Skills

A skill is a package of structured files that teaches an AI coding agent how to work with a specific tool or framework. The skill below was generated by Great Docs from this project’s documentation. Install it in your agent and it will be able to run commands, edit configuration, write content, and troubleshoot problems without step-by-step guidance from you.

Any agent — install with npx:

npx skills add https://ketchbrookanalytics.github.io/fcall-py/

Codex / OpenCode

Tell the agent:
Fetch the skill file at https://ketchbrookanalytics.github.io/fcall-py/skill.md and follow the instructions.

Manual — download the skill file:

curl -O https://ketchbrookanalytics.github.io/fcall-py/skill.md

Or browse the SKILL.md file.

SKILL.md

---
name: fcall
description: >
  Parse Farm Credit Administration (FCA) Call Report data into tidy Polars data frames. Use when writing Python code that uses the fcall package.
license: MIT
compatibility: Requires Python >=3.11.
---

# fcall

Parse Farm Credit Administration (FCA) Call Report data into tidy Polars data frames

## Installation

```bash
pip install fcall
```

## API overview

### Core API

The three user-facing functions that mirror the R {fcall} package.

- `download_data`: Download a quarter's FCA Call Report archive and unzip into *dest*
- `process_data`: Read a quarter's downloaded .TXT files into tidy Polars DataFrames
- `compare_metadata`: Diff the metadata (D_*.TXT) files between two quarter directories

### Lower-level helpers

Exported for power users who need fine-grained control over parsing. Most users should call ``process_data()`` instead.


- `process_metadata_file`: Parse a metadata (D_*.TXT) file into a scenario + vars_info dict
- `process_data_file`: Read *file* and return a tidy DataFrame using *metadata* and *codes_dict*
- `read_data_file`: Low-level reader: returns an unnamed DataFrame matching the raw CSV shape
- `get_codes_dict`: Return the codes dictionary for *data_name*, or ``None`` values if none
- `compare_files_content`: Return unified-diff lines between *filename* in *dir1* and *dir2*

### Data assets

Internal datasets shipped with the package. ``fcall.file_metadata`` is a Polars DataFrame (36 rows) mapping file prefixes to descriptions — access it directly. ``get_code_df`` returns a code-to-label DataFrame for datasets that have repeating code groups.


- `get_code_df`: Return a DataFrame[code: Int64, value: Utf8] for *registry_key*

## Resources

- [Full documentation](https://ketchbrookanalytics.github.io/fcall-py/)
- [llms.txt](llms.txt) — Indexed API reference for LLMs
- [llms-full.txt](llms-full.txt) — Comprehensive documentation for LLMs
- [Source code](https://github.com/ketchbrookanalytics/fcall-py)