Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
Adaptivetest Skill
Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations
skill openclawclawhub Free
Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations
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Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- CHANGELOG.md, CLAUDE.md, README.md, SKILL.md, clawhub.json, references/adaptive-testing.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Then review README.md for any prerequisites, environment setup, or post-install checks. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Then review README.md for any prerequisites, environment setup, or post-install checks. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.1
Provenance
- Publisher
- woodstocksoftware
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
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
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
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
5 Docs1 Config
- SKILL.md
- CHANGELOG.md
- CLAUDE.md
- README.md
- references/adaptive-testing.md
- clawhub.json