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Evo Macro Data Ingestion

Organization
OpenLAIR
evo-macro-data-ingestion

Parses ERP .xls tables and CPI .xlsx files, handles multi-row headers, extracts annual totals, computes partial-year averages from quarterly data, and produces clean annual series indexed by integer year.

Overview

PublisherOpenLAIR
RepositoryOpenSkill
Skill nameevo-macro-data-ingestion
Stars
97
Forks
4
Bundled files
2
LicenseApache-2.0
Links
Skill sourceRepositoryHomepage
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by OpenLAIR on GitHub. Read the source before you install it.

Installation

Install the Evo Macro Data Ingestion AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/OpenLAIR/OpenSkill/tree/main/tasks-evolved/econ-detrending-correlation/environment/skills/evo-macro-data-ingestion
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/OpenLAIR/OpenSkill.git /tmp/OpenSkill
mkdir -p .claude/skills
cp -r /tmp/OpenSkill/tasks-evolved/econ-detrending-correlation/environment/skills/evo-macro-data-ingestion .claude/skills/evo-macro-data-ingestion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evo Macro Data Ingestion in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Evo Macro Data Ingestion on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Evo Macro Data Ingestion is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

evo-macro-data-ingestion

Parses Economic Report of the President (ERP) .xls tables and CPI .xlsx files into clean pandas Series indexed by integer year.

Key Functions

parse_erp_annual_total(filepath, target_year=2024)

Parses an ERP .xls file to extract the annual "Total" column (first data column).

  • Handles multi-row headers, trailing periods on years (e.g., '1973.')
  • Handles quarterly data at bottom of table
  • For partial years (e.g., 2024 with Q1-Q3 only), computes average of available quarters
  • Uses engine="xlrd" for .xls files
  • Returns: pd.Series indexed by integer year

compute_partial_year_average(df, target_year=2024)

Computes arithmetic mean of available quarterly SAAR values for a partial year.

  • Handles ERP quarterly format: '2024: I.' followed by indented ' II.', ' III p.'
  • Returns: float or None

process_cpi_annual(filepath)

Reads CPI data from .xlsx file. Auto-detects format:

  • Pre-processed annual data (Year + CPI columns)
  • Raw FRED monthly CPIAUCSL data (aggregated to annual mean)
  • Uses engine="openpyxl" for .xlsx files
  • Returns: pd.Series indexed by integer year

Usage

python
import sys
sys.path.insert(0, '/app/environment/skills/evo-macro-data-ingestion/scripts')
from utils import parse_erp_annual_total, process_cpi_annual

pce_nominal = parse_erp_annual_total('/root/ERP-2025-table10.xls', target_year=2024)
pfi_nominal = parse_erp_annual_total('/root/ERP-2025-table12.xls', target_year=2024)
cpi_annual = process_cpi_annual('/root/CPI.xlsx')

Data Format Notes

ERP Tables (.xls)

  • Rows 0-4: Multi-row headers (title, units, column labels)
  • Row 5+: Data rows with Period in col 0, Total in col 1
  • Years have trailing periods: '1973.'
  • Quarterly data appended at bottom: '2024: I.', ' II.', ' III p.'
  • Use xlrd engine (xlrd 2.0.1 only supports .xls)

CPI File (.xlsx)

  • May be annual (columns: Year, CPI_2024) or monthly FRED data
  • Use openpyxl engine for .xlsx files
  • CPI_2024 is normalized to 2024=1.0

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Evo Macro Data Ingestion AI skill do?

Parses ERP .xls tables and CPI .xlsx files, handles multi-row headers, extracts annual totals, computes partial-year averages from quarterly data, and produces clean annual series indexed by integer year.

Why use Evo Macro Data Ingestion on TypingMind?

Because you install it once and use it with any model. Evo Macro Data Ingestion is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Evo Macro Data Ingestion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenLAIR/OpenSkill/tree/main/tasks-evolved/econ-detrending-correlation/environment/skills/evo-macro-data-ingestion. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Evo Macro Data Ingestion?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Evo Macro Data Ingestion?

As many as you like. As long as a model supports skills, you can use Evo Macro Data Ingestion with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Evo Macro Data Ingestion AI skill free?

Yes. It is published on GitHub by OpenLAIR under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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