Coverage White Space Search

Organization

yogsoth-ai

coverage-white-space-search

Map known methods against problem/parameter space and generate candidates from uncovered and weakly covered intersections. Acquisition mechanism is runtime-selected.

Overview

Publisher	yogsoth-ai
Repository	`de-anthropocentric-research-engine`
Skill name	`coverage-white-space-search`
Stars	505
Forks	43
Bundled files	Instructions only
License	Apache-2.0
Links	Skill source Repository

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 yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Coverage White Space Search 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/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/coverage-white-space-search
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/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/coverage-white-space-search

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/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/coverage-white-space-search .claude/skills/coverage-white-space-search

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

Use it in TypingMind

Enable Coverage White Space Search 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 Coverage White Space Search 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 Coverage White Space Search 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.

coverage-white-space-search

Purpose

Map known methods against a problem space and generate candidates from intersections that are uncovered or covered without being closed.

Input contract

yaml
required: [reference_items, coverage_dimensions]
optional: [performance_records, eligibility_rules]
constraints: [reference items and dimension values must be identifiable]

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

You MUST load skill inventory-reference-items to inventory references.
You MUST load skill map-coverage-space to map coverage and grade its strength.
You MUST load skill detect-coverage-gap to detect uncovered and weakly covered regions.
You MUST load skill synthesize-idea to synthesize candidates. If a candidate gap is ready to become a testable explanation, consider formulate-hypotheses. If the gap itself still requires evidence validation, consider validate-research-gap. Deviation: acquisition is runtime-selected; do not infer coverage from missing records.

Output contract

yaml
produces: [reference_inventory, coverage_map, white_space_gaps, candidate_ideas]
delta_fields: [findings, evidence_updates, hypothesis_updates, decisions, open_questions]

Thresholds and quality gates

B: every reported gap is either an explicit uncovered intersection over the declared dimensions or an occupied intersection graded below full support, with its covering items and specific shortfall named; duplicate or ineligible regions are excluded.

Failure and counterexamples

Do not label a region white space when it is merely unsearched, incomparable, or outside the declared universe, and do not treat a region as closed because items occupy it.

Provenance map

systematic-enumeration, benchmark-sweep, coverage-analysis, white-space-identification, benchmark-inventory, coverage-gap-detection, white-space-detection: resolved/concept by exact lookup.
Status: all except benchmark-inventory resolved; benchmark-inventory concept (only package-prefixed variant found).

Preserved source criteria ledger

Preserve inventory -> coverage crossing -> gap detection -> candidate synthesis.

Context checkpoint / Delta notes

Append inventory additions, occupied cells with strength grades, gap definitions, exclusions, and generated candidates.

Frequently asked questions

What does the Coverage White Space Search AI skill do?

Map known methods against problem/parameter space and generate candidates from uncovered and weakly covered intersections. Acquisition mechanism is runtime-selected.

Why use Coverage White Space Search on TypingMind?

Because you install it once and use it with any model. Coverage White Space Search 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 Coverage White Space Search in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/coverage-white-space-search. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Coverage White Space Search?

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 Coverage White Space Search?

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

Is the Coverage White Space Search AI skill free?

Yes. It is published on GitHub by yogsoth-ai 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

coverage-white-space-search

Purpose

Input contract

Execution protocol

Output contract

Thresholds and quality gates

Failure and counterexamples

Provenance map

Preserved source criteria ledger

Context checkpoint / Delta notes

Frequently asked questions

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