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 beita6969/ScienceClaw --skill knowledge-discoverygit clone --depth 1 https://github.com/beita6969/ScienceClawWrote 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/beita6969/scienceclaw/knowledge-discovery)<a href="https://agentmods.dev/skills/beita6969/scienceclaw/knowledge-discovery"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/knowledge-discovery/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/beita6969/scienceclaw/knowledge-discovery"><img src="https://agentmods.dev/badge/skills/beita6969/scienceclaw/knowledge-discovery.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00019 | $0.00369 |
| Opus 5 | $0.00010 | $0.00185 |
| Sonnet 5 | $0.00004 | $0.00074 |
| Haiku 4.5 | $0.00002 | $0.00037 |
Grade A, and why
knowledge-discovery 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 9d 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
Knowledge Discovery & Graphs
Purpose
Discover hidden patterns, build knowledge graphs, and extract novel insights from structured and unstructured data.
Key Datasets
- WALS (wals.info): World Atlas of Language Structures — 192 linguistic features across 2,679 languages in CLDF format (CC-BY 4.0)
- HistWords (nlp.stanford.edu/projects/histwords): Historical word embeddings tracking semantic change across 4 languages over centuries (.npy/.pkl format)
Protocol
- Data exploration — Profile data, identify patterns, check distributions
- Feature engineering — Create derived features, temporal features, cross-references
- Pattern detection — Apply clustering, association rules, anomaly detection
- Knowledge graph construction — Build entity-relation graphs from discovered patterns
- Insight generation — Interpret patterns in domain context
- Validation — Verify discoveries against known phenomena
Discovery Types
- Linguistic typology: Cross-linguistic universals, language family features, areal patterns
- Semantic change: Word meaning evolution, neologism tracking, conceptual drift
- Scientific trends: Emerging research topics, citation patterns, collaboration networks
- Biomedical discovery: Drug repurposing candidates, gene-disease associations
Rules
- Distinguish between correlation and causation in discovered patterns
- Report statistical significance and effect sizes
- Validate against domain expertise and existing literature
- Handle missing data transparently
- For knowledge graphs, use standard ontologies (RDF, OWL) when possible
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.
- 9d ago First seen · 35 lines · 19 tokens per session scan A f9a845de0b19
knowledge-discovery is a skill published in the GitHub repository beita6969/ScienceClaw (898 stars, last pushed 3mo ago), licensed MIT. It adds 19 tokens to every session and 369 once invoked, about $0.0001 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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