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 skills add OpenLAIR/OpenSkill --skill evo-macro-cycle-correlationgit clone --depth 1 https://github.com/OpenLAIR/OpenSkillWrote 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/openlair/openskill/evo-macro-cycle-correlation)<a href="https://agentmods.dev/skills/openlair/openskill/evo-macro-cycle-correlation"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-macro-cycle-correlation/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/openlair/openskill/evo-macro-cycle-correlation"><img src="https://agentmods.dev/badge/skills/openlair/openskill/evo-macro-cycle-correlation.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00044 | $0.00579 |
| Opus 5 | $0.00022 | $0.00290 |
| Sonnet 5 | $0.00009 | $0.00116 |
| Haiku 4.5 | $0.00004 | $0.00058 |
Grade A, and why
evo-macro-cycle-correlation 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 today.
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 — 66 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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
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
.statisticattribute - 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
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today First seen · 66 lines · 44 tokens per session scan A 967d5079e80b
evo-macro-cycle-correlation is a skill published in the GitHub repository OpenLAIR/OpenSkill (88 stars, last pushed yesterday), licensed Apache-2.0. It adds 44 tokens to every session and 579 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 2026-09-11.
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