菜单
Sketched Isotropic Gaussian Regularization primitive. Scalar loss matching the embedding distribution to a standard-normal target via Cramér-Wold slicing and the Epps-Pulley empirical characteristic function test. Port of rbalestr-lab/lejepa (MIT). Default-off in v1.49.571.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/Tibsfox/gsd-skill-creator --skill sigreg命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
Use the moment a request is underspecified and you are about to act on an ASSUMED goal. Enumerate the candidate goals the request could mean, project the action or artifact each one implies, and check whether they diverge: if two or more plausible goals lead to materially different actions or artifacts, ask exactly ONE targeted clarifying question; otherwise proceed on the most-likely goal and state the assumption in one line. Clarification is an evidence-producing action, not a delay — but one question, never an interrogation. Distinct from gsd-spec-phase (GSD-phase-bound, emits a heavy SPEC.md) and intent-router (fetch strategy, not goal disambiguation). Backed by agent goal-state inference (arxiv 2606.16813v1). Triggers on acting under an assumed goal when the request admits more than one materially different reading.
Use at write time to vector/embedding memory — Grove content-addressed insertion, chroma/pgvector upserts, memory-consolidation promoting session traces to MEMORY.md, or embedding externally-ingested content. Scores each candidate record against a fixed panel of sentinel queries and quarantines any record that would become the nearest neighbor of too many unrelated queries — a hub — whether from adversarial poisoning or accidental over-generality. This is the memory-record-side sibling of skill-injection-guardian (file-side) and the write-side complement of memory-use-warrant (read-side). Quarantine, never silently drop; a human reviews. Backed by the admission-time hubness gate (arxiv 2606.19692v1). Triggers on inserting into vector memory, consolidating memory, and embedding stranger content.
Run this appropriateness check the moment you are about to integrate a retrieved long-term memory — a Grove content-addressed hit, a chroma/pgvector neighbour, a memory-consolidation digest, or a MEMORY.md line — into a response, especially anything touching private origins, Fox Companies IP, credentials, or Center Camp / consent-governed content. It answers a question intent-router never asks: not WHAT to fetch or HOW, but WHETHER a correctly-retrieved item should reach output. Relevance is not appropriateness — a perfect similarity match can still be a boundary violation. Default is FAIL-CLOSED: if in-context authorization is uncertain, the memory may inform behaviour but must not be surfaced. Backed by RBI-Eval (arxiv 2606.06055v1). Triggers on surfacing recalled sensitive memory into a response.
正在显示 SKILL.md
基于 SOC 职业分类
| name | sigreg |
| description | Sketched Isotropic Gaussian Regularization primitive. Scalar loss matching the embedding distribution to a standard-normal target via Cramér-Wold slicing and the Epps-Pulley empirical characteristic function test. Port of rbalestr-lab/lejepa (MIT). Default-off in v1.49.571. |
| version | 1.49.571 |
| tier | T1 |
| phase | 729 |
| source_paper | arXiv:2511.08544v3 |
| license_attribution | rbalestr-lab/lejepa (MIT) |
Port of SIGReg from Balestriero & LeCun (2025, LeJEPA) arXiv:2511.08544v3. The primitive computes a scalar loss measuring how far an embedding distribution is from the standard-normal target, using Cramér-Wold slicing plus the Epps-Pulley empirical characteristic function test. Linear O(N·M·K) time, naturally differentiable, multi-GPU friendly (all_reduce over ECF averages).
import { sigreg } from './src/sigreg/index.js';
const loss = sigreg(embeddings); // embeddings: number[num_samples][num_dims]
// scalar loss; use as L_total = L_pred + λ · loss
For telemetry:
import { sigregWithBreakdown } from './src/sigreg/index.js';
const { loss, perSliceStatistic, maxSliceStatistic, runTag } = sigregWithBreakdown(embeddings);
Default matches the LeJEPA reference implementation:
const LEJEPA_DEFAULT_CONFIG = {
numSlices: 1024,
univariateTest: { numPoints: 17, sigma: 1.0 },
};
Default-off. Opt-in via .claude/gsd-skill-creator.json:
{
"heuristics-free-skill-space": {
"sigreg": { "enabled": true }
}
}
Ported from https://github.com/rbalestr-lab/lejepa under the MIT license.
© Randall Balestriero and LeJEPA Contributors. See ../../license_notices.md.
src/skill-isotropy/ — Skill Space Isotropy Audit (Phase 728) — read-only audit use case