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.
npx skills add LLM-Coding/Semantic-Anchors --skill anchor-prior-testgit clone --depth 1 https://github.com/LLM-Coding/Semantic-AnchorsWrote 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.
[](https://agentmods.dev/skills/llm-coding/semantic-anchors/anchor-prior-test)<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/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00977 |
| Opus 5.5 | $0.00039 | $0.00391 |
| Sonnet 5.5 | $0.00019 | $0.00195 |
| Haiku 4.5 | $0.00010 | $0.00098 |
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 9d 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.
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 -pprocess — never a sub-agent (sub-agents inherit this project'sCLAUDE.mdand memory and will give circular results). Seereferences/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.adocentry with the reason.
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.
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.
- 9d ago Changed 26ca9de46eaa
- 2mo ago First seen · 48 lines · 97 tokens per session scan A aaa37d08799a
anchor-prior-test is a skill published in the GitHub repository LLM-Coding/Semantic-Anchors (528 stars, last pushed today), licensed Apache-2.0. It adds 97 tokens to every session and 977 once invoked, about $0.0004 per session on Opus 5.5. 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.
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