Results for “l-eval”

学术论文正文写作、结构设计与语言润色总入口。完整起草、续写、扩写、补写或实质性修订各学科的中英文论文正文时，进入paper-write-zh或paper-write-en；只做提纲、结构诊断或重排方案时，进入paper-shape结构模式；只做忠实润色或不新增研究内容的中译英时，进入paper-shape润色模式。研究评价转/doubao-academic-evaluator；独立系统性文献调研转/doubao-literature-research。

Provides technical leadership frameworks for architecture decisions, engineering team scaling, technology strategy, and technical debt assessment.

Design click and first-click tests to evaluate navigation clarity and information findability in user interfaces.

Audits medical LLM benchmarks across five lifecycle phases using 46 medically tailored criteria to assess clinical relevance, data integrity, safety-critical capabilities, validity, and governance.

Benchmarks medical AI agents on synthetic EHR tasks, measuring success rates for data retrieval, temporal aggregation, and threshold-based conditional logic with exact-match scoring.

Identify and analyze 3-5 distinct customer segments with demographics, jobs-to-be-done, pain points, and product fit analysis for market opportunity evaluation.

Benchmarks large audio-language models against adversarial audio attacks using the CAA dataset, computing WER, ROUGE-L, cosine similarity, and coherence scores to assess robustness in conversational settings.

Analyze and optimize content for visibility in AI Overviews, ChatGPT, Perplexity, and other AI search systems by evaluating citability, structure, authority, and technical accessibility.

Route research prompts to evidence, retrieval, scraping, data, market, or scientific research skills. Use when prompts mention deep research, current facts, citations, Exa, iterative retrieval, search-first, scraping, data pipelines, PubMed, USPTO, gget, literature review, or scholar evaluation.

Evaluates Base ERC-20 tokens by contract address, returning a 0-100 score across five criteria, launch protocol classification, security flags, and a narrative interpretation.

Conducts end-to-end user research, from study design through behavioral insight synthesis, using frameworks like Nielsen's Heuristics, SUS, and User Journey Mapping.

Evaluates the trade-off between file size reduction and image fidelity when encoding radio astronomy data using JPEG2000, benchmarking both lossless and lossy compression modes to determine the compression ratio at which visual artifacts first appear.

Evaluates US market bubble risk through quantitative data analysis using the Minsky/Kindleberger framework, scoring indicators like Put/Call ratio, VIX, margin debt, breadth, and IPO data.

Guides the agent to research existing tools, libraries, and patterns before writing custom code, using parallel searches across package registries, MCP servers, skills, and GitHub, then evaluating and deciding to adopt, extend, or build.

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.

Use this skill when the user asks how to build, measure, or improve safety culture in a motor carrier — leading vs lagging safety indicators, blame-free reporting, near-miss programs, safety committee structure, monthly safety meetings, ATA Safety Management Program guidance, and how to evaluate cultural maturity. Reference ATA SMP + DOT safety culture research.

Systematic strategic planning framework for philosophy and interdisciplinary academic papers targeting preprint platforms (PhilArchive, arXiv, PhilSci-Archive). Use when users want to: (1) plan a paper on a specific topic, (2) identify research gaps and assess originality, (3) develop optimized paper outlines, (4) prepare for preprint submission, or (5) understand platform requirements and writing standards. Triggered by phrases like 'plan a paper on,' 'help me design a paper about,' 'identify research gaps in,' 'is this idea original,' or when users need structured research planning. The skill guides through three phases: Platform Analysis (identifying target venue and studying sample papers), Theoretical Framework (AI-driven literature search and gap identification), and Outline Optimization (structured design with reviewer-perspective self-assessment). Each phase includes quality evaluation standards and validation checkpoints. Output: optimized detailed outline ready for systematic writing (use with acade