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 dialogue-graphgit 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/dialogue-graph)<a href="https://agentmods.dev/skills/benchflow-ai/skillsbench/dialogue-graph"><img src="https://agentmods.dev/badge/skills/benchflow-ai/skillsbench/dialogue-graph.svg" alt="Measured on agentmods" 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 | $0.00032 | $0.00585 |
| Opus 5 | $0.00016 | $0.00293 |
| Sonnet 5 | $0.00006 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00059 |
Grade A, and why
dialogue-graph 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 4d 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
4 near-identical copies found in the catalogue:
- dialogue-graph — 100% identical, 0 lines differ
- dialogue-graph — 100% identical, 0 lines differ
- dialogue-graph — 100% identical, 0 lines differ
- skill-009 — 97% identical, 3 lines differ
How it starts
The opening of the file, as written. The whole thing — 101 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Dialogue Graph Skill
This skill provides a dialogue_graph module to easily build valid dialogue trees/graphs.
When to use
- Script Parsers: When converting text to data.
- Dialogue Editors: When building tools to edit conversation flow.
- Game Logic: When traversing a dialogue tree.
- Visualization: When generating visual diagrams of dialogue flows.
How to use
Import the module:
from dialogue_graph import Graph, Node, Edge
1. The Graph Class
The main container.
graph = Graph()
2. Adding Nodes
Define content nodes.
# Regular line
graph.add_node(Node(id="Start", speaker="Guard", text="Halt!", type="line"))
# Choice hub
graph.add_node(Node(id="Choices", type="choice"))
3. Adding Edges
Connect nodes (transitions).
# Simple transition
graph.add_edge(Edge(source="Start", target="Choices"))
# Choice transition (with text)
graph.add_edge(Edge(source="Choices", target="End", text="1. Run away"))
4. Export
Serialize to JSON format for the engine.
data = graph.to_dict()
# returns {"nodes": [...], "edges": [...]}
json_str = graph.to_json()
5. Validation
Check for integrity.
errors = graph.validate()
# Returns list of strings, e.g., ["Edge 'Start'->'Unk' points to missing node 'Unk'"]
6. Visualization
Generate a PNG/SVG graph diagram.
# Requires: pip install graphviz
# Also requires Graphviz binary: https://graphviz.org/download/
graph.visualize('dialogue_graph') # Creates dialogue_graph.png
graph.visualize('output', format='svg') # Creates output.svg
The visualization includes:
- Diamond shapes for choice nodes (light blue)
- Rounded boxes for dialogue nodes (colored by speaker)
- Bold blue edges for skill-check choices like
[Lie],[Attack] - Gray edges for regular choices
- Black edges for simple transitions
7. Loading from JSON
Load an existing dialogue graph.
# From file
graph = Graph.from_file('dialogue.json')
# From dict
graph = Graph.from_dict({'nodes': [...], 'edges': [...]})
# From JSON string
graph = Graph.from_json(json_string)
What ships with it
1 file 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.
- 4d ago First seen · 101 lines · 32 tokens per session scan A 03d711b93219
dialogue-graph is a skill published in the GitHub repository benchflow-ai/skillsbench (1,748 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 32 tokens to every session and 585 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 2026-09-03.
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