name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token,保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
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Adaptive testing engine with IRT/CAT, AI question generation, and personalized learning recommendations
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
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.
Use AdaptiveTest when the user needs to:
All requests require the X-API-Key header:
X-API-Key: ${ADAPTIVETEST_API_KEY}
Base URL: https://adaptivetest-platform-production.up.railway.app/api
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.
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.
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.
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.
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.
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 depend on your API key tier. Check X-RateLimit-Remaining header on each response.
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).
For detailed endpoint specifications, request/response shapes, and IRT/CAT concepts, see the references/ directory:
references/api-endpoints.md -- Full endpoint referencereferences/adaptive-testing.md -- IRT and CAT conceptsreferences/calibration.md -- Item calibration guide아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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name: 4d-compression-core version: 1.0.2 description: "把长内容压缩成结构化向量——节省 60-80% Token,保留核心信息" metadata: { "openclaw": { "emoji": "🌀", "requires": { "bins": ["jq", "awk"] }, "triggers": ["压缩", "4d",...
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Use cheap, TEE-verified AI models from the 0G Compute Network as OpenClaw providers. Discover available models and compare pricing vs OpenRouter, verify provider integrity via hardware attestation (Intel TDX), manage your 0G wallet and sub-accounts, and configure models in OpenClaw with one workflow. Supports DeepSeek, GLM-5, Qwen, and other models available on the 0G marketplace.
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Send and receive P2P messages using disposable numbers and PINs. No servers, no accounts. Use for human notifications, approval flows, and agent-to-agent communication.
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Query historical crypto market data from 0xArchive across Hyperliquid, Lighter.xyz, and HIP-3. Covers orderbooks, trades, candles, funding rates, open interest, liquidations, and data quality. Use when the user asks about crypto market data, orderbooks, trades, funding rates, or historical prices on Hyperliquid, Lighter.xyz, or HIP-3.
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Find and complete paid tasks on the 0xWork decentralized marketplace (Base chain, USDC escrow). Use when: the agent wants to earn money/USDC by doing work, discover available tasks, claim a bounty, submit deliverables, check earnings or wallet balance, or set up as a 0xWork worker. Task categories: Writing, Research, Social, Creative, Code, Data. NOT for: posting tasks (use the website), managing the 0xWork platform, or frontend development.
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Patterns and practices that dramatically accelerate development velocity. Covers parallel execution, automation, feedback loops, workflow optimization, and anti-pattern avoidance. Use when starting projects, planning sprints, optimizing workflows, or onboarding developers.
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