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 agentmods add skills/legendtkl/agentic-skill-router/skill-009npx skills add legendtkl/agentic-skill-router --skill skill-009git 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-009)<a href="https://agentmods.dev/skills/legendtkl/agentic-skill-router/skill-009"><img src="https://agentmods.dev/badge/skills/legendtkl/agentic-skill-router/skill-009.svg" alt="Measured on agentmods" 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 | $0.00031 | $0.00584 |
| Opus 5 | $0.00015 | $0.00292 |
| Sonnet 5 | $0.00006 | $0.00117 |
| Haiku 4.5 | $0.00003 | $0.00058 |
Grade A, and why
skill-009 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
97% identical to dialogue-graph — 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.
What it actually says
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 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 · 100 lines · 31 tokens per session scan A b96a671cecec
skill-009 is a skill published in the GitHub repository legendtkl/agentic-skill-router (5 stars, last pushed 3mo ago), licensed MIT. It adds 31 tokens to every session and 584 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 97% identical to dialogue-graph, differing in 3 lines, and is treated as a copy.
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