Draft Survey

Community

ShaishavMaisuria

draft-survey

Turns a topic into a ranked reading list of the best papers plus a two-column, arXiv-ready literature-review/survey draft grounded in verified citations. Use when a researcher says "write a survey on X", "do a literature review of X", "give me a ranked reading list on X", "what should I read on X first", "draft a survey paper for arXiv", or "two-column related-work survey on X". Searches across DBLP/Crossref/Semantic Scholar/arXiv, ranks candidates by citation impact, venue strength, recency, and citation-graph centrality, clusters them into a taxonomy, verifies every reference, and writes an original-prose survey (intro, taxonomy, per-theme synthesis, open problems) where each claim cites a real, resolved paper. Never fabricates citations, never copies source text — original synthesis only; the author stays the author. Trigger words - survey, literature review, reading list, ranked papers, what to read, arxiv survey, two-column review.

Overview

Publisher	ShaishavMaisuria
Repository	`research-paper-lifecycle-skills`
Skill name	`draft-survey`
Stars	59
Forks	22
Bundled files	2
License	Apache-2.0
Links	Skill source Repository Homepage

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.
2 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by ShaishavMaisuria on GitHub. Read the source before you install it.

Installation

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

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.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/draft-survey
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/draft-survey

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

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/ShaishavMaisuria/research-paper-lifecycle-skills.git /tmp/research-paper-lifecycle-skills
mkdir -p .claude/skills
cp -r /tmp/research-paper-lifecycle-skills/skills/draft-survey .claude/skills/draft-survey

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Draft 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 Draft 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 Draft 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.

Draft Survey

Two deliverables from one topic: a ranked reading list (what to read, best first, with a one-line why) and a two-column, arXiv-ready survey draft that synthesizes the area in original prose with verified citations. Composes find-papers, verify-citations, study-exemplars, and draft-related-work.

When to use vs. literature-review

literature-review builds the related-work for your own paper — scoped to your contribution.
draft-survey produces a standalone survey/review document (a reading list + a publishable 2-column draft) of a whole area. Use it to learn a field fast or to draft a survey paper for arXiv.

Inputs

The topic (e.g. "geospatial data conflation"), optionally a sub-scope and a target length (default 5–6 two-column pages).
Optional: a venue/format (default: generic two-column article, which compiles on arXiv).

Process

Gather candidates broadly. Run find-papers across DBLP + Crossref + Semantic Scholar + arXiv for the topic and its synonyms; then run its citation-graph expansion so seminal anchors and direct lineages are not missed (a survey that omits the foundational papers is a weak survey).
Rank them. Run python3 scripts/rank_papers.py candidates.json — a composite of normalized citation count, venue tier, recency, and citation-graph centrality (weights documented in references/ranking-criteria.md). Output the ranked reading list: rank, title, authors, year, venue, a citation/impact signal, and a one-line why read this (seminal / survey / SOTA / dataset / contrarian). Keep seminal and recent both represented.
Verify every entry. Route the list through verify-citations so each has a real DOI/arXiv id; drop or flag anything unresolved. A survey with a fabricated reference is disqualifying.
Build a taxonomy. Cluster the verified papers into 3–6 themes/sub-problems (the survey's section structure), each with its lineage (foundational → recent).
Draft the survey, in original prose. Write a two-column .tex: abstract, introduction (scope + why a survey now), one section per theme (synthesize and contrast methods — never copy source sentences), a cross-cutting comparison (a table helps), open problems / future directions, conclusion, and \bibliography. Every claim cites a verified paper. Target the requested length.
Make it arXiv-ready. Ensure it compiles (latexmk), uses a portable two-column class, and the .bib is clean. arXiv has no peer-review desk-reject, but it expects a compilable source and a real abstract; preflight-check can sanity-check length/structure.

Output

reading-list.md — the ranked, verified, annotated reading list.
survey.tex + refs.bib — the two-column, arXiv-ready draft (original prose, verified citations).
Both written to paper-workspace/research/ or a path the user names.

Guardrails

Real citations only. Every reference is resolved via verify-citations; never invent a paper, DOI, author, or year to fill a gap.
Original synthesis, not copying. Summarize and contrast in your own words; do not paste sentences from abstracts or papers. No paper content is bundled (fetch on demand, process transiently).
No completeness theater. State the search scope and that a survey is never exhaustive; flag themes where coverage is thin rather than padding.
Copilot, not pilot: the author reviews the reading list and the draft, and is the author of any submission.

Source verification

Citation impact and venue facts come from live scholarly APIs, not memory, and each cited paper carries a resolvable identifier the user can open.

Memory

Uses .paper-memory/ as described by paper-memory-convention.md: read prior reading lists/lessons for the topic at start; append the topic, the chosen taxonomy, and any coverage gaps at end (deduped).

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Draft Survey AI skill do?

Why use Draft Survey on TypingMind?

Because you install it once and use it with any model. Draft 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 Draft Survey in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ShaishavMaisuria/research-paper-lifecycle-skills/tree/main/skills/draft-survey. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Draft 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 Draft Survey?

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

Is the Draft Survey AI skill free?

Yes. It is published on GitHub by ShaishavMaisuria under the Apache-2.0 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Draft Survey

When to use vs. literature-review

Inputs

Process

Output

Guardrails

Source verification

Memory

Bundled files

Frequently asked questions

Related AI Skills

Set up your own AI workspace now