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Survey Generator

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rohitg00
survey-generator

Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.

Overview

Publisherrohitg00
Repositorypro-workflow
Skill namesurvey-generator
Stars
2.9K
Forks
286
Bundled files
2
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.

  • 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 rohitg00 on GitHub. Read the source before you install it.

Installation

Install the Survey Generator 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.
  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/rohitg00/pro-workflow.git /tmp/pro-workflow
mkdir -p .claude/skills
cp -r /tmp/pro-workflow/skills/survey-generator .claude/skills/survey-generator
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Survey Generator 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 Survey Generator 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 Survey Generator 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.

Survey Generator

Provider-agnostic literature-survey artifact generator. Output flows into a pro-workflow wiki, not a standalone HTML file — survives sessions and indexes for FTS5 retrieval.

Diff vs dair-academy version

dairpro-workflow
Hardcoded Kimi K2.6 on FireworksProvider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom)
Output = single-file HTML with inline SVGOutput = wiki markdown page + bibliography rows in sources.md
One-off artifact, no follow-upPersists in FTS5 index; reused by wiki-research-loop
Manual run onlyComposable with /wiki research for auto-bibliography expansion

When to use

  • "Survey on " / "lit review on "
  • Onboarding a new domain — generate the map-of-the-field
  • After a wiki has 10-30 sources, compile a synthesis page over them
  • Pre-step before /wiki research runs: gives the loop a high-quality seed bundle

Inputs

InputRequiredDescription
topicyes"Reasoning Models", "Agentic Engineering"
source_urlyesPublic anchor: arXiv survey, GitHub awesome-list, canonical blog post
--wiki <slug>yesTarget wiki for the artifact
--bibliography-size NnoDefault 20. 40-50 comprehensive, 80-100 exhaustive
--section-count NnoDefault 6-10 numbered sections
--provider namenoOverride provider (default: first env var found)
--model idnoOverride model

Workflow (the agent runs these in order)

Step 1 — Read the anchor

WebFetch source_url. Extract subtopics + cited papers. For GitHub awesome-lists, walk README + linked papers files. For arXiv survey PDFs, use abstract + ToC.

Step 2 — Build research_bundle.json

Use templates/research_bundle.template.json as scaffold. Required keys:

json
{
  "topic": "...",
  "anchor_source": "...",
  "abstract_hints": ["..."],
  "taxonomy": [{"branch": "...", "children": [{"name": "...", "description": "..."}]}],
  "sections": [{"title": "...", "guidance": "...", "papers": ["key1","key2"]}],
  "bibliography": [{"key": "author-year-shortname", "authors": "...", "year": 2024, "title": "...", "venue": "...", "summary": "..."}]
}

Hard rules:

  • Every paper in bibliography must be real. No invented entries.
  • Every key referenced in sections[].papers must exist in bibliography.
  • 4-8 taxonomy branches, 2-4 children each.
  • 6-10 numbered sections covering: introduction → foundations → methods → evaluation → open problems.

Step 3 — Run the generator

bash
node $SKILL_ROOT/scripts/build-survey.js \
  --bundle <path-to-research_bundle.json> \
  --wiki <slug> \
  [--provider anthropic|openai|openrouter|fireworks|custom] \
  [--model <id>]

Generator:

  1. Reads bundle.
  2. Sends to LLM with strict markdown spec (numbered sections, inline [^paper-key] citations, no HTML).
  3. Writes output to <wiki>/derived/surveys/<topic-slug>.md.
  4. Appends bibliography rows to <wiki>/sources.md (deduped by key).
  5. Calls wiki-cli.js page to upsert into FTS5 index.

Step 4 — Iterate

If prose is thin: tighten sections[].guidance and rerun. Output filename versions automatically (<slug>-v2.md, <slug>-v3.md).

To compare providers:

bash
node build-survey.js --bundle bundle.json --wiki agent-memory --provider openai --model gpt-4o
node build-survey.js --bundle bundle.json --wiki agent-memory --provider anthropic --model claude-opus-4-7

Each writes a separate versioned file; diff them.

Output structure

text
<wiki-root>/
├── sources.md                                 # bibliography rows appended (deduped)
└── derived/surveys/
    └── <topic-slug>-v1.md                     # the survey
        # title (h1)
        # ## 1. Introduction
        # ## 2. Foundations
        # ...
        # ## References
        # [^src-bib-<slug>] author year. title. venue.

Hard rules

  1. Never invent bibliography entries — every paper must be a real work with venue.
  2. Every section's papers array references keys in bibliography.
  3. Output is markdown ONLY. No HTML, no inline SVG, no JS.
  4. Bibliography rows in sources.md use the slug-style id src-bib-<slug> (derived from the bibliography key); cite as [^src-bib-<slug>]. Manual non-bibliography sources continue to use src-NNN.
  5. Iterate on inputs (research_bundle.json), not on the generated output.
  6. Provider+model selection is the user's call — never hardcode.

Composing with research loop

bash
/wiki init reasoning-models --title "Reasoning Models" --flavor research
# Manually compile a research_bundle.json
node skills/survey-generator/scripts/build-survey.js --bundle bundle.json --wiki reasoning-models
# Now the wiki has a structured survey + 50 bibliography rows
# Enable auto-research to expand:
# (edit reasoning-models/wiki.config.md, set auto_research.enabled: true)
node skills/wiki-research-loop/scripts/research-loop.js seed reasoning-models "chain-of-thought failure modes" --depth 0
node skills/wiki-research-loop/scripts/research-loop.js run reasoning-models

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 Survey Generator AI skill do?

Compile a structured literature survey on any AI/ML topic. Agent curates a research bundle (taxonomy + sections + bibliography of real papers) from a public anchor resource, then a chosen LLM generates the survey artifact. Output target is a wiki page (markdown), not a one-off HTML — survey lands in `<wiki>/derived/surveys/<slug>.md` with full bibliography rows in `sources.md`. Provider-agnostic (Anthropic/OpenAI/OpenRouter/Fireworks/custom OpenAI-compat). Use when the user asks for a "survey", "literature review", "lit review", or "deep dive" on a technical topic.

Why use Survey Generator on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rohitg00/pro-workflow/tree/main/skills/survey-generator. 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 Survey Generator?

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 Survey Generator?

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

Is the Survey Generator AI skill free?

It is published on GitHub by rohitg00. Check the repository for licensing terms. 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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