SkillsBench is a benchmark for measuring how effectively AI agents use modular skills—folders containing instructions, scripts, and resources—to complete specialized tasks. It helps researchers and developers evaluate both skill quality and agent behavior, including tasks that require combining multiple skills. The catalogue’s skills and instructions are evaluated as part of this workflow.
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 benchflow-ai/skillsbench --skill hierarchical-taxonomy-clusteringgit clone --depth 1 https://github.com/benchflow-ai/skillsbenchWrote 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/benchflow-ai/skillsbench/hierarchical-taxonomy-clustering)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/hierarchical-taxonomy-clustering"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/hierarchical-taxonomy-clustering/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/benchflow-ai/skillsbench/hierarchical-taxonomy-clustering"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/hierarchical-taxonomy-clustering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00038 | $0.00952 |
| Opus 5 | $0.00019 | $0.00476 |
| Sonnet 5 | $0.00008 | $0.00190 |
| Haiku 4.5 | $0.00004 | $0.00095 |
Grade A, and why
hierarchical-taxonomy-clustering 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 11d 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.
Copies of this mod
2 near-identical copies found in the catalogue:
- hierarchical-taxonomy-clustering — 100% identical, 0 lines differ
- skill-068 — 95% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 72 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 ships with it
5 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 72 lines · 38 tokens per session scan A 4442f0462e66
hierarchical-taxonomy-clustering is a skill published in the GitHub repository benchflow-ai/skillsbench (1,760 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 38 tokens to every session and 952 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 2026-08-30.
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