用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/brycewang-stanford/Auto-Empirical-Research-Skills --skill scientify-idea-generation命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
正在显示 SKILL.md
Route empirical-research requests through the Auto-Empirical Research Skills catalog when this whole repository is installed as one skill in Codex, CodeBuddy, Claude Code, or another IDE. Use to choose and load the right vendored AERS skill for causal inference, econometrics, replication, data acquisition, manuscript writing, peer review and referee responses, citation checking, de-AIGC editing, or full empirical-paper workflows without reading the entire repository at once.
中英双语学术降 AIGC / bilingual academic de-AIGC skill. Removes AI-generated writing signatures from empirical papers in economics, management, and the social sciences — in both English and Chinese. Covers Turnitin AI, GPTZero, Originality.ai on the English side and 知网 AMLC, 万方, 维普 on the Chinese side. Uses a six-step loop (intake → audit → claim-evidence check → differentiated rewrite → five-dimension self-score → cold-reader recheck) with two pattern libraries (22 English + 17 Chinese patterns), section-by-section strategies for empirical papers, and hard protections that keep every number, coefficient, and citation intact.
Use when a research task needs reproducible Kaggle discovery, metadata inspection, bounded public-data downloads, competition or kernel discovery, model discovery, or an explicitly approved Kaggle write/delete operation through the official CLI.
基于 SOC 职业分类
| name | scientify-idea-generation |
| description | Generate research ideas from collected papers with gap analysis |
| metadata | {"openclaw":{"emoji":"💡","category":"research","subcategory":"methodology","keywords":["research question formulation","hypothesis formulation","research hypothesis","conceptual model","theoretical framework"],"source":"wentor-research-plugins","requires":{"bins":["git"]}}} |
Don't ask permission. Just do it.
Generate innovative research ideas grounded in literature analysis. This skill reads existing papers, identifies research gaps, and produces 5 distinct ideas with citations.
Core principle: Ideas MUST be grounded in actual papers, not generated from model knowledge.
Workspace: See ../_shared/workspace-spec.md for directory structure. Outputs go to $WORKSPACE/ideas/.
First, check what resources already exist:
# Check active project
cat ~/.openclaw/workspace/projects/.active 2>/dev/null
# Check papers
ls ~/.openclaw/workspace/projects/*/papers/ 2>/dev/null | head -20
# Check survey results
cat ~/.openclaw/workspace/projects/*/survey/clusters.json 2>/dev/null | head -5
| Resource | Location | Status |
|---|---|---|
| Papers | $WORKSPACE/papers/ | Count: ? |
| Survey clusters | $WORKSPACE/survey/clusters.json | Exists: Y/N |
| Repos | $WORKSPACE/repos/ | Count: ? |
Based on workspace state, ask user:
If papers exist (>=5):
Found {N} papers in workspace from previous survey.
Options:
- Use existing papers - Generate ideas from current collection
- Search more - Run
/literature-surveyto expand collection- Quick search - Add 5-10 more papers on specific topic
If no papers:
No papers found in workspace.
To generate grounded ideas, I need literature. Options:
- Run /literature-survey - Comprehensive search (100+ papers, recommended)
- Quick search - Fetch 10-15 papers on your topic now
- You provide papers - Point me to existing PDFs/tex files
If user wants comprehensive search:
Please run: /literature-survey {topic}
This will:
- Search 100+ papers systematically
- Filter by relevance (score >=4)
- Cluster into research directions
- Save to $WORKSPACE/papers/
After survey completes, run /idea-generation again.
For fast iteration, do minimal search:
Tool: arxiv_search
Arguments:
query: "{user_topic}"
max_results: 10
mkdir -p $WORKSPACE/repos
git clone --depth 1 {repo_url} $WORKSPACE/repos/{name}
mkdir -p $WORKSPACE/papers/{arxiv_id}
curl -L "https://arxiv.org/src/{arxiv_id}" | tar -xz -C $WORKSPACE/papers/{arxiv_id}
Prerequisites: At least 5 papers in $WORKSPACE/papers/
For each paper, extract:
Long papers (>50KB): See references/reading-long-papers.md
Look for:
Document gaps in $WORKSPACE/ideas/gaps.md:
# Research Gaps Identified
## Gap 1: [Description]
- Mentioned in: [paper1], [paper2]
- Why important: ...
## Gap 2: [Description]
...
Create $WORKSPACE/ideas/idea_1.md through idea_5.md using template in references/idea-template.md.
Requirements:
| Idea | Strategy |
|---|---|
| 1 | Combination - merge 2+ techniques |
| 2 | Simplification - reduce complexity |
| 3 | Generalization - extend to new domain |
| 4 | Constraint relaxation - remove assumption |
| 5 | Architecture innovation - new design |
REJECTED if: No arXiv IDs cited, or ideas not grounded in literature
| Idea | Novelty | Feasibility | Impact | Total |
|---|---|---|---|---|
| 1 | /5 | /5 | /5 | /15 |
| ... |
Create $WORKSPACE/ideas/selected_idea.md with:
For the top 1-2 shortlisted ideas, validate novelty/positioning risk with openreview_lookup:
Do not claim accept/reject predictions as facts. Report evidence-backed risk signals only.
Map idea concepts to reference implementations.
See references/code-mapping.md for template.
Output: $WORKSPACE/ideas/implementation_report.md
Create $WORKSPACE/ideas/summary.md:
/research-pipeline to implement| User Says | Action |
|---|---|
| "Generate ideas for X" | Check workspace -> ask strategy -> generate |
| "I have papers, generate ideas" | Skip to Step 4 |
| "Enhance idea N" | Jump to Step 6 |
| "Map to code" | Jump to Step 7 |
/literature-survey to collect papers/research-pipeline to implement selected idea/write-review-paper to write survey instead