القائمة
Quick surprise retention check. A 30-second mini-quiz to test if a concept is still in memory. Use for concepts with high stability that haven't been tested recently.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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 | delayed-probe |
| description | Quick surprise retention check. A 30-second mini-quiz to test if a concept is still in memory. Use for concepts with high stability that haven't been tested recently. |
| user-invocable | false |
A minimal, non-intrusive retention check. Takes 30 seconds max.
Pick a concept from the recommendations (one that the API suggests for delayed_probe)
Present ONE question, framed as a casual check:
Quick check — You learned about [concept] [X days ago]. Your predicted recall is [Y%].
[One clear question about the concept]
Accept the answer. Grade it.
Record via the probes endpoint:
curl -s -X POST -H "x-api-key: $TOKEN" -H "Content-Type: application/json" \
https://cognition-api.fly.dev/v1/probes/outcome \
-d '{
"probe_id": "probe-<uuid>",
"concept_id": "<concept_id>",
"horizon_days": <days_since_learned>,
"predicted_recall": <predicted_R>,
"result_correct": <true/false>,
"result_score": <0.0-1.0>,
"result_confidence": <user_confidence>
}'
Choose based on concept type: