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Run a full study session — observe, predict, and practice in one flow
Installer avec Codex ou Claude Copiez ce prompt, collez-le dans Codex, Claude ou un autre assistant, puis laissez-le vérifier la page du skill et l'installer pour vous.
Basé sur la classification professionnelle SOC
Connect a notification channel (Slack, Gmail, etc.) so Cognition can send you learning exercises throughout the day — even when your laptop is off.
Schedule learning exercises for the next 24 hours via Slack/Gmail. Runs at the end of every session so exercises arrive even when Claude Code is closed. The API decides WHEN and WHAT technique — you build the exercise and schedule delivery.
Send screen observations to the Cognition API as learning events. Transforms Screenpipe data into API event format.
Launch the Cognition GUI app. Opens the Electron window and connects Claude Code to it via WebSocket. Use this after /cognition:start or whenever you need the GUI.
Generate and deliver a learning exercise based on what the user is forgetting. Orchestrates different learning techniques.
Test if the user's confidence matches their actual knowledge. Use when calibration ECE is high or strategy is "overconfident".
| name | session |
| description | Run a full study session — observe, predict, and practice in one flow |
| disable-model-invocation | true |
Run a complete learning loop.
Ensure Screenpipe is running. If not, start it:
curl -s http://localhost:3030/health 2>/dev/null || (npx screenpipe@latest record > /dev/null 2>&1 & sleep 5)
Use the screenpipe MCP tools to check what the user has been doing in the last hour. Summarize the learning-relevant content.
For each learning-relevant observation, POST a screen_capture event to the Cognition API:
TOKEN=$(cat ~/.cognition/token)
curl -s -X POST -H "x-api-key: $TOKEN" -H "Content-Type: application/json" \
https://cognition-api.fly.dev/v1/events \
-d '{ "event": { ... screen_capture event ... } }'
Use the event schema from llms.txt. Generate proper UUIDs for event_id and idempotency_key.
Fetch recommendations and learner state. Show the user what's decaying.
Get the world model plan for optimal intervention:
curl -s -X POST -H "x-api-key: $TOKEN" \
"https://cognition-api.fly.dev/v1/world-model/plan?user_id=USER_ID"
Check the interleaving plan for concept pairing:
curl -s -H "x-api-key: $TOKEN" \
"https://cognition-api.fly.dev/v1/interleaving/plan?user_id=USER_ID"
Based on recommendations, run 3-5 exercises:
For each question:
After all exercises, show:
Session Complete!
━━━━━━━━━━━━━━━━
Questions: 5
Correct: 4/5 (80%)
Concepts strengthened: 3
• useEffect cleanup: 43% → 68% (+25%)
• SQL JOINs: 52% → 52% (missed — review again tomorrow)
• Git rebase: 61% → 78% (+17%)
Next session recommended: tomorrow at 2pm
Record a session_event (type: "end") to close the session.