Find, verify, and synthesize scientific literature — from "what's the seminal paper for X" through full multi-source reviews. Covers grounding claims in real retrieved sources, avoiding fabricated citations, handling retractions, and calibrating confidence to evidence strength.
65
80%
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 ./configs/microservice/bff-service/configs/agent-skills/claude-science/literature-review/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.
Outsider-authored free text can enter the LLM context via the skill’s runtime web/retrieval step (SKILL.md:20-27) where it performs “agent’s own web search” and “literature/data MCP tools” (e.g., Semantic Scholar/PubMed/preprints), and then uses that retrieved text to “build the answer” (SKILL.md:20-23); those fetched pages/messages are outsider content that the agent will likely paste into the LLM prompt for synthesis.
f618458
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.