Evo Macro Cycle Correlation

Organization

OpenLAIR

evo-macro-cycle-correlation

Deflates nominal series to real values using CPI, applies log transformation and Hodrick-Prescott filter to extract cyclical components, and computes Pearson correlation between two cyclical series.

Overview

Publisher	OpenLAIR
Repository	`OpenSkill`
Skill name	`evo-macro-cycle-correlation`
Stars	97
Forks	4
Bundled files	1
License	Apache-2.0
Links	Skill source Repository Homepage

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.
1 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 Cycle Correlation AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/OpenLAIR/OpenSkill/tree/main/tasks-evolved/econ-detrending-correlation/environment/skills/evo-macro-cycle-correlation
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/OpenLAIR/OpenSkill/tree/main/tasks-evolved/econ-detrending-correlation/environment/skills/evo-macro-cycle-correlation

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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-cycle-correlation .claude/skills/evo-macro-cycle-correlation

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Evo Macro Cycle Correlation 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 Cycle Correlation 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 Cycle Correlation 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-cycle-correlation

Deflates nominal macroeconomic series, applies HP filter for cycle extraction, and computes Pearson correlation between cyclical components.

Key Functions

`deflate_nominal_to_real(nominal, cpi)`

Converts nominal to real values: Real_t = (Nominal_t / CPI_t) * 100

`extract_hp_cycle(real_series, lamb=100)`

Applies np.log() then HP filter (statsmodels hpfilter) to extract cyclical component.

Uses natural log (np.log, NOT np.log10)
hpfilter returns (cycle, trend) - parameter name is lamb
lamb=100 for annual data (standard)

`compute_cycle_pearson_correlation(cycle1, cycle2)`

Computes Pearson r using scipy.stats.pearsonr (returns PearsonRResult.statistic)

`run_full_pipeline(pce_file, pfi_file, cpi_file, ...)`

Complete end-to-end pipeline: parse -> deflate -> HP filter -> correlate -> output. Depends on evo-macro-data-ingestion for data parsing.

Usage

python
import sys
sys.path.insert(0, '/app/environment/skills/evo-macro-cycle-correlation/scripts')
from utils import run_full_pipeline

correlation = run_full_pipeline(
    pce_file='/root/ERP-2025-table10.xls',
    pfi_file='/root/ERP-2025-table12.xls',
    cpi_file='/root/CPI.xlsx',
    start_year=1973,
    end_year=2024,
    hp_lambda=100,
    output_file='/root/answer.txt'
)

Technical Details

Deflation

Formula: Real = (Nominal / CPI) * 100
CPI base period doesn't matter for HP cycle analysis (log differences cancel it)

HP Filter

Import: from statsmodels.tsa.filters.hp_filter import hpfilter
Signature: hpfilter(x, lamb=1600) - use lamb=100 for annual data
Returns: (cycle, trend) tuple
Input must have NO NaN values

Pearson Correlation

scipy.stats.pearsonr returns PearsonRResult object (scipy 1.14.1)
Access correlation via .statistic attribute
Output formatted with f"{value:.5f}" for exactly 5 decimal places

Year Range

Use pandas .loc[1973:2024] for inclusive filtering
.loc is inclusive of both start and stop labels

Bundled files

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

scripts/utils.py5 KB

Frequently asked questions

What does the Evo Macro Cycle Correlation AI skill do?

Deflates nominal series to real values using CPI, applies log transformation and Hodrick-Prescott filter to extract cyclical components, and computes Pearson correlation between two cyclical series.

Why use Evo Macro Cycle Correlation on TypingMind?

Because you install it once and use it with any model. Evo Macro Cycle Correlation 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 Cycle Correlation 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-cycle-correlation. 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 Cycle Correlation?

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 Cycle Correlation?

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

Is the Evo Macro Cycle Correlation 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

evo-macro-cycle-correlation

Key Functions

deflate_nominal_to_real(nominal, cpi)

extract_hp_cycle(real_series, lamb=100)

compute_cycle_pearson_correlation(cycle1, cycle2)

run_full_pipeline(pce_file, pfi_file, cpi_file, ...)

Usage

Technical Details

Deflation

HP Filter

Pearson Correlation

Year Range

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now

`deflate_nominal_to_real(nominal, cpi)`

`extract_hp_cycle(real_series, lamb=100)`

`compute_cycle_pearson_correlation(cycle1, cycle2)`

`run_full_pipeline(pce_file, pfi_file, cpi_file, ...)`