Build and Evaluate Adaptive Text Classifiers
Small labeled datasets and changing categories make text classification difficult to maintain. This skill guides incremental classifier updates and held-out evaluation with the adaptive-classifier library.
將此請求複製給你的智慧代理，其中包含權威 Skill 頁面和清單。
Review the Skillstore skill "adaptive-classifier" from https://skillstore.io/skills/lambdasec-adaptive-classifier.md and its manifest at https://skillstore.io/api/skills/lambdasec-adaptive-classifier/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.你的智慧代理仍應顯示其計畫，並依安全政策請求必要確認。
當 AI Agent、爬蟲或腳本需要乾淨脈絡、而不是讀取完整頁面時，請使用這些連結。
正在使用「adaptive-classifier」。 Plan a support intent classifier from a small labeled dataset without downloading weights yet.
預期結果:
正在使用「adaptive-classifier」。 Explain what to check after adding a new technical-support category.
預期結果:
All 32 static findings are false positives: 31 match Markdown backticks, and one matches a documentation URL. No evidence found of prompt injection, credential access, or data exfiltration in the reviewed files. Model downloads require approval, remote model code stays disabled, and model-dependent examples were not executed during this audit.
分享版本化評估報告、中性徽章、嵌入卡片與引用。Skillstore 只報告證據，不判斷該 Skill 是否安全。
https://skillstore.io/skills/lambdasec-adaptive-classifier/audits/1?utm_source=security_passport&utm_medium=share&utm_campaign=versioned_report[](https://skillstore.io/skills/lambdasec-adaptive-classifier?utm_source=security_passport_badge)<a href="https://skillstore.io/skills/lambdasec-adaptive-classifier?utm_source=security_passport_badge"><img src="https://skillstore.io/badges/skills/lambdasec-adaptive-classifier/security.svg" alt="Skillstore security assessment" loading="lazy"></a><iframe src="https://skillstore.io/embed/skills/lambdasec-adaptive-classifier.html" title="Skillstore Security Assessment" sandbox="allow-popups allow-popups-to-escape-sandbox" loading="lazy" referrerpolicy="no-referrer" width="420" height="180"></iframe>lambdasec. (2026). adaptive-classifier security audit report (audit version 1) [Author version unspecified]. Skillstore. https://skillstore.io/skills/lambdasec-adaptive-classifier/audits/1@techreport{lambdasec-lambdasec-adaptive-classifier-2026,
author = {lambdasec},
title = {adaptive-classifier security audit report (audit version 1)},
institution = {Skillstore},
year = {2026},
number = {1},
url = {https://skillstore.io/skills/lambdasec-adaptive-classifier/audits/1},
note = {Author version unspecified}
}cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "adaptive-classifier security audit report (audit version 1)"
version: "unspecified"
type: report
authors:
- name: "lambdasec"
date-released: "2026-10-28"
url: "https://skillstore.io/skills/lambdasec-adaptive-classifier/audits/1"
identifiers:
- type: other
value: "skillstore:lambdasec-adaptive-classifier:audit:1"
description: "Skillstore immutable audit report identifier"
Build a ticket classifier from labeled examples, add emerging intents, and reevaluate existing categories after updates.
Compare an adaptive classifier against simple baselines using held-out data, per-class metrics, and learning curves.
Explore multilabel predictions and strategic robustness for moderation queues while retaining human review for consequential decisions.
Assess whether adaptive-classifier fits my ticket-routing task with 20 labeled examples per class. Identify missing information and ask before downloading anything.
Use my labeled texts to draft a local intent classifier with held-out evaluation. Check the installed version and request approval before downloading weights.
Add my new billing examples to an existing classifier. Check label consistency and compare held-out performance for old and new classes.
Design validation-only comparisons for pooling, blending weights, ONNX inference, and strategic robustness. Include order sensitivity, calibration, and a final untouched test evaluation.
作者
lambdasec授權
Apache-2.0
Skillstore 修訂
r1
版本提示
作者未聲明版本。
引用
87c9669baf85385959f9b0a344dc9dfc8d978f9c
維護新鮮度
2026/10/28
使用量
0 次下載 · 0 次瀏覽
檔案結構
Analyze Local Model Reasoning with PTS
Reasoning traces do not show which events change a model's chance of solving a task. This skill guides PTS searches, event interpretation, and local dataset exports with explicit measurement caveats.
Scan and Triage Code Vulnerabilities with Frame
Security scan results need source review before teams can trust findings or apply fixes. This skill guides Frame scans, evidence-based triage, patch review, and verification.
Run Local Models on Apple Silicon with mlx-optiq
Local model serving requires matching model size, memory limits, and client settings. This skill guides mlx-optiq checks, consent-based server startup, client connections, and performance evaluation.
Evaluate and Improve Prompts With Test Cases
Prompt changes can look useful without improving real results. This skill scores examples, proposes revisions, and retains candidates that improve training scores without lowering validation scores.
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