Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/legendtkl/agentic-skill-routernpx agentmods add skills/legendtkl/agentic-skill-router/skill-114Wrote 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-114)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-114"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-114/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-114"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-114.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 | $0.00019 | $0.00424 |
| Opus 5 | $0.00010 | $0.00212 |
| Sonnet 5 | $0.00004 | $0.00085 |
| Haiku 4.5 | $0.00002 | $0.00042 |
Grade A, and why
skill-114 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.
What it actually says
Data Analysis Workflow
Overview
This skill provides a systematic approach to analyzing both quantitative and qualitative research data, enabling researchers to derive actionable insights. It emphasizes best practices in data cleaning, exploratory data analysis, and visual representation of findings.
When to Use This Skill
Use this skill when:
- You need to analyze survey data or experimental results.
- Cleaning raw data from various sources for analysis.
- Visualizing data trends and patterns effectively.
- Preparing data reports for scholarly publications.
Data Cleaning Techniques
Handling Missing Data
Identify and manage missing data points to improve the integrity of your analysis.
Example of dealing with missing values:
python scripts/handle_missing_data.py --input data/survey_results.csv --method mean_imputation
Data Transformation
Transform variables for better analysis. This could involve normalization or encoding categorical variables.
Example of normalization:
python scripts/normalize_data.py --input data/raw_data.csv --output data/normalized_data.csv
Exploratory Data Analysis (EDA)
Perform EDA to summarize the main characteristics of your data and uncover patterns or anomalies.
Visualization Techniques
Use libraries such as Matplotlib and Seaborn to visualize your data effectively.
Example of generating a scatter plot:
python scripts/generate_scatter_plot.py --input data/normalized_data.csv --x_variable age --y_variable satisfaction
Reporting Results
Compile results from your analysis into a comprehensible report.
Example of generating a data report:
python scripts/generate_report.py --input data/analysis_results.csv --output report/analysis_report.pdf
Conclusion
By applying this skill, researchers can enhance their data analysis capabilities and ensure that their findings are robust and well-presented, leading to informed decision-making in their respective fields.
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 · 68 lines · 19 tokens per session scan A 79c5aec0d440
skill-114 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 424 once invoked, about $0.0001 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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