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-071Wrote 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-071)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-071"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-071/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-071"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-071.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 | $0.00020 | $0.00440 |
| Opus 5 | $0.00010 | $0.00220 |
| Sonnet 5 | $0.00004 | $0.00088 |
| Haiku 4.5 | $0.00002 | $0.00044 |
Grade A, and why
skill-071 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.
What it actually says
Biomedical Data Mining
Overview
This skill focuses on the extraction and analysis of large biomedical datasets, enabling researchers to discover significant insights through data mining techniques. It is particularly useful for identifying trends, relationships, and anomalies in health-related data.
When to Use This Skill
Use this skill when:
- Analyzing large datasets from clinical trials, genomic studies, or electronic health records.
- Seeking to identify correlations between variables in health data.
- Mining unstructured data from research articles or clinical notes.
- Developing predictive models for health outcomes.
Data Mining Techniques
Association Rule Learning
Discover interesting relationships between variables in large datasets. For example, identifying common comorbidities in patients.
Example of running an association rule mining algorithm:
python scripts/association_rule_mining.py --input data/clinical_trials.csv --min_support 0.5
Clustering Analysis
Group similar data points to uncover patterns and relationships. This can help in understanding patient populations or treatment responses.
Example of clustering patient data:
python scripts/clustering_analysis.py --input data/patient_data.csv --algorithm kmeans --num_clusters 5
Text Mining for Literature Review
Analyze text data from research publications to extract relevant findings, keywords, or entities.
Example of text mining publication abstracts:
python scripts/text_mining.py --input data/publication_abstracts.txt --output extracted_keywords.csv
Data Interpretation
Interpreting the results of your data mining efforts is crucial. Use statistical methods to validate and understand the significance of your findings.
Statistical Validation Example
python scripts/statistical_validation.py --input data/mining_results.csv --method t-test
Conclusion
Employing this skill allows researchers to leverage data mining techniques effectively, leading to groundbreaking insights in biomedical research that can inform clinical practices and health policies.
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 · 64 lines · 20 tokens per session scan A 9f3ab73876d1
skill-071 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 20 tokens to every session and 440 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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