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 bibinprathap/VeritasGraph --skill visualizegit clone --depth 1 https://github.com/bibinprathap/VeritasGraphWrote 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/bibinprathap/veritasgraph/visualize)<a href="https://agentmods.dev/skills/bibinprathap/veritasgraph/visualize"><img src="https://agentmods.dev/badge/skills/bibinprathap/veritasgraph/visualize/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/bibinprathap/veritasgraph/visualize"><img src="https://agentmods.dev/badge/skills/bibinprathap/veritasgraph/visualize.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 | $0.00000 | $0.01741 |
| Opus 5.5 | $0.00000 | $0.00696 |
| Sonnet 5.5 | $0.00000 | $0.00348 |
| Haiku 4.5 | $0.00000 | $0.00174 |
Grade A, and why
visualize 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 1mo 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
98% identical to visualize — 36 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 — 250 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/veritasreason:visualize
Render graph visualizations as Mermaid, ASCII, or structured Markdown. Usage: /veritasreason:visualize <sub-command> [args]
$ARGUMENTS = sub-command + optional node label or filter.
topology [--filter <node_type>]
Full graph structure analysis — node types, edge distribution, connectivity metrics.
from veritasreason.kg.graph_analyzer import GraphAnalyzer
from veritasreason.context import ContextGraph
graph = ContextGraph(advanced_analytics=True)
analyzer = GraphAnalyzer()
# Comprehensive analysis
analysis = analyzer.analyze_graph(graph=graph.to_dict())
metrics = analyzer.compute_metrics(graph=graph)
connectivity = analyzer.analyze_connectivity(graph=graph)
Output:
Graph Topology:
Nodes: N (M types)
Edges: P
Density: 0.23
Avg degree: 4.7
Connected: YES / NO (K components)
Node type distribution:
[Mermaid pie chart]
| Type | Count | % | Avg Degree |
Top-10 connected nodes:
| Node | Type | Degree | Betweenness |
centrality [--type degree|betweenness|closeness|eigenvector|pagerank|all] [--top N]
Calculate and rank nodes by centrality.
from veritasreason.kg.centrality_calculator import CentralityCalculator
from veritasreason.context import ContextGraph
graph = ContextGraph()
calc = CentralityCalculator()
if centrality_type == "all" or not centrality_type:
scores = calc.calculate_all_centrality(graph=graph)
elif centrality_type == "degree":
scores = calc.calculate_degree_centrality(graph=graph)
elif centrality_type == "betweenness":
scores = calc.calculate_betweenness_centrality(graph=graph)
elif centrality_type == "closeness":
scores = calc.calculate_closeness_centrality(graph=graph)
elif centrality_type == "eigenvector":
scores = calc.calculate_eigenvector_centrality(graph=graph)
elif centrality_type == "pagerank":
scores = calc.calculate_pagerank(
graph=graph,
max_iterations=20,
damping_factor=0.85,
)
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.
- 1mo ago First seen · 250 lines · 0 tokens per session scan A 2a720aa74626
visualize is a skill published in the GitHub repository bibinprathap/VeritasGraph (325 stars, last pushed 4d ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,741 tokens. A static security scan graded it A with 0 findings. It is 98% identical to visualize, differing in 36 lines, and is treated as a copy.
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