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Surveying Prior Work

Organization
K-Dense-AI
surveying-prior-work

Use after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation

Overview

PublisherK-Dense-AI
Repositoryscience-superpowers
Skill namesurveying-prior-work
Stars
360
Forks
37
Bundled files
Instructions only
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 K-Dense-AI on GitHub. Read the source before you install it.

Installation

Install the Surveying Prior Work 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/K-Dense-AI/science-superpowers/tree/main/skills/surveying-prior-work
  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/K-Dense-AI/science-superpowers.git /tmp/science-superpowers
mkdir -p .claude/skills
cp -r /tmp/science-superpowers/skills/surveying-prior-work .claude/skills/surveying-prior-work
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Surveying Prior Work 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 Surveying Prior Work 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 Surveying Prior Work 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.

Surveying Prior Work

Overview

Before designing an analysis, ground the question and your chosen methods in what is already known. Most questions have established methods, known confounds, and prior effect sizes. Reinventing a method badly — or rediscovering a known artifact and reporting it as a finding — wastes effort and erodes credibility.

Core principle: Find out what is already known before you generate new claims.

This is the science analog of reading the existing codebase before writing new code. It is a flexible skill — adapt depth to the stakes of the investigation.

When to Use

  • After framing-research-questions, before designing-the-analysis
  • When selecting a statistical method or model and unsure what is standard
  • When a result looks novel — check whether it is a known effect or artifact first
  • When you need a plausible prior effect size to power the study
  • When you suspect confounds but don't know which are established in the field

What to Ground

Survey four things:

  1. Established methods — what is the standard, accepted way to analyze this kind of question? What are its assumptions and failure modes?
  2. Known confounds and artifacts — what variables are known to drive this outcome? What measurement artifacts are common in this data type?
  3. Prior effect sizes — what magnitude have others found? This feeds the power/sample-size calculation in the analysis design.
  4. Relationship to prior work — is this a replication, an extension, a new population, or genuinely novel? Be honest about which.

The Process

  1. List what needs grounding — turn the "open questions for prior-work survey" from the question document into concrete search targets.
  2. Search — use web search and any available literature tools for published methods and effect sizes; search the repo and your human partner's prior analyses for in-house precedent and conventions.
  3. For broad surveys, dispatch parallel subagents — one per independent sub-topic (a method, a confound, a prior estimate). REQUIRED: use science-superpowers:dispatching-parallel-investigations for the workflow. This keeps your own context clean.
  4. Synthesize — write a short prior-work note (or a section appended to the question document):
    • Established method(s) you will adopt and why
    • Confounds you must measure or control for
    • Prior effect size(s) and the source, for powering the design
    • Honest statement of how this investigation relates to prior work
  5. Cite sources — record where each fact came from so the design and final report can reference it. When you present findings from web search, follow your platform's citation standards.

Red Flags

  • Reaching for a method you invented when a standard one exists → find and use the standard one (or justify the deviation explicitly)
  • Designing the analysis with no idea what effect size to expect → you skipped the power input; survey first
  • About to report something as novel without having checked → search first; novelty is a claim that needs evidence
  • Ignoring a confound because checking is inconvenient → known confounds are not optional

Handoff

After grounding, invoke science-superpowers:designing-the-analysis. Bring forward the adopted methods, the confound list, and the prior effect size for powering the design.

Frequently asked questions

What does the Surveying Prior Work AI skill do?

Use after framing a question and before designing an analysis, or when choosing a method, judging whether a result is novel, or needing a prior effect size for a power calculation

Why use Surveying Prior Work on TypingMind?

Because you install it once and use it with any model. Surveying Prior Work 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 Surveying Prior Work in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/K-Dense-AI/science-superpowers/tree/main/skills/surveying-prior-work. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Surveying Prior Work?

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 Surveying Prior Work?

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

Is the Surveying Prior Work AI skill free?

It is published on GitHub by K-Dense-AI. 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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