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 veritasreasongit 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/veritasreason)<a href="https://agentmods.dev/skills/bibinprathap/veritasgraph/veritasreason"><img src="https://agentmods.dev/badge/skills/bibinprathap/veritasgraph/veritasreason/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/veritasreason"><img src="https://agentmods.dev/badge/skills/bibinprathap/veritasgraph/veritasreason.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 | $0.00038 | $0.00748 |
| Opus 5.5 | $0.00015 | $0.00299 |
| Sonnet 5.5 | $0.00008 | $0.00150 |
| Haiku 4.5 | $0.00004 | $0.00075 |
Grade A, and why
veritasreason 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 2mo 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
91% identical to semantica — 12 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 — 58 lines — stays where its author put it; the contents beside it link to each section on GitHub.
VeritasReason
This Skill helps Claude apply VeritasReason knowledge graph capabilities to context graph analysis, decision intelligence, explainability, semantic extraction, graph analytics, reasoning, provenance, ontology, policy, ingestion, deduplication, and export.
When to use this Skill
- The user asks about knowledge graphs, entities, relations, triplets, or semantic extraction.
- A task requires context graph analysis, graph topology, centrality, communities, paths, or embeddings.
- The request involves decision intelligence, causal influence, decision graphs, or outcome analysis.
- The user asks for explainability, decision rationale, or transparency for graph results.
- The request involves reasoning: deductive, abductive, SPARQL, Datalog, or Rete rules.
- The user needs provenance, audit history, lineage tracking, or change tracing.
- The request is about ontology modeling, schema validation, or policy enforcement.
- Data must be ingested from files, databases, APIs, repositories, or MCP servers.
- There is a need to deduplicate entities, normalize graph data, or merge duplicate graph objects.
- The user wants to export graphs to JSON, RDF, Parquet, CSV, GraphML, or similar.
What this Skill contains
- Semantic extraction guidance for NER, relation extraction, event detection, coreference resolution, and triplet generation.
- Context graph and graph analytics workflows for topology, centrality, community detection, path finding, embeddings, and decision insights.
- Decision intelligence support for causal reasoning, decision impact, decision graphs, and outcome analysis.
- Explainability guidance for decision rationale, graph reasoning, rule traces, and result transparency.
- Reasoning support for logic, hypotheses, SPARQL, Datalog, and rule-based inference.
- Provenance and audit guidance for tracing sources, recording changes, and verifying graph lineage.
- Ontology guidance for defining concepts, validating schemas, and modeling relationships.
- Policy checks for compliance evaluation and graph governance.
- Temporal analysis guidance for event timelines and graph evolution.
- Deduplication support for duplicate detection, fuzzy matching, and graph cleanup.
- Export workflows for sharing results in multiple structured formats.
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.
- 2mo ago First seen · 58 lines · 38 tokens per session scan A 9a50f3ac1949
veritasreason is a skill published in the GitHub repository bibinprathap/VeritasGraph (326 stars, last pushed today), licensed MIT. It adds 38 tokens to every session and 748 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 91% identical to semantica, differing in 12 lines, and is treated as a copy.
veritasreason compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| veritasreasonbibinprathap/VeritasGraph | Skill | 326 | today | MIT | A | 38 | Claude Code |
| document-chunkerellmos-ai/skills | Skill | 9 | today | MIT | A | 230 | Claude Code, Codex |
| dspyNousResearch/hermes-agent | Skill | 262k | today | MIT | A | 19 | Claude Code, Codex |
| chromaNousResearch/hermes-agent | Skill | 262k | today | MIT | A | 12 | Claude Code, Codex |
| faissNousResearch/hermes-agent | Skill | 262k | today | MIT | A | 11 | Claude Code, Codex |
| ai-engineering-toolkitsickn33/agentic-awesome-skills | Skill | 49k | yesterday | MIT | A | 47 | Claude Code |
| memory-rag-poisoning-detectionTencent/AI-Infra-Guard | Skill | 7.4k | 3d ago | Apache-2.0 | A | 48 | Claude Code |