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 agentmods add skills/legendtkl/agentic-skill-router/skill-019npx skills add legendtkl/agentic-skill-router --skill skill-019git 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-019)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-019"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-019.svg" alt="Measured on agentmods" 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 | $0.00039 | $0.00672 |
| Opus 5 | $0.00019 | $0.00336 |
| Sonnet 5 | $0.00008 | $0.00134 |
| Haiku 4.5 | $0.00004 | $0.00067 |
Grade A, and why
skill-019 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 6d 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.
Financial Data Normalization
Overview
Financial Data Normalization offers methods to standardize and clean financial datasets from different sources. Consistent financial reporting is crucial for accurate analysis, budgeting, and forecasting.
This skill covers:
- Currency Conversion: Standardizing values to a common currency.
- Format Consistency: Ensuring uniformity in number formatting, including decimal places and thousands separators.
- Data Quality Checks: Identifying and correcting discrepancies in financial records.
- Aggregation Techniques: Combining data from multiple sources into a coherent dataset for analysis.
When to Use This Skill
Use this skill when:
- Integrating financial data from different departments or organizations.
- Converting financial values into a single currency for consolidated reporting.
- Cleaning up financial records with varying formats or discrepancies.
- Preparing datasets for financial modeling or predictive analysis.
- Ensuring compliance with financial reporting standards.
Data Quality Issues Reference
Financial datasets often contain various quality issues that must be addressed:
| Issue Type | Description | Typical Prevalence | Example |
|---|---|---|---|
| Inconsistent Currencies | Values reported in different currencies | 10-20% | Sales reported in USD vs EUR |
| Decimal Formatting | Variation in decimal places used | 15-30% | 1,200.50 vs 1200.50 |
| Leading Zeros | Presence of unnecessary leading zeros | 5-10% | 000123.45 vs 123.45 |
| Whitespace Issues | Extra spaces or tabs in values | 10-15% | 100.00 vs 100.00 |
| Missing Values | Rows with missing financial information | Variable | NaN, -999, blank |
Currency Conversion Example
When dealing with multiple currencies, you can use the following Python code to convert financial values:
conversion_rates = {
'USD': 1,
'EUR': 1.1,
'JPY': 0.009
}
def convert_currency(amount, from_currency, to_currency):
return amount * conversion_rates[to_currency] / conversion_rates[from_currency]
# Example Usage
amount_in_usd = convert_currency(100, 'EUR', 'USD')
print(f'100 EUR is equivalent to {amount_in_usd:.2f} USD')
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.
- 6d ago First seen · 75 lines · 39 tokens per session scan A 46631c887e62
skill-019 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 39 tokens to every session and 672 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 2026-08-31.
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