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 ItamarZand88/awesome-agent-conventions --skill research-time-series-econometricsgit clone --depth 1 https://github.com/ItamarZand88/awesome-agent-conventionsWrote 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/itamarzand88/awesome-agent-conventions/research-time-series-econometrics)<a href="https://agentmods.dev/skills/itamarzand88/awesome-agent-conventions/research-time-series-econometrics"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/research-time-series-econometrics/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/itamarzand88/awesome-agent-conventions/research-time-series-econometrics"><img src="https://agentmods.dev/badge/skills/itamarzand88/awesome-agent-conventions/research-time-series-econometrics.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 | $0.00000 | $0.01767 |
| Opus 5 | $0.00000 | $0.00883 |
| Sonnet 5 | $0.00000 | $0.00353 |
| Haiku 4.5 | $0.00000 | $0.00177 |
Grade A, and why
research-time-series-econometrics 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 12d 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.
This is a copy
91% identical to time-series-guide — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 237 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: time-series-guide description: "Apply ARIMA, VAR, cointegration, and time series econometric methods" metadata: openclaw: emoji: "📉" category: "analysis" subcategory: "econometrics" keywords: ["time series", "ARIMA", "VAR", "cointegration", "stationarity", "forecasting", "econometrics"] source: "wentor-research-plugins"
Time Series Guide
A skill for applying time series econometric methods including ARIMA modeling, VAR systems, cointegration analysis, and unit root tests. Covers stationarity concepts, model selection, forecasting, and diagnostic checking for economic and financial data.
Stationarity and Unit Root Tests
Why Stationarity Matters
A time series is stationary when its statistical properties (mean, variance, autocorrelation) do not change over time. Most econometric methods require stationarity. Non-stationary series can produce spurious regressions.
Testing for Stationarity
from statsmodels.tsa.stattools import adfuller, kpss
import pandas as pd
def test_stationarity(series: pd.Series, name: str = "Series") -> dict:
"""
Test for stationarity using ADF and KPSS tests.
Args:
series: Time series data
name: Label for the series
"""
# Augmented Dickey-Fuller test
# H0: Unit root exists (non-stationary)
adf_result = adfuller(series.dropna(), autolag="AIC")
# KPSS test
# H0: Series is stationary
kpss_result = kpss(series.dropna(), regression="c", nlags="auto")
return {
"series": name,
"adf": {
"statistic": adf_result[0],
"p_value": adf_result[1],
"lags_used": adf_result[2],
"conclusion": (
"Stationary (reject unit root)"
if adf_result[1] < 0.05
else "Non-stationary (fail to reject unit root)"
)
},
"kpss": {
"statistic": kpss_result[0],
"p_value": kpss_result[1],
"conclusion": (
"Non-stationary (reject stationarity)"
if kpss_result[1] < 0.05
else "Stationary (fail to reject stationarity)"
)
}
}
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.
- 12d ago First seen · 237 lines · 0 tokens per session scan A b37b44c1f77f
research-time-series-econometrics is a skill published in the GitHub repository ItamarZand88/awesome-agent-conventions (31 stars, last pushed 1mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,767 tokens. A static security scan graded it A with 0 findings. It is 91% identical to time-series-guide, differing in 1 line, and is treated as a copy.
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