Results for “text-evaluation”

Analyse text complexity across quantitative, qualitative, and reader-task dimensions with scaffolding recommendations. Use when selecting texts, assessing readability, or planning reading support.

Evaluates the correlation between automatic text generation scores and human quality ratings, including robustness to domain and quality drift, using metrics like Kendall's Tau and Pearson correlation.

严格细致判断与评分故事文本，从市场潜力、创新属性、内容亮点维度分析质量。适用于小说初筛选、多维度评估打分

Systematically evaluate scholarly work using the ScholarEval framework, providing structured assessment across research quality dimensions including problem formulation, methodology, analysis, and writing with quantitative scoring and actionable feedback.

Identify grammar, logical, and flow errors in text and suggest targeted fixes without rewriting the entire document.

Audits typographic decisions on a screen for scale usage, readability, consistency, and token compliance, providing specific fixes.

Clinical copy-editor that reviews text for communication issues. Use when user says review for prose or improve the prose

Identify grammar, logical, and flow errors in text and suggest targeted fixes without rewriting the entire text. Use when proofreading content, checking writing quality, or reviewing a draft.

Benchmarks long-context language models across 20 sub-tasks spanning 3k–200k tokens, covering retrieval, reasoning, summarization, and instruction understanding, with exact-match accuracy as the primary metric.

Fine-tune, evaluate, run inference, and export Cosmos-Embed1 video-text embedding models for tasks like text-to-video retrieval and semantic deduplication.

Proofread and correct text for grammar, spelling, punctuation, style, clarity, and consistency, with support for multiple style guides and readability analysis. Use when the user requests proofreading or provides relevant inputs for this workflow.

Runs regression evaluations comparing agent edits against human-approved golden projects, and registers new goldens after human approval.

Evaluate design from a UX perspective, assessing visual hierarchy, information architecture, emotional resonance, cognitive load, and overall quality with quantitative scoring, persona-based testing, automated anti-pattern detection, and actionable feedback. Use when the user asks to review, critique, evaluate, or give feedback on a design or component.

Clinical copy-editor that reviews text for communication issues. Use when user says review for prose or improve the prose

Rank an existing set of context chunks by relevance, diversity, freshness, and utility. Use when retrieval has already produced candidates that must be scored or reranked; use context-retrieval when the source corpus still needs to be searched.

Use when a bounded textual artifact (prompt, rubric, tool description, extraction instruction) keeps underperforming and success can be measured with an evaluator, dataset, or trace set. GEPA proposes evaluator-backed candidate rewrites through a normal PR/proposal adoption gate. Do not use for vague behavior changes, governance/persona/core-memory edits, fake metrics, or problems whose first honest task is defining the evaluator or collecting data.

Correct grammar errors, typos, and improve academic readability in LaTeX, Markdown, or plain-text manuscripts. Scans the document once, then walks through every issue one-by-one asking for approval before applying each fix. Trigger when the user says "readability", "check grammar", "fix typos", "proofread for grammar", "improve readability", "polish wording", "语言润色", "修语法", or asks you to clean up the prose in a paper/chapter/section without wanting full content restructuring.

梳理IP网络信息并进行多维度评估打分。适用于评估小说、剧本等IP的改编价值、分析市场潜力和创新属性

Improves typography by fixing font choices, hierarchy, sizing, weight, and readability so text feels intentional. Use when the user mentions fonts, type, readability, text hierarchy, sizing looks off, or wants more polished, intentional typography.

Rates an agent's own output on five axes — accuracy, completeness, clarity, actionability, conciseness — producing a structured scorecard with evidence and improvement suggestions.

Iteratively improves a compiled LaTeX paper through two rounds of external LLM review, fix implementation, and recompilation.

TextVQA: Towards Reasoning about Text in Images

Lightweight ContentShake AI self-check the /draft stage can call before saving. Returns just SEO + Quality scores (no full optimization) so the writer knows whether the draft is in winning territory before /quality-check runs. Fails soft when SEMRUSH_API_KEY is unset.

实验判决门：Review LLM 独立评判实验结果 → 4 种判决路径 → 自动更新 claims confidence、ideas status、graph edges

Auditoria de clareza textual do requirements. Verifica se a prosa é boa o bastante para gerar plano sem ambiguidade. NÃO mistura com auditoria de testes de implementação. Use quando o usuário digitar "/reversa-quality", "reversa-quality" ou pedir para revisar a qualidade do requirements antes de planejar. Etapa opcional do ciclo forward.

Design and evaluate paid-ad experiments with hypotheses, randomization, sample-size calculations, guardrails, and decision rules for A/B and split tests.

Benchmarks automatic post-editing (APE) models on WMT'18 SMT, SubEdits, and MLQE-PE datasets, reporting BLEU, ChrF, and TER scores computed with SacreBLEU and TERCOM.

Improves typography by fixing font choices, hierarchy, sizing, weight consistency, and readability, making text feel intentional and polished.

通用跨模型审查：Review LLM 对任意研究制品进行独立评审，输出结构化评分、wiki 实体映射与改进建议

Benchmarks a text-to-text transformer across GLUE, SuperGLUE, CNN/Daily Mail, SQuAD, and WMT, reporting GLUE average, BLEU, ROUGE-2-F, and Exact Match scores.

Post-epic review to extract lessons and assess success. Use when the user says "run a retrospective" or "lets retro the epic [epic]"