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-065git 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-065)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-065"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-065/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-065"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-065.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 | $0.00027 | $0.00598 |
| Opus 5 | $0.00014 | $0.00299 |
| Sonnet 5 | $0.00005 | $0.00120 |
| Haiku 4.5 | $0.00003 | $0.00060 |
Grade A, and why
skill-065 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 — 69 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Text Data Analysis Framework
This framework provides a general approach to analyzing text data, suitable for various applications including categorization, clustering, and summarization. It applies fundamental natural language processing and machine learning techniques.
Overview
Text data is becoming increasingly important in various fields. An effective framework for analyzing this data must include several key components:
- Data Collection: Gather text data from multiple sources like social media, customer reviews, articles, etc.
- Preprocessing: Clean and prepare the data, applying necessary transformations such as tokenization, normalization, and lemmatization.
- Analysis Techniques: Implement various methods like clustering for categorization, sentiment analysis for opinions, and summarization for extracting key insights.
General Methodology
- Data Collection: Use APIs or web scraping to gather text data.
- Preprocessing: Remove irrelevant characters, convert text to lowercase, and perform stemming or lemmatization.
- Feature Extraction: Convert text to vector representations using techniques like TF-IDF or word embeddings.
- Modeling: Apply machine learning models for classification or clustering tasks.
- Visualization: Generate visualizations to interpret results, using libraries like Matplotlib or Seaborn.
Output
The framework can produce various outputs:
- Categorized data sets
- Clusters of similar text data
- Summarized reports of key findings
Installation
pip install pandas numpy nltk scikit-learn matplotlib seaborn
General Steps Involved
Step 1: Load and Prepare Data
- Input: Text data from various sources.
- Process: Normalize and clean the text.
- Output: Clean text data ready for analysis.
Step 2: Analyze Text Data
- Input: Cleaned text data.
- Process: Apply chosen analysis methods.
- Output: Results based on the analysis applied.
Step 3: Visualize Findings
- Input: Results from the analysis.
- Process: Use visualization techniques to present findings.
- Output: Graphs and charts summarizing the results.
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 · 69 lines · 27 tokens per session scan A f82f8876b713
skill-065 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 27 tokens to every session and 598 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.
Other skills, from other repositories
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
foundry-config-setup
Resolve missing setup caused by a hardcoded Foundry project endpoint or model in a sample. Use when a sample fails because it uses a placeholder/hardcoded projectendpoint (for example "https://your-project.services.ai.azure.com") or a hardcoded model instead of reading them from the environment.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…
training-check
Interactively monitor training metrics from the current Codex session, periodically checking WandB or fallback logs for NaN, divergence, plateaus, and broken runs.
nemo-automodel-launcher-config
Configure NeMo AutoModel job launches for interactive runs, Slurm clusters, and SkyPilot cloud execution.