Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.
59
68%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
Fix and improve this skill with Tessl
tessl review fix ./understand-anything-plugin/skills/understand-knowledge/SKILL.mdLow
Low-risk findings.
1 low severity finding. Worth noting, but not necessarily harmful.
The skill exposes the agent to untrusted, user-generated content from public third-party sources, creating a risk of indirect prompt injection. This includes browsing arbitrary URLs, reading social media posts or forum comments, and analyzing content from unknown websites.
Yes: the required workflow reads outsider-authored wiki markdown files from the user-supplied target directory (e.g., `wiki_root.rglob("*.md")` in `parse-knowledge-base.py`), places their free text into `scan-manifest.json` as `knowledgeMeta.content`, and then the Phase 3 `article-analyzer` subagents feed that content into the LLM context as “untrusted article data”.
6ae7187
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.