Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Developer Tools
openlesson
Interact with the openLesson tutoring API to generate learning plans, start audio-based sessions, analyze reasoning gaps, and manage tutoring workflows.
skill openclawclawhub Free
Interact with the openLesson tutoring API to generate learning plans, start audio-based sessions, analyze reasoning gaps, and manage tutoring workflows.
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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
- requires.env, skill.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. 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. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.2
Provenance
- Publisher
- dncolomer
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
openLesson Agent API Skill
You are an AI agent that can interact with the openLesson tutoring platform via API.
Overview
openLesson is a tutoring system that uses audio-based dialogue to help users learn by asking questions rather than giving answers. The platform generates personalized learning plans as directed graphs, where each node is a session. Agents can programmatically generate learning plans, start sessions, and analyze audio chunks for reasoning gaps.
Important: No Browser Tool Required
You do not need a browser tool. You only need shell tools (e.g., curl) to make API calls to openLesson.
Important: Audio-Only System
CRITICAL: The openLesson platform is audio-only. The analyze endpoint accepts ONLY audio input, NOT text. Always convert speech to base64-encoded audio before calling the analyze endpoint Supported formats: webm, mp4, ogg Do not send text to the analyze endpoint - it will be rejected
Authentication
Include your API key in the Authorization header: Authorization: Bearer YOUR_API_KEY Important: Always use https://www.openlesson.academy for API calls. The domain openlesson.academy has a redirect that loses the Authorization header. API keys can be generated from the user's dashboard at /dashboard.
Credentials
This skill requires an API key for the openLesson API: Environment variable: OPENLESSON_API_KEY How to obtain: Generate from the user's dashboard at /dashboard No calendar access needed: The skill does NOT create actual calendar events. "Reminders" means the agent proactively notifies the human when a session is due โ this is behavioral, not a technical integration.
Session State
Session IDs are stored in-memory for the duration of the conversation. No persistent storage is used or required.
Bash Command Patterns
When running API calls as shell commands, use this pattern to avoid JSON escaping issues:
Basic POST with JSON body
bash -c 'printf "{\"topic\":\"Quantum Computing\",\"days\":60}" | curl -X POST "https://www.openlesson.academy/api/agent/plan" -H "Authorization: Bearer $OPENLESSON_API_KEY" -H "Content-Type: application/json" --data-binary @-'
With variables
TOPIC="Quantum Computing" DAYS=60 bash -c "printf '{\"topic\":\"$TOPIC\",\"days\":$DAYS}' | curl -X POST 'https://www.openlesson.academy/api/agent/plan' -H 'Authorization: Bearer $OPENLESSON_API_KEY' -H 'Content-Type: application/json' --data-binary @-"
Start session
bash -c 'printf "{\"plan_node_id\":\"NODE_UUID\",\"problem\":\"Explain neural networks\"}" | curl -X POST "https://www.openlesson.academy/api/agent/session/start" -H "Authorization: Bearer $OPENLESSON_API_KEY" -H "Content-Type: application/json" --data-binary @-'
Analyze audio
bash -c 'printf "{\"session_id\":\"SESSION_UUID\",\"audio_base64\":\"BASE64_DATA\",\"audio_format\":\"webm\"}" | curl -X POST "https://www.openlesson.academy/api/agent/session/analyze" -H "Authorization: Bearer $OPENLESSON_API_KEY" -H "Content-Type: application/json" --data-binary @-'
1. Generate Learning Plan
Creates a directed graph of learning sessions for a given topic. Endpoint: POST /api/agent/plan Request: { "topic": "Machine Learning Fundamentals", "days": 30 // optional: number of days to spread the plan across (default: 30) } Response: { "planId": "uuid", "topic": "Machine Learning Fundamentals", "days": 30, "nodes": [ { "id": "uuid", "title": "Introduction to ML", "description": "Basic concepts and overview", "is_start": true, "next_node_ids": ["uuid2"], "status": "available" } ] } Days to Sessions: 7 days: 3-5 sessions 14 days: 4-7 sessions 30 days (default): 5-10 sessions 60 days: 8-14 sessions 90 days: 10-18 sessions 180 days: 15-25 sessions
2. Start Session
Starts a new Socratic session. Endpoint: POST /api/agent/session/start Request: { "problem": "Explain how gradient descent works in neural networks", "plan_node_id": "uuid-from-plan" // optional, links to plan node } Response: { "sessionId": "uuid", "problem": "Explain how gradient descent works...", "nodeTitle": "Gradient Descent", "planId": "uuid", "status": "active", "instructions": { "audioFormat": "webm", "submitEndpoint": "/api/agent/session/analyze", "maxChunkDuration": 60000 } }
3. Analyze Audio Chunk
Submits an audio chunk for Socratic analysis. Returns reasoning gap score and follow-up questions. Endpoint: POST /api/agent/session/analyze Request: { "session_id": "uuid-from-start", "audio_base64": "base64-encoded-audio-data", "audio_format": "webm" } Response: { "sessionId": "uuid", "gapScore": 0.7, "signals": [ "Missing consideration of local minima", "No mention of learning rate impact" ], "transcript": "transcribed audio...", "followUpQuestion": "What happens when the gradient becomes very small?", "requiresFollowUp": true }
4. End Session
Ends an agent session and generates a summary report. Endpoint: POST /api/agent/session/end Request: { "session_id": "uuid-from-start" } Response: { "success": true, "sessionId": "uuid", "message": "Session ended and report generated", "chunkCount": 5, "wordCount": 1200 }
5. Get Session Summary
Retrieves the summary report of a completed session. Endpoint: GET /api/agent/session/summary?session_id=xxx Response (if ready): { "ready": true, "sessionId": "uuid", "report": "# Session Report\n\n## Overview\n...", "createdAt": "2026-02-24T12:00:00Z", "status": "completed" } Response (if not ready): { "ready": false, "message": "Session report not ready yet. Call /session/end first to generate the report.", "sessionId": "uuid", "status": "active" }
Complete Agent Workflow
import base64 import requests API_KEY = "your_api_key" BASE_URL = "https://openlesson.academy" HEADERS = {"Authorization": f"Bearer {API_KEY}"} # Step 1: Generate a learning plan (optional: specify days) plan_response = requests.post( f"{BASE_URL}/api/agent/plan", json={ "topic": "Quantum Computing", "days": 14 # optional: number of days for the plan }, headers=HEADERS ) plan = plan_response.json() # Step 2: Start a session for the first node first_node = next(n for n in plan["nodes"] if n["is_start"]) session_response = requests.post( f"{BASE_URL}/api/agent/session/start", json={"plan_node_id": first_node["id"], "problem": first_node["title"]}, headers=HEADERS ) session = session_response.json() # Step 3: Record and analyze audio # 1. Record audio from user (use browser MediaRecorder or similar) # 2. Convert to base64 with open("audio.webm", "rb") as f: audio_base64 = base64.b64encode(f.read()).decode() analyze_response = requests.post( f"{BASE_URL}/api/agent/session/analyze", json={ "session_id": session["sessionId"], "audio_base64": audio_base64, "audio_format": "webm" }, headers=HEADERS ) analysis = analyze_response.json() print(f"Gap Score: {analysis['gapScore']}") print(f"Follow-up: {analysis['followUpQuestion']}")
Error Handling
401: Invalid or inactive API key 403: Session doesn't belong to this key or wrong endpoint 404: Session not found 500: Internal server error
Tips for Agents
Always use audio: The analyze endpoint ONLY accepts audio. Never send text. Record in webm format: This is the preferred format (Opus codec) Chunk audio: Send 30-60 second chunks for analysis Follow up: If requiresFollowUp is true, ask the follow-up question and record response Track gap scores: Use gap score to determine when user has mastered a concept Use bash -c for curl: When running API calls in shell scripts, use the bash -c 'printf "..." | curl ... --data-binary @-' pattern to avoid JSON escaping issues Schedule all sessions: When you generate a learning plan, it is important to schedule all the sessions suggested in the plan for your human. Create calendar events or reminders for each session so they can follow the plan. Always remind your human when a session is due - don't wait for them to ask.
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
1 Docs1 Files
- skill.md
- requires.env