research-survey — Claude Code skill: literature survey from papers
Turns a collected paper set into a structured survey: outline, draft, section-by-section expansion, comparison tables and citations.
npx skills add EvoScientist/EvoSkills --skill research-survey -a claude-codeWhat research-survey does
The skill takes a paper collection and produces a survey-grade report. Six stages: an outline adapted to the query type, a draft built from the top-30 most relevant papers, expansion of every section using the full collection (30–120 papers), section summaries, refinement of Abstract, Intro and Conclusion, then final assembly with references.
Structure follows the field. Medicine gets organization by intervention type and PRISMA, chemistry by reaction class, social sciences by theoretical perspective. Inside a section, methods are grouped by underlying mechanism rather than chronology, with comparison tables and LaTeX formalization of the problem statement.
One limit matters: the skill does not find papers. If you have no collection yet, run paper-navigator first — generic web search is off the table here, since the workflow needs Semantic Scholar and the citation graph. It also is not an idea generator or a Related Work section for your own paper; research-ideation and paper-writing cover those.
What it helps with
- synthesize a field overview from 30–120 collected papers
- group methods by mechanism instead of by year
- build a comparison table with advantages and limitations
- write the problem statement in LaTeX
- produce a survey draft for a conference or thesis
Example prompts for the agent
- build a literature survey on catalyst design for CO2 reduction from these papers
- write a systematic review from my collection, with comparison tables
- draft a field overview: outline, sections by mechanism, references
How to install research-survey
1Skills CLI (Vercel)
npx skills add EvoScientist/EvoSkills --skill research-survey -a claude-code2Manually: put the files in place
~/.claude/skills/research-survey/SKILL.mdCopy the whole skill folder; for one project use .claude/skills/research-survey/
1Skills CLI (Vercel)
npx skills add EvoScientist/EvoSkills --skill research-survey -a codex2Manually: put the files in place
.agents/skills/research-survey/SKILL.mdFor all projects use ~/.agents/skills/
1Skills CLI (Vercel)
npx skills add EvoScientist/EvoSkills --skill research-survey -a cursor2Manually: put the files in place
.cursor/skills/research-survey/SKILL.mdCursor also reads .agents/skills/ and .claude/skills/
1GitHub CLI
gh skill install EvoScientist/EvoSkills research-survey2Manually: put the files in place
.github/skills/research-survey/SKILL.md1Skills CLI (Vercel)
npx skills add EvoScientist/EvoSkills --skill research-survey -a gemini-cli2Platform CLI
gemini skills install https://github.com/EvoScientist/EvoSkills --path skills/research-surveyManually: put the files in place
.devin/skills/research-survey/SKILL.mdThe legacy .windsurf/skills/ path still works
Skills CLI (Vercel)
npx skills add EvoScientist/EvoSkills --skill research-survey -a opencodeClaude.ai
Zip the folder and upload it in Settings → Capabilities → Skills
Step by step, with commands for each platform: How to install skills in Claude Code: five ways
Questions about research-survey
How do I install research-survey in Claude?
Run npx skills add EvoScientist/EvoSkills --skill research-survey -a claude-code. The skill lands in ~/.claude/skills/research-survey/SKILL.md.
What does research-survey do?
It builds a survey report from a paper collection: a query-adaptive outline, a draft, section expansion, summaries and final assembly with references.
Do I need papers before using it?
Yes, the skill works on an existing collection. Without one, invoke paper-navigator first to gather 30–120 papers.
Source text
SKILL.md · Apache-2.0
---
name: research-survey
description: "Generates structured literature survey reports from collected papers using a multi-stage pipeline: outline generation (query-type adaptive) → draft survey → section-by-section expansion → summary section refinement → final assembly. Produces survey-grade output with taxonomy-based method analysis, LaTeX formalizations, comparative tables, and dense citations. Use when: user wants a literature review, research survey, field overview, or systematic synthesis of multiple papers. Do NOT use for finding/searching papers (use paper-navigator), generating research ideas (use research-ideation), or writing a paper's Related Work section (use paper-writing)."
allowed-tools: "write_file edit_file read_file think_tool"
metadata:
author: EvoScientist
version: '1.0.0'
tags: [core, research, literature, survey, synthesis]
---
# Research Survey
Generates high-quality, survey-grade literature reviews from papers collected by `paper-navigator`.
```
paper-navigator (collect 30-120 papers)
↓
Stage 1: Generate Outline (query-type adaptive structure)
↓
Stage 2: Draft Survey (outline + top-30 papers)
↓
Stage 3: Expand Sections (draft + all papers, section-by-section)
↓
Stage 4: Generate Section Summaries
↓
Stage 5: Refine Summary Sections (Abstract/Intro/Conclusion)
↓
Stage 6: Assemble + References
```
## When to Use
- User asks for a "literature review", "survey", "field overview", or "systematic review"
- User has collected papers and wants them synthesized into a structured report
- User wants to understand the full landscape of a research field
## When NOT to Use
- **Finding papers** → use `paper-navigator` first, then come here
- **Generating research ideas** → use `research-ideation`
- **Writing a Related Work section for a paper** → use `paper-writing`
## Dependency: paper-navigator
This skill requires papers as input. If the user hasn't provided papers, **first invoke `paper-navigator`** (Workflow 1, target 30-120 papers) to collect them.
**CRITICAL: All paper discovery 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 cannot access Semantic Scholar, citation graphs, or academic recommendation systems. Only `paper-navigator` provides the academic search infrastructure needed for survey-quality literature collection.
---
## Stage 1: Generate Outline
**This is a two-phase process.** Different fields have different survey conventions — a clinical systematic review looks nothing like a CS methods survey. First generate a domain-appropriate template, then create the detailed outline.
### Phase 1A: Generate Domain-Specific Survey Template
Before outlining, identify the field and adapt the structure:
1. **Identify the field** from the user's goal and collected papers
2. **Select section names and organization logic** using the field-specific conventions in `assets/survey-template.md` (e.g., medicine organizes by intervention type and follows PRISMA; chemistry organizes by reaction class; social sciences organize by theoretical perspective)
3. **Add field-specific sections** (e.g., Risk of Bias Assessment for medicine, Structure-Property Relationships for materials, Ethical Considerations for human-subjects research)
4. **Determine comparison table dimensions** appropriate to the field
### Phase 1B: Create Detailed Outline
With the domain-specific template as the framework, generate the outline:
#### Query Type Classification
| Type | Example | Structure |
|------|---------|-----------|
| **A: Single-topic deep dive** | "Catalyst design for electrochemical CO2 reduction" | Intro → Problem Definition → **Methods (by mechanism/approach)** → Evaluation → Challenges → Conclusion |
| **B: Multi-topic parallel** | "Drug resistance mechanisms and therapeutic strategies in cancer immunotherapy" | Intro → **Topic 1 (definition + methods)** → **Topic 2 (definition + methods)** → Evaluation → Challenges → Conclusion |
| **C: Pipeline/stage-based** | "From sample preparation to data analysis in single-cell RNA sequencing" | Chapters organized by workflow stages |
#### Outline Requirements
The outline is NOT a simple heading list — it's a **blueprint with meta-instructions** for each section. For each `## Section`:
- Include `[Instruction: ...]` specifying what the section must contain
- Specify required tables with field-appropriate columns
- For main body sections: mandate taxonomy by underlying principle/mechanism, NOT chronology
- Include any field-required elements (e.g., PRISMA flowchart for medical systematic reviews, mathematical formalism for physics)
See `references/survey-methodology.md` for full outline generation rules and `assets/survey-template.md` for field-specific conventions.
---
## Stage 2: Draft Survey
Generate a complete draft from the outline using the **top-30 most relevant papers**.
- Use numbered citations [1], [2, 3] throughout
- Follow the outline's meta-instructions strictly
- Each methods section must build a taxonomy and include comparison tables
- Problem definition must include LaTeX formalization (`$$...$$`)
---
## Stage 3: Expand Sections
Expand each non-summary section using **all collected papers** (30-120). This is where survey-grade depth is achieved.
### Section Expansion Targets
| Section Type | Target Length | Focus |
|---|---|---|
| **Methods** | 6000+ words per paradigm chapter | Technical narratives, mechanism analysis, comparison tables |
| **Evaluation** | 3500+ words | Benchmark taxonomy, metric analysis, SOTA summary |
| **Challenges** | 3000+ words | Problem definition + evidence + opportunity per challenge |
| **Applications** | 3000+ words | Real-world use cases with specific achievements |
| **Problem Definition** | 2000+ words | LaTeX formalization, constraints, assumptions |
| **Other** | 2500+ words | Default |
### Expansion Rules
1. **Thematic coherence**: Keep same themes and narrative flow as draft — don't introduce unrelated topics
2. **Cite comprehensively**: Use as many relevant papers from the full collection as possible
3. **Survey-grade depth**: Multi-paragraph technical narratives per method family, not shallow bullet points
4. **For each paradigm/method family, include**:
- Technical narrative: How it works, theoretical assumptions, nuances between papers
- Critical analysis: Why effective, trade-offs, failure modes
- Comparative analysis table: Method | Core Mechanism | Key Advantage | Limitation | Performance
---
## Stage 4: Generate Section Summaries
After all content sections are expanded, generate a condensed summary for each major section:
1. Summarize each expanded section in 150-300 words
2. Preserve the key taxonomy, representative methods, and main trade-offs
3. Keep citation anchors so later summary sections remain grounded
These section summaries become the shared context for the final abstract, introduction, and conclusion.
---
## Stage 5: Refine Summary Sections
After all content sections are expanded, refine the **summary sections** (Abstract, Introduction, Conclusion):
1. Use all section summaries as context to rewrite Abstract, Introduction, Conclusion
2. This ensures summary sections accurately reflect the full survey content
### Summary Section Standards
**Abstract** (300-500 words):
- Continuous narrative, NO bullet points or bold labels
- Must cover: background → gap → scope → key findings → outlook
**Introduction**:
- Continuous narrative, NO subsections or bullet points
- Must cover: research background → why traditional methods fail → method summary → scope & organization
**Conclusion**:
- Summarize findings, state which paradigm is most promising
- Respond to user's original research goal
- Provide clear "next step" recommendation
---
## Stage 6: Assemble Final Survey
Assemble sections in outline order, then append formatted references:
```
**1. Title** (Year). _Authors_. *Venue*. Citations: N. [[Link]](url)
```
Save to `/artifacts/survey-{topic}-{date}.md`.
---
## Core Quality Principles
1. **Build taxonomy, don't enumerate**: Cluster papers by technical mechanism, not chronology. This is the defining characteristic of a survey vs. a summary.
2. **Critical insight over description**: For EVERY method, analyze WHY it works, WHAT trade-off it makes, WHERE it fails. This separates survey-grade writing from shallow summaries.
3. **Goal-centric filtering**: Every piece of information must answer "How does this help achieve the research goal?" Discard information that doesn't serve the goal, even if it's interesting.
4. **Strict terminology fidelity**: Use the user's exact technical terms. Do NOT drift to related but different concepts.
5. **Dense citations**: Ground ALL claims with numbered citations [X]. Nearly every sentence should reference at least one paper.
6. **Zero vagueness**: Replace generic statements with specific method names, dataset names, metric values, and problem descriptions.
7. **Visual structure**: Use Markdown tables extensively — paradigm comparison, intra-paradigm method comparison, benchmark tables, metric tables.
---
## Reference Materials
| Resource | Location | Purpose |
|----------|----------|---------|
| Multi-stage pipeline details | `references/survey-methodology.md` | Full methodology: outline rules, section standards, expansion targets |
| Section quality checklist | `references/section-quality-checklist.md` | Per-section verification checklist before finalizing |
| Survey output template | `assets/survey-template.md` | English Markdown template with section structure, table formats, and placeholder guidance |
---
## Handoff
| From → To | When |
|-----------|------|
| `paper-navigator` → here | Papers collected, user wants synthesis |
| Here → `research-ideation` | Survey reveals research gaps worth pursuing |
| Here → `paper-writing` | Survey informs Related Work section of a paper |
| Here → `paper-planning` | Survey provides literature context for story design |
© EvoScientist · Apache-2.0 · EvoScientist/EvoSkills