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Agent Skill · 研究检索

research-ideation(研究构思)

research-ideation 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

research-ideation 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为需要联网;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 20,947。

总安装 5,033周安装 214GitHub Stars 346下载 1,763核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:research-ideation(研究构思)
来源仓库:https://github.com/evoscientist/evoskills
仓库路径:skills/research-ideation
安装命令:
npx skills add https://github.com/evoscientist/evoskills --skill research-ideation
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/evoscientist/evoskills --skill research-ideation

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

research-ideation 从研究目标出发生成候选方向,经 ELO 锦标赛选出前三方案并展开提案。适用于探索新研究方向、评估创意新颖性或制定详细研究计划的场景。结合文献 grounding 与多 persona 视角生成多样化思路,支持多轮迭代精炼。

使用时需确保已加载 evo-memory 知识库,并遵循标准化流程逐步推进。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/research-ideation

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/evoscientist/evoskills --skill research-ideati…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要需要联网,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

research-ideation 怎么安装?

在终端执行 npx skills add https://github.com/evoscientist/evoskills --skill research-ideation。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

research-ideation 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

research-ideation 需要什么权限?

权限级别是需要联网。该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

research-ideation 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

research-ideation 用的人多吗?

公开数据是累计安装 5,033 次,近一周安装 214 次,下载 1,763 次,GitHub 346 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

research-ideation 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
research-ideation

研究构思 · 本页

5,033346需要联网未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Research Ideation

From research goal to ranked ideas and a detailed proposal.

Step 0: Load evo-memory (M_I)
    ↓
Step 1: Define Goal
    ↓
Step 2: Literature Grounding (MUST use paper-navigator scripts)
    ↓
Step 3: Generate Ideas (3 directions × 3 personas)
    ↓
Step 4: Refine Ideas (3 tracks × N iterations)
    ↓
Step 5: ELO Tournament → Present Top-3 to User
    ↓
Step 6: Update evo-memory (IDE)
    ↓
User Selects
    ↓
Step 7: Expand into Proposal
    ↓
Step 8: Validate and Iterate

When to Use

  • User wants to find a research direction or brainstorm research ideas
  • User wants to evaluate whether an idea is novel or worth pursuing
  • User wants to rank or compare multiple research ideas
  • User wants to generate a research proposal from an idea

When NOT to Use

  • Finding/reading papers → use paper-navigator
  • Literature survey report → use research-survey
  • Planning a paper (story design, experiment plan) → use paper-planning

Step 0: Load Prior Knowledge from evo-memory

Before any ideation begins, load Ideation Memory (M_I) from prior research cycles:

  1. Read M_I at /memory/ideation-memory.md (refer to evo-memory skill)
  2. Select the top-2 entries (k_I=2) most relevant to the user's current goal by comparing each entry's Summary and Retrieval Tags against the goal
  3. Feasible directions from prior cycles → use as seeds in Step 3 (incorporate as candidate research directions alongside new ones)
  4. Unsuccessful directions marked as fundamental failures → use during idea pruning in Step 4 (prune any idea that matches a fundamental failure pattern)
  5. If M_I doesn't exist yet (first cycle), skip this step

This step prevents repeating known dead ends and builds on prior successes across research cycles.

Step 1: Define a Long-Term Research Goal

Start with a goal that has both scientific and practical value. Ambitious enough for multiple papers, concrete enough to guide daily decisions.

Ask: "What is the ultimate form of this research direction? What would the world look like if this problem were fully solved?"

Step 2: Literature Grounding (via paper-navigator)

Invoke paper-navigator (Workflow 9: Ideation Support) to collect 30-50 relevant papers. Do NOT skip this step or substitute with general knowledge — ideas must be grounded in real papers.

CRITICAL: All paper discovery in this step MUST use the paper-navigator skill and its scripts (scholar_search, citation_traverse, arxiv_monitor, recommend, etc.). Using WebSearch, WebFetch, or any generic web search tool for finding papers is PROHIBITED. Generic web search returns blog posts, news articles, and low-quality results — only paper-navigator provides access to Semantic Scholar, arXiv, citation graph traversal, and academic recommendations needed for rigorous literature grounding.

Build Challenge-Insight Tree

From the collected papers, construct a challenge-insight tree — a many-to-many mapping between technical challenges and the insights/techniques that address them:

  • Extract challenges: From each paper, what technical problem does it solve?
  • Extract insights: What technique or key idea does it use?
  • Map connections: Which insights address which challenges?

How this drives ideation:

  • Challenges with few insights → unsolved problem (candidate for Step 3)
  • Insights not yet applied to a challenge → cross-domain transfer opportunity (candidate for Step 4)
  • Challenges with many insights → well-studied, avoid unless you have a fundamentally new angle

Also generate a condensed literature review synthesis as context for idea generation (for full surveys use research-survey).

See references/literature-tree.md for construction methodology.

Execution rule: Do NOT generate ideas without real paper grounding. The tree must reference actual papers with titles, authors, and findings. Paper search MUST go through paper-navigator — never use WebSearch/WebFetch as a shortcut.

Step 3: Generate Ideas

Generate 3 initial research ideas from 3 distinct research directions, grounded in the literature.

Three Personas

PersonaFocus
InnovatorNovelty & creativity — groundbreaking, high-risk/high-reward
PragmatistFeasibility — realistic, clearly executable
CriticScientific value — advances understanding, rigorous

Process

  1. Analyze literature + challenge-insight tree → identify 3 fundamentally different research directions
  2. Generate one idea per direction using Innovator persona
  3. Each idea must follow one of two methodological paths:

- Path 1 (Focused Contribution): Single new component; clean hypothesis - Path 2 (System Contribution): Tight causal interaction between components; emergent capability

Idea Format

# Research Idea: [Concise Title]

## Core Idea
[One paragraph: the proposal + which research direction it addresses]

## Validation Plan
[Concrete experiment outline: datasets, baselines, metrics]

Step 4: Refine Ideas

Run 3 parallel refinement tracks — one per initial idea. Each track uses all 3 personas.

For each track:
  For N=3 iterations:
    1. Evaluate current best idea (novelty, feasibility, impact, alignment)
    2. All 3 personas generate refined versions based on evaluation
    3. Pick the best refinement as seed for next iteration
  Track champion = best idea across iterations

5 Evolution Strategies

  1. Enhancement through Grounding: Strengthen with literature citations
  2. Improving Coherence: Fix logical flaws in the mechanism
  3. Inspiration and Combination: Combine with a different concept from literature
  4. Simplification: Strip down to a clean, testable hypothesis
  5. Literature-Driven Pivot: Abandon the mechanism; propose a new approach from literature

Critical rule: If evaluation says the approach is a dead-end, the persona MUST pivot — refinement is not restricted to patching.

Logical Cohesion Principles

  • Too many variables → Focus via Subtraction: isolate the most promising variable
  • Disconnected components → Justify via Strong Correlation: build explicit causal links

Step 5: ELO Tournament → Present Top-3

Rank all track champions through pairwise comparison, then present the top-3 to the user for selection.

Four Dimensions

DimensionWhat It Measures
NoveltyHow different from existing published work?
FeasibilityCan this be implemented within reasonable resources?
RelevanceDoes this address an important problem aligned with the goal?
ClarityIs the idea well-defined enough to start immediately?

Tournament

  • Starting Elo: 1500 | K-factor: 32
  • Compare ideas pairwise → update Elo → sort by final score
  • See references/elo-ranking-guide.md for rubric and formula

Present Top-3 to User

After the tournament, present the top-3 ideas with both a comparison table and the full refined idea for each. This ensures the user sees the concrete, actionable version of each idea — not just a summary.

Part 1: Comparison Table
## Top-3 Research Ideas (ranked by ELO)

| Rank | Title | Core Mechanism | Novelty | Feasibility | Relevance | Clarity | ELO |
|------|-------|---------------|---------|-------------|-----------|---------|-----|
| 1 | ... | ... | 9 | 7 | 8 | 8 | 1280 |
| 2 | ... | ... | 7 | 9 | 8 | 7 | 1240 |
| 3 | ... | ... | 8 | 6 | 9 | 7 | 1210 |
Part 2: Full Refined Ideas

For each of the top-3, present the refined idea using the same structured format as Step 3, plus a refinement summary:

# Refined Idea [Rank]: [Concise Title]

## Core Idea
[One paragraph: the refined proposal — this should reflect ALL changes from Step 4 refinement,
not the original Step 3 version]

## Validation Plan
[Concrete experiment outline updated with refinement insights: datasets, baselines, metrics,
key ablations identified during refinement]

## Refinement Summary
[Brief paragraph summarizing what changed from the initial idea and why:
- What was simplified or removed (and why)
- What was added or concretized (and why)
- Which persona drove the most impactful change
- Key risk mitigations added during refinement]

This section is mandatory — do NOT skip the full refined ideas or collapse them into the comparison table. The user needs to see the complete, refined version to make an informed selection.

Part 3: Selection Prompt
Which idea would you like to develop into a full proposal? (1/2/3, or combine elements)

After presenting top-3, trigger Step 6 (evo-memory IDE) before finalizing user selection. The user may:

  • Pick one of the top-3
  • Ask to combine elements from multiple ideas
  • Request modifications before expanding
  • Ask to regenerate with different constraints

Step 6: Update evo-memory

After the tournament and before the user selects, trigger evo-memory IDE (Idea Direction Evolution):

  1. Save the top-3 directions to /direction-summary.md
  2. Trigger IDE protocol via evo-memory skill with the direction summary
  3. Each top direction is added to M_I as a feasible direction with its ELO score
  4. Any ideas that were clearly unworkable during refinement (Step 4) are recorded as unsuccessful directions with failure classification (fundamental vs implementation)

This ensures future ideation cycles benefit from what was learned in this cycle.

Step 7: Expand into Proposal

After the user selects an idea, expand it into a manuscript-quality research proposal. This is a two-phase process because different fields require different proposal structures.

Phase 1: Generate a Domain-Specific Template

Before writing, first generate a proposal template tailored to the user's field:

  1. Identify the field from the research goal and literature
  2. Start with universal sections (Abstract, Problem, Related Work, Method, Evaluation, Conclusion)
  3. Add field-specific sections (e.g., Ethics/IRB for medical research, Safety analysis for chemistry, Statistical power analysis for clinical trials, Ablation design for ML)
  4. Adapt terminology to the field's conventions (e.g., "Study Design" in medicine, "Methodology" in social sciences, "Proposed Method" in engineering)

See assets/proposal-template.md for the complete field-specific section guide and writing instructions.

Phase 2: Write the Proposal

Fill the generated template following these universal principles:

  • Write for a top-tier reviewer in the field — every claim supported, every design justified
  • Avoid variable confusion: clearly isolate the core contribution
  • Match the field's rigor standards (math for quantitative fields, protocols for experimental fields, coding schemes for qualitative fields)
  • Anticipate skeptical reviewer questions proactively

See references/proposal-extension.md for detailed section guidance.

Step 8: Validate and Iterate

Run experiments on representative data. If the approach fails, return to Step 3 or Step 4 with updated knowledge. See experiment-craft for systematic debugging.


Counterintuitive Rules

  1. Problem selection > solution design: Choosing WHAT to solve matters more than HOW
  2. Pursue new failure cases, not incremental improvements: Find settings where existing methods break
  3. If a well-established solution exists, switch problems: Improvement space is too small
  4. Technology is creative combination, not concatenation: Simple A→B pipelines are not contributions
  5. Quantity before quality in generation: Generate many candidates before evaluating any
  6. Feasibility is not optional: Brilliant but infeasible ideas waste research cycles
  7. The tournament finds surprises: Trust rankings over gut feeling

Dependency: paper-navigator

All paper discovery goes through paper-navigator. This skill does not search for papers itself. Using WebSearch, WebFetch, or any generic search tool to find papers is PROHIBITED — these tools cannot access Semantic Scholar, citation graphs, or academic recommendation systems. Always use paper-navigator and its scripts (scholar_search, citation_traverse, arxiv_monitor, recommend, trending, etc.) for all paper discovery needs in Steps 2, 3, and 4.

StepRequires paper-navigator for
Step 2Collect 30-50 relevant papers for literature tree construction
Step 3Verify no well-established solution exists for selected problems
Step 4Cross-domain search for transferable techniques during refinement

evo-memory Integration

WhenActionDetails
Step 0 (before ideation)Read M_ILoad /memory/ideation-memory.md, select top-2 relevant entries, use feasible directions as seeds, avoid fundamental failures
Step 6 (after tournament)Write M_I via IDESave top-3 directions with ELO scores as feasible; save dead-end ideas as unsuccessful with failure classification

Handoff

ToWhenKey Artifacts
paper-planningProposal complete (Step 7) → plan paper structure/research-proposal.md, /direction-summary.md
experiment-pipelineProposal complete (Step 7) → start experiments/research-proposal.md, /direction-summary.md
evo-memoryAfter tournament (Step 6) → update Ideation Memory via IDE protocol/direction-summary.md

References & Assets

TopicFile
Literature tree constructionreferences/literature-tree.md
Problem selection frameworkreferences/problem-selection.md
Solution design methodologyreferences/solution-design.md
Tree expansion rulesreferences/tree-search-protocol.md
ELO formula & rubricreferences/elo-ranking-guide.md
Proposal section guidancereferences/proposal-extension.md
Idea candidate templateassets/idea-candidate-template.md
Ranking scorecardassets/ranking-scorecard-template.md
Direction summaryassets/direction-summary-template.md
Proposal example (E-FNO)assets/proposal-template.md

平台分布

Codex

35.14%
按下载量换算620

Claude

29.48%
按下载量换算520

Cursor

21.76%
按下载量换算384

Gemini CLI

9.13%
按下载量换算161

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

需要联网

该 Skill 可能需要联网访问来源站点、仓库或外部 API;具体网络访问范围需要结合源码和 README 复核。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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