Menu

Individual skill

embedding-anisotropy-check

Use before reporting any cosine similarity computed over embeddings or transformer hidden states, whether the agent computed the vectors or loaded them. Measures the anisotropy of each side, centres by the population mean, and scores matched pairs against a permuted floor taken from the same comparison. Use when comparing one model's embeddings across precisions, runtimes or layers, when evaluating retrieval, probes or adapters, or before calling a similarity high, two conditions in agreement, or a small gap a null.

skills/embedding-anisotropy-check/SKILL.md

Read pinned source

Source commit edbecfa9099c4addae1f500cca1ac067204b30fd

What was checked

Matches the observed repository HEAD
Yes
Scanner rules are current
Yes
Repository scan complete
No
Skill scan complete
No
Observed behavior
Network or broad shell
Last checked
Nov 4, 2026

These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.

Install this skill

Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.

An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.

Repository context

Repository stars (not skill ratings)
40,901
Repository last updated
Nov 6, 2026
License
MIT

Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.

Files and excerpt

File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.

Browse files
Read excerpt

# Embedding anisotropy check

Raw cosine between embeddings is rarely centred on zero. Most sentence encoders and most hidden-state layers place
every vector near one shared direction, so two unrelated inputs can score 0.6, 0.9 or 0.99. A matched pair at 0.9 is
then not evidence of anything by itself, and a gap of 0.01 between two conditions can be the whole signal or none of
it. This skill makes every such comparison carry its own floor.

## How an agent reaches this skill

The agent does not vectorise anything here. The skill applies at the point where the agent is about to report a cosine
number, whoever produced the vectors:

- **The agent wrote the evaluation.** A request such as "write a retrieval eval for our index" or "check that the 4-bit
  export of this encoder still matches bf16" ends in a script that prints a mean cosine. Before the agent says