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Related Work Survey

Community
jxtse
related-work-survey

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.

Overview

Publisherjxtse
Repositoryscientific-research-skills
Skill namerelated-work-survey
Stars
76
Forks
6
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by jxtse on GitHub. Read the source before you install it.

Installation

Install the Related Work Survey AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/jxtse/scientific-research-skills/tree/main/skills/related-work-survey
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/jxtse/scientific-research-skills.git /tmp/scientific-research-skills
mkdir -p .claude/skills
cp -r /tmp/scientific-research-skills/skills/related-work-survey .claude/skills/related-work-survey
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Related Work Survey in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Related Work Survey on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Related Work Survey is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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

Frequently asked questions

What does the Related Work Survey AI skill do?

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 i...

Why use Related Work Survey on TypingMind?

Because you install it once and use it with any model. Related Work Survey is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Related Work Survey in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/jxtse/scientific-research-skills/tree/main/skills/related-work-survey. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Related Work Survey?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Related Work Survey?

As many as you like. As long as a model supports skills, you can use Related Work Survey with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Related Work Survey AI skill free?

Yes. It is published on GitHub by jxtse under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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