What is anchor-prior-test?

A testing procedure for deciding whether a proposed term is a well-known concept to language models. A semantic anchor is a familiar term that reliably brings a larger shared idea to mind.

Use it to evaluate new anchor proposals, possible renames, and whether a term is understood consistently across several model tiers.

It replaces guesswork with evidence when deciding whether a term should be used as a shorthand anchor or explained fully in a contract.

anchor-prior-test — Skill for Claude Code by LLM-Coding

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent

npx skills add LLM-Coding/Semantic-Anchors --skill anchor-prior-test

Clone the repo

git clone --depth 1 https://github.com/LLM-Coding/Semantic-Anchors

Made for: Claude Code.

Or install semantic-anchors, the plugin that ships this one along with the rest of its 6 skills.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

README.md

[![agentmods](https://agentmods.dev/badge/skills/llm-coding/semantic-anchors/anchor-prior-test.svg)](https://agentmods.dev/skills/llm-coding/semantic-anchors/anchor-prior-test)

Your own site

<a href="https://agentmods.dev/skills/llm-coding/semantic-anchors/anchor-prior-test"><img src="https://agentmods.dev/badge/skills/llm-coding/semantic-anchors/anchor-prior-test.svg" alt="Measured on agentmods" height="20"></a>

Per session 97 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.

When invoked 981 The whole file, excluding the scripts and references it only reads on demand.

Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.

Origin original No closer match found in the catalogue.

Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

Model	Per session	Once invoked
Fable 5.1	$0.00097	$0.00981
Opus 5	$0.00048	$0.00491
Sonnet 5	$0.00019	$0.00196
Haiku 4.5	$0.00010	$0.00098

Measured 7d ago against content hash aaa37d08799a, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-07, from the pricing page.

Security

Grade A, and why

anchor-prior-test scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

plugins/semantic-anchors/skills/anchor-prior-test/SKILL.md · 48 lines

How it starts

The opening of the file, as written. The whole thing — 48 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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-paste propose-anchor.yml activation-test section, or a rejected-proposals.adoc entry with the reason.

Read the full file on GitHub · 48 lines

Files

What ships with it

4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

7d ago First seen · 48 lines · 97 tokens per session scan A aaa37d08799a

Subscribe to this mod's changes

anchor-prior-test is a skill published in the GitHub repository LLM-Coding/Semantic-Anchors (465 stars, last pushed 5d ago), licensed Apache-2.0. It adds 97 tokens to every session and 981 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.

Research Testing

research-engineer

An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.

davila7/claude-code-templates · 43 tokens

tika-eval-compare

Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".

apache/tika · 50 tokens

neuron-evaluation-engineer

Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…

neuron-core/neuron-ai · 77 tokens

anchor-prior-test