Anchor Prior Test

Organization

LLM-Coding

anchor-prior-test

Empirically test whether a candidate term qualifies as a semantic anchor by probing how densely it sits in LLM training data — across several model tiers in a clean room — before proposing it. Use when triaging an anchor proposal, deciding anchor vs contract, or vetting a rename. Produces a verdict against the four criteria (Precise, Rich, Consistent, Attributable), a tier rating, and a ready-to-paste proposal or rejection.

Overview

Publisher	LLM-Coding
Repository	`Semantic-Anchors`
Skill name	`anchor-prior-test`
Stars	521
Forks	45
Bundled files	4
License	MIT
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.
4 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by LLM-Coding on GitHub. Read the source before you install it.

Installation

Install the Anchor Prior Test 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/LLM-Coding/Semantic-Anchors/tree/main/plugins/semantic-anchors/skills/anchor-prior-test
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/LLM-Coding/Semantic-Anchors/tree/main/plugins/semantic-anchors/skills/anchor-prior-test

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/LLM-Coding/Semantic-Anchors.git /tmp/Semantic-Anchors
mkdir -p .claude/skills
cp -r /tmp/Semantic-Anchors/plugins/semantic-anchors/skills/anchor-prior-test .claude/skills/anchor-prior-test

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

Use it in TypingMind

Enable Anchor Prior Test 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 Anchor Prior Test 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 Anchor Prior Test 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.

Anchor Prior Test

Measure whether a term is a strong semantic anchor instead of guessing. This skill turns the manual litmus test in CONTRIBUTING.adoc ("ask the LLM what it associates") into a rigorous, multi-model, clean-room procedure with a structured verdict.

The principle

A semantic anchor only delivers leverage if the term is already a dense, pre-computed prior in the model's training data. Naming it must reliably trigger the rich concept, the same way for everyone, across models you do not control. This skill measures that density empirically. The reasoning behind it is the article An Anchor Delivers Only as Far as the Prior Reaches (route /training-data-vs-practice).

Two facts drive the whole method:

Power tracks density, not merit. A good, recent, niche method (e.g. "Use-Case 3.0") can be a weak anchor; a model will silently substitute the nearest concept it holds rather than admit the gap. Density is what you measure.
A weak prior is not a dead end. It is a candidate for a contract (which supplies its own meaning in text) instead of an anchor. The verdict routes the term to the right home.

When to use

Triaging a [Anchor Proposal] issue before accepting it.
Deciding anchor vs contract for new vocabulary.
Vetting a rename — does the new name trigger the same concept the body describes?

Procedure

Frame the candidate. Write down the exact string a user would type. List the precise/qualified form and any ambiguous bare form (e.g. "Morphological Box / Zwicky Box" vs bare "morphological analysis"). Note rival terms a model might confuse it with.
Open a clean room. Run a fresh claude -p process — never a sub-agent (sub-agents inherit this project's CLAUDE.md and memory and will give circular results). See references/clean-room.md.
Run the probe battery. Four probe types across at least two model tiers (weak + strong), with at least two runs of the decisive probe. See references/probe-battery.md.
Score. Map results to the four criteria, prior density, and a tier (★). See references/scoring.md.
Emit. A verdict plus either a ready-to-post verdict comment for the proposal issue, or a rejected-proposals.adoc entry with the reason.

Critical gotchas — do not skip

Sub-agents are contaminated. They inherit the session's CLAUDE.md + memory snapshot. Use a fresh claude -p with --setting-sources "" from a neutral directory. A running session freezes that context at start-up, so moving files mid-session does not help.
Recall ≠ execution. Test both "what is X?" (knowledge) and "use X to do Y" (the anchor as instruction). A model can fail to explain a method yet still apply its core move when the move has a dense name.
Name ambiguity. Always probe the bare term too. If a strong model defaults to a different field (e.g. "morphological analysis" → linguistics), the catalog name must carry a disambiguator.
n=1 is not evidence. Run the decisive probe (recognition and the action test) at least twice per model; dramatic single-run behaviour (hedging, mislabelling, substitution) must reproduce before you cite it.

Output

A short verdict table (criteria × model), a tier (★★★ / ★★☆ / ★☆☆), a route (anchor / contract / reject), and the filled-in proposal or rejection text. See references/worked-example.md for a complete run (the Morphological Box).

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 Anchor Prior Test AI skill do?

Why use Anchor Prior Test on TypingMind?

Because you install it once and use it with any model. Anchor Prior Test 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 Anchor Prior Test in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/LLM-Coding/Semantic-Anchors/tree/main/plugins/semantic-anchors/skills/anchor-prior-test. 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 Anchor Prior Test?

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 Anchor Prior Test?

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

Is the Anchor Prior Test AI skill free?

Yes. It is published on GitHub by LLM-Coding under the MIT 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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Autoreview

Structured code review when explicitly requested, preferring OpenAI/Codex before Claude.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Anchor Prior Test

The principle

When to use

Procedure

Critical gotchas — do not skip

Output

Bundled files

Frequently asked questions

Related AI Skills

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