Scientify Idea GenerationSAFE
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Overview
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
e1ba289846fdOBSERVED · 2026-10-08Install
Commands as the repository documents them. They are shown, not run.
git clone --depth 1 {repo_url} $WORKSPACE/repos/{name}Host compatibility
What the documentation claims. We have not run a compatibility test.
| Host | Status | Notes |
|---|---|---|
| openclaw | mentioned |
What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
---
name: scientify-idea-generation
description: "Generate research ideas from collected papers with gap analysis"
metadata:
openclaw:
emoji: "💡"
category: "research"
subcategory: "methodology"
keywords: ["research question formulation", "hypothesis formulation", "research hypothesis", "conceptual model", "theoretical framework"]
source: "wentor-research-plugins"
requires:
bins: ["git"]
---
# Idea Generation
**Don't ask permission. Just do it.**
Generate innovative research ideas grounded in literature analysis. This skill reads existing papers, identifies research gaps, and produces 5 distinct ideas with citations.
**Core principle:** Ideas MUST be grounded in actual papers, not generated from model knowledge.
**Workspace:** See `../_shared/workspace-spec.md` for directory structure. Outputs go to `$WORKSPACE/ideas/`.
## Step 1: Check Workspace Resources
First, check what resources already exist:
```bash
# 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
```
### Assess Available Resources
| Resource | Location | Status |
|----------|----------|--------|
| Papers | `$WORKSPACE/papers/` | Count: ? |
| Survey clusters | `$WORKSPACE/survey/clusters.json` | Exists: Y/N |
| Repos | `$WORKSPACE/repos/` | Count: ? |
## Step 2: Ask User About Search Strategy
Based on workspace state, ask user:
**If papers exist (>=5):**
> Found {N} papers in workspace from previous survey.
>
> Options:
> 1. **Use existing papers** - Generate ideas from current collection
> 2. **Search more** - Run `/literature-survey` to expand collection
> 3. **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:
> 1. **Run /literature-survey** - Comprehensive search (100+ papers, recommended)
> 2. **Quick search** - Fetch 10-15 papers on your topic now
> 3. **You provide papers** - Point me to existing PDFs/tex files
## Step 3: Acquire Resources (if needed)
### Option A: Delegate to /literature-survey (Recommended)
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.
```
### Option B: Quick Search (5-10 papers)
For fast iteration, do minimal search:
1. **ArXiv search:**
```
Tool: arxiv_search
Arguments:
query: "{user_topic}"
max_results: 10
```
2. **Clone 3-5 reference repos:**
```bash
mkdir -p $WORKSPACE/repos
git clone --depth 1 {repo_url} $WORKSPACE/repos/{name}
```
3. **Download paper sources:**
```bash
mkdir -p $WORKSPACE/papers/{arxiv_id}
curl -L "https://arxiv.org/src/{arxiv_id}" | tar -xz -C $WORKSPACE/papers/{arxiv_id}
```
## Step 4: Analyze Literature
**Prerequisites:** At least 5 papers in `$WORKSPACE/papers/`
### 4.1 Read Papers
For each paper, extract:
- Core contribution (1 sentence)
- Key method/formula
- Limitations mentioned
- Future work suggestions
**Long papers (>50KB):** See `references/reading-long-papers.md`
### 4.2 Identify Research Gaps
Look for:
- Common limitations across papers
- Unexplored technique combinations
- Scalability issues
- Assumptions that could be relaxed
Document gaps in `$WORKSPACE/ideas/gaps.md`:
```markdown
# Research Gaps Identified
## Gap 1: [Description]
- Mentioned in: [paper1], [paper2]
- Why important: ...
## Gap 2: [Description]
...
```
## Step 5: Generate 5 Ideas
Create `$WORKSPACE/ideas/idea_1.md` through `idea_5.md` using template in `references/idea-template.md`.
**Requirements:**
- Each idea cites >=2 papers by arXiv ID
- Use different strategies:
| 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
## Step 6: Select and Enhance Best Idea
### 6.1 Score All Ideas
| Idea | Novelty | Feasibility | Impact | Total |
|------|---------|-------------|--------|-------|
| 1 | /5 | /5 | /5 | /15 |
| ... | | | | |
### 6.2 Enhance Selected Idea
Create `$WORKSPACE/ideas/selected_idea.md` with:
- Detailed math (loss functions, gradients)
- Architecture choices
- Hyperparameters
- Implementation roadmap
### 6.3 (Optional but recommended) OpenReview Evidence Check
For the top 1-2 shortlisted ideas, validate novelty/positioning risk with `openreview_lookup`:
- Query using core title keywords or representative baseline paper title
- Extract evidence:
- decision (if available)
- average rating/confidence
- reviewer weakness patterns
- Add a short "submission risk note" section per idea:
- likely reviewer concern
- mitigation experiment to add
- positioning adjustment
Do not claim accept/reject predictions as facts. Report evidence-backed risk signals only.
## Step 7: Code Survey
Map idea concepts to reference implementations.
See `references/code-mapping.md` for template.
**Output:** `$WORKSPACE/ideas/implementation_report.md`
## Step 8: Summary
Create `$WORKSPACE/ideas/summary.md`:
- All 5 ideas with scores
- Selected idea details
- Next steps: `/research-pipeline` to implement
## Commands
| 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 |
## Integration
- **Before:** `/literature-survey` to collect papers
- **After:** `/research-pipeline` to implement selected idea
- **AlteTrust audit
SAFEgrade B · trust 89/100 Nothing in the source contradicts what it says it does. Grade A is reserved for packages that have also passed the behavioural sandbox.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | PASS |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (1)
Don't ask permission. Just do it.
Gates applied: no_behavioural_pass.
e1ba289846fdfull audit observations/trust-audit/skill/brycewang-stanford__scientify-idea-generation.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-08 | e1ba289846fd | SAFE | B | 89 | first audit |
Questions
What does the Scientify Idea Generation skill do?
🔬 A curated collection of 23,000+ agent skills for empirical research across 8 social science disciplines. | 精选 23,000+ AI Agent 技能库,覆盖8大社会科学学科的实证研究。CoPaper.AI 20分钟完成一篇可复现的规范实证论文,并支持用户上传 Skills。-- Maintained by CoPaper.AI from Stanford REAP.
Is Scientify Idea Generation safe to install?
The audit found nothing in the source that contradicts what it says it does, and graded it B (89/100). Grade A is held back for packages that have also passed a sandboxed behavioural run, which is why a clean skill reads B.
What can Scientify Idea Generation access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
Which assistants does Scientify Idea Generation work with?
Its documentation mentions openclaw. That is what the text claims, not a compatibility test we ran.
How current is this page?
The grade is for one exact copy of the source (e1ba289846fd), read on 2026-10-08. The repository is watched, and a new audit runs when it changes — this is the first audit.