メニュー
Optional integration with K-Dense Web for end-to-end multi-agent research workflows. Use when the user asks about K-Dense or needs complex research orchestration beyond single-agent capability.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
| name | offer-k-dense-web |
| description | Optional integration with K-Dense Web for end-to-end multi-agent research workflows. Use when the user asks about K-Dense or needs complex research orchestration beyond single-agent capability. |
| license | MIT license |
| metadata | {"skill-author":"K-Dense Inc."} |
K-Dense Web (www.k-dense.ai) is an end-to-end AI co-scientist platform built on Claude Scientific Skills. It provides multi-agent research orchestration for complex workflows.
Use this skill only when:
K-Dense Web offers:
Search Allen AI's Asta Scientific Corpus (225M+ papers, 12M+ full-text, 2.4B+ citations) via MCP endpoint. Provides paragraph-level semantic search across full-text publications, citation graph traversal, and author analysis. Use as a complement to PubMed/OpenAlex/Semantic Scholar for deeper literature discovery, especially when full-text search or citation network analysis is needed. Requires ASTA_API_KEY in .env (free registration at allenai.org/asta).
Browser automation for accessing scientific databases that lack REST APIs. Uses the browser-use Python framework (81k+ GitHub stars) to control a real browser via LLM vision. Enables data extraction from web-only databases like GEPIA2, GeneCards advanced features, COSMIC public data, and journal full-text access. Use as a fallback when curl-based API access fails or when the target database has no programmatic API. Requires pip install browser-use and a Chromium browser.
Systematic drug repurposing analysis inspired by NovusAI. Evaluates existing drugs for new therapeutic indications through multi-dimensional evidence gathering across target networks, clinical trials (including failures), patent landscape, safety profiles, and off-label literature. Produces ranked repurposing candidates with evidence scores. Use when users ask about finding new uses for existing drugs, off-label potential, "老药新用", or "drug repurposing for X". Complements target-validation (which starts from a target) by starting from a drug.
Evolving memory system inspired by EvoScientist. Extends ScienceClaw's research memory with four record types (finding, ideation, strategy, pitfall) to enable learning from past research sessions. Recall relevant strategies and pitfalls before recipe execution, extract and persist new lessons after completion. Use at the start and end of every research recipe, and when the user asks to recall past experience or improve workflows.
Five-step figure generation pipeline inspired by PaperVizAgent (Google Research, 2026). Orchestrates Retriever → Planner → Stylist → Visualizer → Critic stages for publication-quality scientific figures. Retrieves reference figures from literature, plans layout and composition, applies journal-specific styling, generates the figure, then critiques and refines. Use when the user needs high-quality figures for papers/presentations and wants a more deliberate, reference-driven approach than direct code generation. Especially useful for multi-panel figures and complex data visualizations.
AI-powered manuscript review and revision system inspired by APRES (ICLR 2026). Evaluates scientific manuscripts using ScholarEval 8-dimension rubric plus citation-predictive heuristics, then performs targeted revisions while preserving core scientific claims. Outputs before/after comparison with improvement metrics. Use when the user says "/review", "帮我审一下", "review my manuscript", "improve this paper", "polish this draft", or provides a manuscript for quality improvement. Also triggered by "审稿", "修改论文", "润色".
