Derived from arXiv:2607.16989 - Real-World Evaluation of an AI Agent Drafting Translational Impact Summaries
Scanned 9/11/2026
Install to Claude Code
Skills Directorynpx -y skills add hiyenwong/ai_collection --skill real-world-evaluation-of-an-ai-agent-drafting-tran --agent claude-codeInstalls into .claude/skills of the current project.
Are you the author of Real World Evaluation Of An Ai Agent Drafting Tran?
Add the live security badge to your README — it updates automatically with every re-scan.
[](https://www.skillsdirectory.com/skills/hiyenwong-real-world-evaluation-of-an-ai-agent-drafting-tran)More formats (shields.io, HTML) on the badges page.
# Real-World Evaluation of an AI Agent Drafting Translational Impact Summaries
Derived from arXiv:2607.16989 - Real-World Evaluation of an AI Agent Drafting Translational Impact Summaries
## Core Concept
Introduction. Clinical and Translational Science Award (CTSA) programs must document their scholars' research impact, but assembling each scholar's record by hand takes staff an estimated 15 hours and does not scale to a full cohort. An artificial intelligence (AI) agent could serve as a tool to gather scholar data across platforms and disciplines. Methods. We built a human-in-the-loop AI agent that assembles a dossier of sourced evidence for each scholar and drafts one-sentence Translational ...
## Key Insights
- Derived from arXiv:2607.16989
- Published: 2026-07-18
- Utility Score: 1.00
- Authors: Mohammad Arvan, Amber E. Osterholt, Bailee Rue et al.
## Activation
real-world-evaluation-of-an-ai-agent-drafting-tran, 2607.16989
## References
- arXiv: https://arxiv.org/abs/2607.16989
Is this your skill, or is something wrong with this listing? Request removal or report an issue. Author removals are honored within 72 hours.
No comments yet. Be the first to comment!
This skill provides comprehensive analysis of competitor SEO and GEO strategies, revealing what's working in your market and identifying opportunities to outperform the competition.
Universal deep research agent team. 13-agent pipeline for rigorous academic research on any topic. 7 modes: full research, quick brief, paper review, lit-review, fact-check, Socratic guided research dialogue, and systematic review with optional meta-analysis. Covers research question formulation, Socratic mentoring, methodology design, systematic literature search, source verification, cross-source synthesis, risk of bias assessment, meta-analysis, APA 7.0 report compilation, editorial review...
Use when an operation issue is a Paperclip cursor-window, distill, or backfill — `operationType: "distill"` or `"backfill"` and the body references a Paperclip source bundle for a project or root issue. Turn raw Paperclip activity into a wiki-insightful project page, decisions log, and history note. This skill exists specifically to replace the stiff, datestamp-heavy templated output that the deterministic distiller produces.
Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publi...
Semantic search, similar content discovery, and structured research using Exa API
