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affaan-m/scholar-evaluation

針對論文、提案、文獻回顧、方法章節、證據品質、引用支持以及研究寫作回饋的結構化學術工作評估。

scholar-evaluation 是什麼?

此技能提供結構化的學術工作評估,適用於研究論文、提案、文獻回顧、方法部分、證據品質、引用支持以及研究寫作回饋。它旨在幫助研究人員、審稿人和學術寫作者通過系統性的標準來評估和改進學術文本。此技能支援多種平台,如 Claude Code、Cursor 和 Codex,並透過 SKILL.md 文件定義的評估框架來運作,包括檢查邏輯連貫性、引用準確性、證據強度和寫作清晰度。使用此技能可以確保學術工作達到嚴謹的標準,節省審閱時間,並提供具體改進建議。它透過結構化的提示和規則來引導 AI 輸出高質量的學術回饋,從而使評估過程一致且可靠。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/scholar-evaluation

Installed? Explore more 研究與資料分析 skills: affaan-m/uspto-database, affaan-m/literature-review, affaan-m/everything-claude-code · View all 6 →

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說明文件

scholar-evaluation 是做什麼的?

針對論文、提案、文獻回顧、方法章節、證據品質、引用支持以及研究寫作回饋的結構化學術工作評估。

概述

本技能提供系統化的方法來評估學術作品,包括研究論文、提案、文獻回顧、方法部分、證據品質、引用支持以及寫作回饋。它設計用於學術環境,幫助作者、審稿人和編輯確保學術工作的嚴謹性和清晰度。

功能

  • 論文評估:對研究論文的邏輯結構、原創性和貢獻進行評估。
  • 提案審查:檢查研究提案的目標、方法論和可行性。
  • 文獻回顧分析:評估文獻回顧的全面性、相關性和綜合性。
  • 方法部分評估:檢查方法論的清晰度、適當性和再現性。
  • 證據品質檢查:評估提供的證據的強度、可靠性和相關性。
  • 引用支持分析:確保引用準確、相關且適當地整合。
  • 寫作回饋:針對學術寫作的清晰度、風格和連貫性提供回饋。

如何使用

使用此技能時,提供需要評估的學術文本(例如論文段落、提案摘要、文獻回顧部分)。技能將根據預定義的標準分析文本並生成結構化回饋。

範例提示

請評估以下研究論文摘要的邏輯結構和清晰度:

[在此處粘貼文本]

支援平台

  • Claude Code
  • Cursor
  • Codex

配置

無需特殊配置。只需在 AI 代理程式中啟用此技能即可使用。

注意事項

  • 確保輸入文本為純文字格式。
  • 對於長文本,可能需要分段評估以獲得最佳結果。
  • 回饋應視為建議,最終判斷由人工完成。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

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