Vibe-Skills is a collection and routing system that helps AI agents discover, select, and coordinate specialized skills for completing tasks. It is intended for agents that need to organize workflows across many installed capabilities. The catalogue entries are skills and an agent belonging to this system.
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 foryourhealth111-pixel/Vibe-Skills --skill etetoolkitgit clone --depth 1 https://github.com/foryourhealth111-pixel/Vibe-SkillsWrote 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/foryourhealth111-pixel/vibe-skills/etetoolkit)<a href="https://agentmods.dev/skills/foryourhealth111-pixel/vibe-skills/etetoolkit"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/etetoolkit/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/foryourhealth111-pixel/vibe-skills/etetoolkit"><img src="https://agentmods.dev/badge/skills/foryourhealth111-pixel/vibe-skills/etetoolkit.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 | $0.00046 | $0.04512 |
| Opus 5 | $0.00023 | $0.02256 |
| Sonnet 5 | $0.00009 | $0.00902 |
| Haiku 4.5 | $0.00005 | $0.00451 |
Grade B, and why
etetoolkit scanned grade B with 1 finding 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 9d 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.
Asks for rootmediumPrivilege escalation
A mod that escalates privileges can change anything on the machine, not only the project.
sudo apt-get install python3-pyqt5 python3-pyqt5.qtsvg This is a copy
94% identical to etetoolkit — 7 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 — 618 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ETE Toolkit Skill
Overview
ETE (Environment for Tree Exploration) is a toolkit for phylogenetic and hierarchical tree analysis. Manipulate trees, analyze evolutionary events, visualize results, and integrate with biological databases for phylogenomic research and clustering analysis.
Core Capabilities
1. Tree Manipulation and Analysis
Load, manipulate, and analyze hierarchical tree structures with support for:
- Tree I/O: Read and write Newick, NHX, PhyloXML, and NeXML formats
- Tree traversal: Navigate trees using preorder, postorder, or levelorder strategies
- Topology modification: Prune, root, collapse nodes, resolve polytomies
- Distance calculations: Compute branch lengths and topological distances between nodes
- Tree comparison: Calculate Robinson-Foulds distances and identify topological differences
Common patterns:
from ete3 import Tree
# Load tree from file
tree = Tree("tree.nw", format=1)
# Basic statistics
print(f"Leaves: {len(tree)}")
print(f"Total nodes: {len(list(tree.traverse()))}")
# Prune to taxa of interest
taxa_to_keep = ["species1", "species2", "species3"]
tree.prune(taxa_to_keep, preserve_branch_length=True)
# Midpoint root
midpoint = tree.get_midpoint_outgroup()
tree.set_outgroup(midpoint)
# Save modified tree
tree.write(outfile="rooted_tree.nw")
Use scripts/tree_operations.py for command-line tree manipulation:
# Display tree statistics
python scripts/tree_operations.py stats tree.nw
# Convert format
python scripts/tree_operations.py convert tree.nw output.nw --in-format 0 --out-format 1
# Reroot tree
python scripts/tree_operations.py reroot tree.nw rooted.nw --midpoint
# Prune to specific taxa
python scripts/tree_operations.py prune tree.nw pruned.nw --keep-taxa "sp1,sp2,sp3"
# Show ASCII visualization
python scripts/tree_operations.py ascii tree.nw
2. Phylogenetic Analysis
Analyze gene trees with evolutionary event detection:
- Sequence alignment integration: Link trees to multiple sequence alignments (FASTA, Phylip)
- Species naming: Automatic or custom species extraction from gene names
- Evolutionary events: Detect duplication and speciation events using Species Overlap or tree reconciliation
- Orthology detection: Identify orthologs and paralogs based on evolutionary events
- Gene family analysis: Split trees by duplications, collapse lineage-specific expansions
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.
- 9d ago First seen · 618 lines · 46 tokens per session scan B 31fe1b501aee
etetoolkit is a skill published in the GitHub repository foryourhealth111-pixel/Vibe-Skills (3,252 stars, last pushed 12d ago), licensed Apache-2.0. It adds 46 tokens to every session and 4,512 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it B with 1 finding (asks for root). It is 94% identical to etetoolkit, differing in 7 lines, and is treated as a copy.
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Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
research-paper-writing
End-to-end pipeline for writing ML/AI research papers — from experiment design through analysis, drafting, revision, and submission. Covers NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Integrates automated experiment monitoring, statistical analysis, iterative writing, and citation verification.