Token导航 LogoToken导航TokenDH.com
开发敏感数据clawhub未标认证来源可访问clear审计通过

adaptivetest-skill适应性测试技能

Agent Skill

用于辅助测试设计、自动化测试、用例整理和回归验证。它适合让 Agent 编写单元测试、端到端测试、测试计划或根据失败日志定位问题。使用时需要确认项目测试框架、运行命令和夹具数据,避免为了通过测试而改坏真实逻辑;涉及浏览器或外部服务时,应区分本地模拟、测试环境和生产环境。

总安装

11,429

周安装

481

GitHub Stars

公开资料未说明

下载量

4,002
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:adaptivetest-skill(适应性测试技能)
来源仓库:https://github.com/woodstocksoftware/adaptivetest-skill
安装命令:
openclaw skills install adaptivetest-skill
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install adaptivetest-skill

简介

提供自适应测试引擎,支持 IRT/CAT 与 AI 题目生成。

  • 适用于 OpenClaw 中辅助测试设计、自动化验证和回归检查。
  • 可生成个性化测试用例并提供学习建议。adaptivetest-skill 属于开发类 Skill,可作为该场景下的辅助能力补充。
  • 需确认项目测试框架兼容性,避免误改真实逻辑。
  • 涉及浏览器或外部服务时应区分环境与模拟方式。

SKILL.md

name
adaptivetest
description
Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations
version
1.0.1
author
woodstocksoftware
metadata
openclaw
requires
env
bins
capabilities
base_url
https://adaptivetest-platform-production.up.railway.app/api
tags

AdaptiveTest

Production-grade adaptive testing API. Uses Item Response Theory (IRT 2PL/3PL) with Computerized Adaptive Testing (CAT) to deliver precise ability estimates in fewer questions. Includes AI-powered question generation and personalized learning recommendations.

When to Use This Skill

Use AdaptiveTest when the user needs to:

  • Create or manage assessments and tests
  • Run adaptive testing sessions that select questions based on student ability
  • Generate assessment questions by topic, difficulty, or academic standard
  • Get personalized learning recommendations for students
  • Calibrate test items using IRT parameter estimation
  • Manage students, classes, and enrollments
  • Analyze test results and track student mastery

Authentication

All requests require the X-API-Key header:

X-API-Key: ${ADAPTIVETEST_API_KEY}

Base URL: https://adaptivetest-platform-production.up.railway.app/api

Core Workflows

1. Create and Administer an Adaptive Test

POST /tests              -- Create a test (set cat_enabled: true)
POST /tests/{id}/items   -- Add items to the test
POST /tests/{id}/sessions -- Start an adaptive session for a student
GET  /sessions/{id}/next-item -- Get the next CAT-selected item
POST /sessions/{id}/responses -- Submit student response
GET  /sessions/{id}/results   -- Get ability estimate and results

The CAT engine selects items using maximum Fisher information. Ability is estimated after each response using IRT 2PL or 3PL models. Sessions terminate when the standard error drops below threshold or max items are reached.

2. Generate Questions with AI

POST /gen-q -- Generate questions by topic, difficulty, and standard

Request body:

{
  "topic": "Quadratic equations",
  "difficulty": "medium",
  "count": 5,
  "standard": "CCSS.MATH.CONTENT.HSA.REI.B.4",
  "format": "multiple_choice"
}

Returns QTI 3.0-compatible items with stems, distractors, and rationales. Generation takes ~7 seconds.

3. Get Learning Recommendations

POST /recs -- Get personalized learning recommendations for a student

Request body:

{
  "student_id": "student-uuid",
  "subject": "Mathematics",
  "include_resources": true
}

Returns a personalized learning plan based on the student's ability profile and assessment history. Generation takes ~25 seconds.

4. Calibrate Test Items

POST /tests/{id}/calibrate -- Run IRT calibration on collected response data

Requires sufficient response data (minimum 30 responses per item recommended). Returns IRT parameters: difficulty (b), discrimination (a), and guessing (c) for 3PL.

5. Manage Students and Classes

POST /students           -- Create a student
GET  /students           -- List students
POST /classes            -- Create a class
POST /classes/{id}/enroll -- Enroll students in a class

OneRoster 1.2 compatible for SIS integration.

6. View Results and Analytics

GET /sessions/{id}/results       -- Detailed session results with ability estimate
GET /students/{id}/history       -- Assessment history for a student
GET /tests/{id}/analytics        -- Item-level analytics for a test

Rate Limits

Rate limits depend on your API key tier. Check X-RateLimit-Remaining header on each response.

Error Handling

All errors return JSON with a detail field:

{"detail": "Human-readable error message"}

Common status codes: 400 (validation), 401 (auth), 403 (limit exceeded), 404 (not found), 429 (rate limited).

Reference Documentation

For detailed endpoint specifications, request/response shapes, and IRT/CAT concepts, see the references/ directory:

  • references/api-endpoints.md -- Full endpoint reference
  • references/adaptive-testing.md -- IRT and CAT concepts
  • references/calibration.md -- Item calibration guide

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

87.41%
按下载量换算3,498

安全审计

VirusTotal

通过

ClawScan

通过

Static analysis

未展示

权限和风险

敏感数据

该 Skill 可能接触密钥、Token、环境变量或敏感配置,应进入高风险复核队列,默认不自动发布。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

继续浏览同类 Skills