jxtse/scientific-research-skills/related-work-survey

Claude Code and agent skill by jxtse/scientific-research-skills

Trust tier 2
  • community
LLM review pending
Source check pending

Checking that the SKILL.md file is still in the repository.

Conducts a systematic related-work / literature-survey / state-of-the-art review for a research question by defining survey dimensions, searching each axis, building a taxonomy of prior work, identifying the gap, and producing a positioning narrative for a paper's Related Work section. Goes beyond a flat paper list to deliver structured analysis. Use when the user is starting a new research project and needs to map the landscape, asks "what's been done on X?" or "how does my idea compare to existing work?", needs to write or revise a Related Work / Background / Prior Art section, wants to identify a research gap or position their contribution, or asks to build a taxonomy of approaches in a research area.

Install

npx vskill@latest install jxtse/scientific-research-skills/related-work-survey

SKILL.md

Related Work Survey

Systematic literature survey for positioning a research contribution.

When to Use

  • User starts a new research project and needs landscape understanding
  • User asks "what's been done on X?"
  • User needs to write a related work section
  • User wants to identify the gap their work fills

Workflow

Step 1: Define the Research Question

Work with the user to pin down:

  • Core question: What specific problem are we solving?
  • Key concepts: What are the 3-5 key terms/concepts?
  • Scope boundaries: What's in scope vs. adjacent but out of scope?
Step 2: Identify Survey Dimensions

Every research topic sits at an intersection of multiple dimensions. Identify 2-4 axes:

Example for "Neural-Symbolic Decomposition in LLM Agents":

  • Axis 1: Neuro-symbolic integration approaches
  • Axis 2: LLM agent architectures (harness, scaffolding)
  • Axis 3: Adaptive/metacognitive planning
  • Axis 4: Text analysis at scale (specific application)
Step 3: Search Each Dimension

For each axis, use literature-search skill with targeted queries:

Axis 1 → "neuro-symbolic integration LLM reasoning 2024 2025"
Axis 2 → "LLM agent harness scaffolding architecture survey"
Axis 3 → "metacognitive planning adaptive tool use LLM"
Axis 4 → "large scale text analysis LLM code generation quality"

Collect 10-20 papers per axis, then deduplicate across axes.

Step 4: Build the Taxonomy

Organize papers into a structured taxonomy:

markdown
## Related Work Taxonomy
### 1. Neuro-Symbolic Integration
1.1 Neural reasoning with symbolic verification
1.2 Symbolic planning with neural execution
1.3 Adaptive decomposition (our focus)
### 2. LLM Agent Architecture
2.1 Harness engineering
2.2 Self-evolving agents
2.3 Meta-Harness optimization
### 3. ...
Step 5: Identify the Gap

The gap is where your work lives — the intersection that no existing paper covers:

Paper A does X but not Y.
Paper B does Y but not X.
We do both X and Y, connected by Z.
Step 6: Write the Narrative

Structure the related work section as a story:

  1. Context: What's the broader field?
  2. Prior art per axis: What has been done?
  3. Gap statement: What's missing?
  4. Our position: How do we fit in?

Output Format

Deliver a structured document:

markdown
# Related Work Survey: [Topic]
## Research Question
[1-2 sentences]
## Taxonomy
[Structured tree]
## Key Papers
| Paper | Axis | Key Contribution | Gap Relative to Us |
|-------|------|------------------|--------------------|
| ... | ... | ... | ... |
## Identified Gap
[Clear statement of what no existing work covers]
## Recommended Positioning
[How to frame our contribution relative to existing work]

Principles

  • Be honest about overlap: If someone has done something very similar, acknowledge it
  • Recent > old: Prioritize 2024-2026 papers for positioning; cite older seminal works for foundations
  • Quality > quantity: 20 well-chosen papers > 50 tangentially related ones
  • Track the conversation: Note who cites whom — citation chains reveal intellectual lineage
  • Check workshops/findings: Important early-stage work often appears in workshop papers

Security

  • Security scanPassed, 100 of 100Pattern checks on the skill's files.
  • Model reviewNot runA model review is scheduled for the next rescan.

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

Popularity

Repository stars
71
Repository forks
7
7-day trend score
21

Stars and forks are for the whole GitHub repository, not this skill alone. Not refreshed yet.

Works with

Any agent vskill installs to: 56 targets in all. These 39 have a page on this site.

  • Claude Code
  • Cursor
  • GitHub Copilot
  • Windsurf
  • Codex
  • Gemini CLI
  • Cline
  • Amp
  • Roo Code
  • Goose
  • Continue
  • Kilo Code
  • Junie
  • Trae
  • OpenHands
  • Qwen Code
  • AI Agent
  • Tabnine Chat
  • Sourcegraph Cody
  • Amazon Q
  • Firebase Genkit
  • Sweep
  • Devin
  • Phind
  • Mentat
  • Claude Desktop
  • Aider
  • GPT Pilot
  • Plandex
  • bolt.new
  • v0
  • Replit Agent
  • Anse
  • TypingMind
  • BetterChatGPT
  • LibreChat
  • Warp AI
  • Pieces
  • Fig AI
Embed badge
vskill VERIFIED badge
[![vskill VERIFIED](https://verified-skill.com/api/v1/skills/jxtse/scientific-research-skills/related-work-survey/badge)](https://verified-skill.com/skills/jxtse/scientific-research-skills/related-work-survey)

Something wrong with this skill or its page? Tell us.