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-068git 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-068)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-068"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-068/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-068"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-068.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 | $0.00035 | $0.00950 |
| Opus 5 | $0.00017 | $0.00475 |
| Sonnet 5 | $0.00007 | $0.00190 |
| Haiku 4.5 | $0.00003 | $0.00095 |
Grade A, and why
skill-068 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.
This is a copy
95% identical to hierarchical-taxonomy-clustering — 3 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hierarchical Taxonomy Clustering
Create a unified multi-level taxonomy from hierarchical category paths by clustering similar paths and automatically generating meaningful category names.
Problem
Given category paths from multiple sources (e.g., "electronics -> computers -> laptops"), create a unified taxonomy that groups similar paths across sources, generates meaningful category names, and produces a clean N-level hierarchy (typically 5 levels). The unified category taxonomy could be used to do analysis or metric tracking on products from different platform.
Methodology
- Hierarchical Weighting: Convert paths to embeddings with exponentially decaying weights (Level i gets weight 0.6^(i-1)) to signify the importance of category granularity
- Recursive Clustering: Hierarchically cluster at each level (10-20 clusters at L1, 3-20 at L2-L5) using cosine distance
- Intelligent Naming: Generate category names via weighted word frequency + lemmatization + bundle word logic
- Quality Control: Exclude all ancestor words (parent, grandparent, etc.), avoid ancestor path duplicates, clean special characters
Output
DataFrame with added columns:
unified_level_1: Top-level category (e.g., "electronic | device")unified_level_2: Second-level category (e.g., "computer | laptop")unified_level_3throughunified_level_N: Deeper levels
Category names use | separator, max 5 words, covering 70%+ of records in each cluster.
Installation
pip install pandas numpy scipy sentence-transformers nltk tqdm
python -c "import nltk; nltk.download('wordnet'); nltk.download('omw-1.4')"
4-Step Pipeline
Step 1: Load, Standardize, Filter and Merge (step1_preprocessing_and_merge.py)
- Input: List of (DataFrame, source_name) tuples, each of the with
category_pathcolumn - Process: Per-source deduplication, text cleaning (remove &/,/'/-/quotes,'and' or "&", "," and so on, lemmatize words as nouns), normalize delimiter to
>, depth filtering, prefix removal, then merge all sources. source_level should reflect the processed version of the source level name - Output: Merged DataFrame with
category_path,source,depth,source_level_1throughsource_level_N
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 · 71 lines · 35 tokens per session scan A c43e918c5334
skill-068 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 35 tokens to every session and 950 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 95% identical to hierarchical-taxonomy-clustering, differing in 3 lines, and is treated as a copy.
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