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Survey And Audit Novelty

Community
gxCaesar
survey-and-audit-novelty

Use when deciding whether a research direction is still open, including a literature survey that has to end in a decision, a novelty audit, a prior art check, a scoop check against recent work, asking whether anyone has already done this, or checking that the variables an idea needs actually co-exist in one obtainable dataset. Use before an experimental plan is written, and again whenever a near-identical paper appears.

Overview

PublishergxCaesar
Repositoryopen-research-skills
Skill namesurvey-and-audit-novelty
Stars
63
Forks
3
Bundled files
8
LicenseApache-2.0
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.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

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

Installation

Install the Survey And Audit Novelty 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/gxCaesar/open-research-skills/tree/main/skills/survey-and-audit-novelty
  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/gxCaesar/open-research-skills.git /tmp/open-research-skills
mkdir -p .claude/skills
cp -r /tmp/open-research-skills/skills/survey-and-audit-novelty .claude/skills/survey-and-audit-novelty
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Survey And Audit Novelty 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 And Audit Novelty 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 And Audit Novelty 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 and Audit Novelty

Turn a direction into a decision. The survey ends with a verdict rather than a reading list, the novelty audit tries to destroy the idea rather than confirm it, and the feasibility check asks whether the data the idea needs exists in one place.

Three verdicts come out and they are recorded separately, because merging them hides which one actually fired.

Select one mode

ModeResponsibilityRead first
contribution-laneName the lane before searching, because the lane decides what counts as a collisionreferences/contribution-lanes.md
joint-variable-auditCheck that the variables the hypothesis needs co-exist in one obtainable cohortreferences/joint-variable-audit.md
search-anglesRun the survey across several independent framings and record what each returnedreferences/search-angles.md
kill-layerAdjudicate collisions and name which layer a kill lands onreferences/kill-layers.md

Run contribution-lane first. A search launched without a declared lane produces "this space is taken", which is a statement about the lane, not about the space.

Resolve the installed skill and components

Resolve SKILL_DIR as the directory containing this SKILL.md. Every runtime resource is below that directory; never resolve through a package parent.

Record three verdicts, not one

FieldValuesMeaning
scoop_verdictIDENTICAL, ADJACENT, UNCERTAINWhether existing work is the same work
feasibilitysupported by measured headroom and a measured noise floorWhether the task has room left
venue_fita judgement, with reasonsWhether the result would be publishable where you intend

venue_fit may recommend stopping, but it must be reported under its own name. A venue-fit doubt presented as a collision is a silent rejection, and it is unappealable because the reader cannot see what was actually decided.

Only IDENTICAL kills. Adjacent work is a baseline to build on and to compare against, not a verdict.

Audit the data before auditing the idea

Before any novelty judgement, establish that the variables the hypothesis needs are observed together in samples you can actually obtain. Conditions satisfied separately with an empty intersection is the most common way a direction dies, and it is invisible until counted.

The evidence is a measured co-occurrence table, produced before any analysis code is written. "The data all exists" is not that table.

An empty intersection does not close the direction. Three moves come next, and skipping them turns a data question into an unsupported claim that the data does not exist: widen or substitute the source, relax the matching level to a coarser but still meaningful unit, or assemble the pairing yourself and say so.

Say what you searched

"No such data exists" and "no one has done this" are strong assertions. Both require an enumerated list of what was checked. Not having looked is not evidence of absence, and the list is what a reader uses to find the resource you missed.

When quoting another paper's ablation, limitation or conclusion in support of a kill, read the whole passage first. A verbatim half-sentence used against its own context is more dangerous than no citation, because it is checkable and wrong.

Name the layer before reporting a kill

A collision report is not automatically a kill. State which of three layers died.

  1. Framing. The way the question is phrased is taken. Change the framing; the underlying asset is untouched. This layer is routinely misreported as a dead asset.
  2. Data. The required observations are unavailable. This claim must pass the enumeration rule above; the first place you looked coming up empty is not this layer.
  3. Task. Measured headroom is at or below the measured noise floor. This is the only layer that kills the asset.

A kill that cannot name its layer has not been established.

Read per-item verdicts before the summary verdict. A summary verdict is written at the widest scope in the report, and the widest scope is the one most likely to be occupied already, so a narrow claim can survive a report whose headline says otherwise.

Audit your own kills before reporting them

List every kill, then ask for each one what single correction to the search or the framing would reverse it. If most of them would be reversed by an available correction, the audit was measuring your fatigue rather than the literature. Report the count.

Boundaries

This skill does not design experiments, does not measure headroom itself, and does not write the plan that consumes its output. It produces the charter, the survey and the audit that a planning step reads.

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 And Audit Novelty AI skill do?

Use when deciding whether a research direction is still open, including a literature survey that has to end in a decision, a novelty audit, a prior art check, a scoop check against recent work, asking whether anyone has already done this, or checking that the variables an idea needs actually co-exist in one obtainable dataset. Use before an experimental plan is written, and again whenever a near-identical paper appears.

Why use Survey And Audit Novelty on TypingMind?

Because you install it once and use it with any model. Survey And Audit Novelty 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 And Audit Novelty in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gxCaesar/open-research-skills/tree/main/skills/survey-and-audit-novelty. 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 And Audit Novelty?

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 And Audit Novelty?

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

Is the Survey And Audit Novelty AI skill free?

Yes. It is published on GitHub by gxCaesar 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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