate
- Max line length: 120 characters
- Use
rufffor linting and formatting - Double quotes for strings consistently
- Always use context managers for file operations
- Import order: standard library, third-party, local imports
Project Structure
iterable/helpers/- Utility functions (detect, schema, utils)iterable/datatypes/- Format-specific implementationsiterable/codecs/- Compression codec implementationsiterable/engines/- Processing engines (DuckDB, internal)iterable/convert/- Format conversion utilitiesiterable/pipeline/- Data pipeline processingtests/- Test suite (one test file per format/feature)
Import Patterns
- Main entry:
from iterable import open_iterable - Convert:
from iterable.convert import convert - Format-specific (advanced):
from iterable.datatypes.csv import CSVIterable - Codecs (advanced):
from iterable.codecs.gzipcodec import GZIPCodec - Always use
open_iterable()for user-facing examples; do not teachiterable.helpers.detectas the default import
File Handling
- Always use context managers:
with open_iterable('file.csv') as source: - Never call
.close()when usingwithstatements - Reset iterators with
.reset()method when needed - Handle compression automatically via filename detection
Error Handling
- Format detection failures: provide helpful error messages
- Missing optional dependencies: raise clear ImportError with installation instructions
- Invalid file formats: raise appropriate exceptions (ValueError, TypeError)
- Always handle file I/O errors gracefully
Code Conventions
- Use
open_iterable()for automatic format detection - Prefer bulk operations (
read_bulk,write_bulk) for performance - Use DuckDB engine when appropriate (CSV, JSONL files)
- Handle encoding automatically via
chardetor user specification
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.
- 5d ago First seen · 86 lines · 33 tokens per session scan A bbbc57ad7359
iterabledata-development is a skill published in the GitHub repository datenoio/iterabledata (37 stars, last pushed 16d ago), licensed MIT. It adds 33 tokens to every session and 695 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
pytorch-patterns
PyTorch deep learning patterns and best practices for building robust, efficient, and reproducible training pipelines, model architectures, and data loading.
accelerate
Run PyTorch training across GPUs with minimal changes.
fastapi
FastAPI best practices and conventions. Use when working with FastAPI APIs, Pydantic models, dependencies, streaming responses including Server-Sent Events (SSE), and serving frontend apps. Keeps FastAPI code clean and up to date with the latest features and patterns.
sensitive-logging-audit
Audit and fix sensitive-data exposure through Python runtime logging in openai-agents-python. Use when reviewing logging, print, warnings, stderr, traceback, MCP names, model or tool exceptions, redaction flags, or any diagnostic path that may retain user data.
developing-genkit-python
Develop AI-powered applications using Genkit in Python. Use when the user asks about Genkit, AI agents, flows, or tools in Python, or when encountering Genkit errors, import issues, or API problems.
optimize-for-gpu
GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS…