iterabledata: Skill for Cursor

.cursor/skills/format-implementation/SKILL.md

format-implementation is a skill for Cursor from datenoio/iterabledata. It costs 31 tokens per session (1,221 once invoked), scanned A, original, MIT.

A development guide for adding support for new file formats or compression methods to IterableData, a Python library for reading and writing data files.

In plain words
What is it for?
Use it when implementing a new data format, compression codec, bulk-reading or bulk-writing support, or format detection in IterableData.
Why use it?
It gives developers a consistent way to add format handling, including reading, writing, detection, tests, dependencies, and documentation.

Skill for Cursor

Written for Cursor: installed under .cursor/.

This is datenoio/iterabledata's own configuration. It tells Cursor how to work on iterabledata itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything iterabledata configures →

Reuse

Borrowing it

Nothing to install: this file belongs to datenoio/iterabledata. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/datenoio/iterabledata/main/.cursor/skills/format-implementation/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/datenoio/iterabledata

Made for: Cursor.

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README.md
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Per session 31 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,221 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00031 $0.01221
Opus 5.5 $0.00012 $0.00488
Sonnet 5.5 $0.00006 $0.00244
Haiku 4.5 $0.00003 $0.00122

Measured 1mo ago against content hash 0645766472cf, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

format-implementation 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 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.

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.

.cursor/skills/format-implementation/SKILL.md · 199 lines

How it starts

The opening of the file, as written. The whole thing — 199 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Format Implementation Guide

Adding New Formats

Step-by-Step Process

  1. Create format file: iterable/datatypes/<format>.py
  2. Implement BaseIterable: Inherit from BaseIterable in iterable/base.py
  3. Required methods: read(), write(), read_bulk(), write_bulk(), etc.
  4. Add detection: Update iterable/helpers/detect.py
  5. Create tests: tests/test_<format>.py
  6. Update dependencies: Add optional dependency to pyproject.toml if needed
  7. Update documentation: Add format to docs

Implementation Pattern

from iterable.base import BaseIterable

class NewFormatIterable(BaseIterable):
    def __init__(self, source, mode='r', **kwargs):
        super().__init__(source, mode, **kwargs)
        # Initialize format-specific resources
    
    def read(self):
        # Return iterator of dict objects
        pass
    
    def write(self, data):
        # Write dict objects to file
        pass
    
    def read_bulk(self, size=1000):
        # Bulk read for performance
        pass
    
    def write_bulk(self, data):
        # Bulk write for performance
        pass

Format Detection

Update iterable/helpers/detect.py:

  1. Add file extension detection
  2. Add magic number detection (for binary formats)
  3. Add content-based heuristics if needed
  4. Update detect_file_type() function

Example:

def detect_file_type(filename, content=None):
    # Check extension
    if filename.endswith('.newformat'):
        return 'newformat'
    
    # Check magic numbers
    if content and content.startswith(b'MAGIC'):
        return 'newformat'
    
    # ... existing detection logic

Adding New Codecs

Step-by-Step Process

  1. Create codec file: iterable/codecs/<codec>codec.py
  2. Implement codec class: read(), write(), close() methods
  3. Add detection: Update iterable/helpers/detect.py
  4. Add compression detection: Update format detection logic
  5. Create tests: Add to relevant test file or create new one
  6. Update dependencies: Add optional dependency to pyproject.toml

Read the full file on GitHub · 199 lines

Changes

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.

  1. 1mo ago First seen · 199 lines · 31 tokens per session scan A 0645766472cf

Subscribe to this mod's changes

format-implementation is a skill published in the GitHub repository datenoio/iterabledata (37 stars, last pushed 2mo ago), licensed MIT. It adds 31 tokens to every session and 1,221 once invoked, about $0.0001 per session on Opus 5.5. 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.

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