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 querygit 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/query)<a href="https://agentmods.dev/skills/bibinprathap/veritasgraph/query"><img src="https://agentmods.dev/badge/skills/bibinprathap/veritasgraph/query/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/query"><img src="https://agentmods.dev/badge/skills/bibinprathap/veritasgraph/query.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 | $0.00026 | $0.00223 |
| Opus 5.5 | $0.00010 | $0.00089 |
| Sonnet 5.5 | $0.00005 | $0.00045 |
| Haiku 4.5 | $0.00003 | $0.00022 |
Grade A, and why
query 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.
What it actually says
/veritasreason:query
Run graph queries and search. Usage: /veritasreason:query <mode> [args]
$ARGUMENTS = query mode + query string or filter.
sparql <query>
Execute a SPARQL query against the graph.
from veritasreason.query import QueryEngine
engine = QueryEngine()
results = engine.query_sparql(query)
Return: query bindings as a Markdown table.
cypher <query>
Execute a Cypher-like query.
results = engine.query_cypher(query)
Output: node/relationship results and path summaries.
search <keywords> [--filter <type>]
Search graph entities by keyword.
results = engine.search(keywords=keywords, filter_type=filter_type)
Return: ranked matches with entity types and relevance scores.
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 · 50 lines · 26 tokens per session scan A 8a9b152aed4e
query is a skill published in the GitHub repository bibinprathap/VeritasGraph (325 stars, last pushed yesterday), licensed MIT. It adds 26 tokens to every session and 223 once invoked, about $0.0001 per session on Opus 5.5. 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.
Other skills, from other repositories
qdrant
Vector search engine for production RAG systems.
chroma
Embedding database for RAG and semantic search.
pinecone
Managed vector DB for production RAG and search.
smart-data-analysis
A central workflow for handling data questions and data assets across tables, views, metrics, SQL, charts, and Business Knowledge Networks.
ontology-core
A command-line tool for managing and querying Business Knowledge Networks, data sources, data views, and platform monitoring resources.
smart-ask-data
A guided workflow for answering data questions by selecting a Business Knowledge Network, finding its fields, generating SQL, and running the query.