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 legendtkl/agentic-skill-router --skill skill-093git clone --depth 1 https://github.com/legendtkl/agentic-skill-routerWrote 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/legendtkl/agentic-skill-router/skill-093)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-093"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-093/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/legendtkl/agentic-skill-router/skill-093"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-093.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 | $0.00032 | $0.00441 |
| Opus 5 | $0.00016 | $0.00220 |
| Sonnet 5 | $0.00006 | $0.00088 |
| Haiku 4.5 | $0.00003 | $0.00044 |
Grade A, and why
skill-093 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 7d 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.
How it starts
The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Economic Data Analysis Techniques
This skill outlines various approaches for analyzing economic data, ranging from basic descriptive statistics to complex econometric modeling.
Overview
Economic data analysis provides insights into trends, patterns, and relationships within economic variables. Key areas of analysis include:
- Financial analysis (stock prices, interest rates)
- Labor market trends (employment rates, wages)
- Production and productivity indicators
Data Collection and Preparation
Before analysis, it is crucial to collect and prepare the data. This process usually involves:
- Identifying relevant data sources (government databases, financial markets)
- Cleaning and transforming data for analysis
- Ensuring data integrity and consistency across time periods
Descriptive Statistics
Descriptive statistics summarize the main features of a dataset, providing simple summaries about the sample and measures. Key metrics include:
- Mean, median, mode
- Variance and standard deviation
- Min and max values
Python Implementation
import pandas as pd
# Load your economic data
# data = pd.read_csv('your_data.csv')
# Calculate descriptive statistics
summary = data.describe()
print(summary)
Correlation and Regression Analysis
Correlation analysis helps identify relationships between variables, while regression analysis allows for the modeling of these relationships.
Correlation Analysis
The correlation coefficient (Pearson or Spearman) measures the strength of association between two variables.
Regression Analysis
Regression can be employed to predict the value of a variable based on the value of another variable.
Python Implementation
import statsmodels.api as sm
# Define independent and dependent variables
# X = data[['independent_variable']]
# y = data['dependent_variable']
# Add constant to the model
X = sm.add_constant(X)
# Fit the regression model
model = sm.OLS(y, X).fit()
print(model.summary())
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.
- 7d ago First seen · 75 lines · 32 tokens per session scan A 9c5fed4864ba
skill-093 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 32 tokens to every session and 441 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 2026-09-03.
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