Literature Research logo

Literature Research

Community
fcakyon
literature-research

Use when the user wants to find related work, survey a research area, identify literature gaps, or discover open-source implementations. Triggers on phrases like "find papers on", "related work", "literature review", "what papers exist", "open source implementation", or "papers with code".

Overview

Publisherfcakyon
Repositoryphd-skills
Skill nameliterature-research
Stars
431
Forks
37
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 fcakyon on GitHub. Read the source before you install it.

Installation

Install the Literature Research 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/fcakyon/phd-skills/tree/main/plugin/skills/literature-research
  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/fcakyon/phd-skills.git /tmp/phd-skills
mkdir -p .claude/skills
cp -r /tmp/phd-skills/plugin/skills/literature-research .claude/skills/literature-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Literature Research 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 Literature Research 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 Literature Research 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.

Literature Research Methodology

You are helping a researcher conduct systematic literature research. Follow this methodology to ensure thorough, accurate coverage.

Step 1: Scope Definition

Before searching:

  • Clarify the exact research question or topic boundary
  • Identify key terms and their synonyms (e.g., "content moderation" = "safety filtering" = "NSFW detection")
  • Define inclusion/exclusion criteria (year range, venue type, methodology type)
  • Ask if the user has seed papers to start from

Step 2: Systematic Search

Use multiple search strategies in order:

2a. Direct Search

Use Paper Search CLI; follow setup if it is missing.

bash
paper-search search 'object detection' -s fast -n 5
paper-search search 'object detection' -s arxiv,crossref -n 5

Inspect the JSON errors field and retain each paper's source ID and URL. Report unavailable providers rather than treating errors as an empty literature search. Vary search terms to catch different framings of the same concept. Use web search for proceedings and sources the CLI cannot access.

2b. Citation Chaining

From seed papers or initial results:

  • Forward chaining: run paper-search tool get_citing_papers <doi>
  • Backward chaining: run paper-search tool get_referenced_papers <doi>
  • Both accept a DOI or OpenAlex work ID, not a title. Inspect pagination and truncation metadata; neither command promises an exhaustive citation list. Read the paper's reference section to verify cited works.
  • This catches papers that use different terminology but address the same problem

2c. Venue Mining

  • Identify top venues for the topic (conferences, journals, workshops)
  • Check recent proceedings of these venues for relevant papers
  • Workshop papers often contain early-stage work not yet in main conferences

2d. Open Source Discovery

  • Search GitHub for implementations related to the topic
  • Check Papers With Code for the specific task/dataset
  • Look for "awesome-X" lists curated by the community

Step 3: Categorization

Organize found papers into a structured taxonomy:

| Paper | Year | Venue | Approach | Key Result | Code? | Relevance |
|-------|------|-------|----------|-----------|-------|-----------|

Group by methodology or approach type, not chronologically.

Step 4: Gap Identification

Map what exists vs. what's missing:

  1. Coverage matrix: rows = problem aspects, columns = existing approaches
  2. Empty cells = potential gaps
  3. For each candidate gap:
    • Search specifically for work filling this gap (it may exist under different terms)
    • Check very recent papers (last 6 months) that might have addressed it
    • Assess whether the gap is meaningful (would filling it advance the field?)

Step 5: Gap Validation

For each identified gap, verify it's real:

  • Searched with at least 3 different phrasings
  • Checked proceedings of top-3 venues from last 2 years
  • No preprint on arXiv addressing this gap
  • The gap is technically feasible to address
  • Filling the gap would be a meaningful contribution

Rate confidence: HIGH (extensively searched, clearly missing), MEDIUM (searched but might have missed niche work), LOW (limited search, gap may exist elsewhere).

Step 6: Output Format

Produce a structured research landscape:

  1. Topic summary — 2-3 sentence overview of the research area
  2. Paper table — categorized list with year, venue, approach, results, code availability
  3. Gap table — identified gaps with confidence levels and evidence
  4. Recommended readings — top 5-10 most relevant papers for the user's specific work
  5. Candidate citations — BibTeX entries for papers worth citing (MUST verify against DBLP before presenting)

Citation Integrity

Every paper mentioned must have verified metadata:

  • Author names: cross-check with DBLP or the paper's official page
  • Venue and year: confirm against the published version (not preprint)
  • Claims about results: only include numbers you can trace to a specific table/figure in the paper
  • If uncertain about any detail, flag it explicitly rather than guessing

Frequently asked questions

What does the Literature Research AI skill do?

Use when the user wants to find related work, survey a research area, identify literature gaps, or discover open-source implementations. Triggers on phrases like "find papers on", "related work", "literature review", "what papers exist", "open source implementation", or "papers with code".

Why use Literature Research on TypingMind?

Because you install it once and use it with any model. Literature Research 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 Literature Research in TypingMind?

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

Which AI models can use Literature Research?

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 Literature Research?

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

Is the Literature Research AI skill free?

Yes. It is published on GitHub by fcakyon 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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