Proactive Memory
SkillDocs & knowledgeTurn repeated failures, corrections, and recurring requests into durable follow-up work.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the Proactive Memory skill
About this skill
Self-hosted AI workspace where chat becomes visual workflows, multi-agent operations, and reviewable automations. Local memory; local or cloud models
What this skill tells your AI
The instructions your AI receives, as published by aaronnat23/disp8ch in skills/proactive-memory/SKILL.md and read by ahel’s review.
Turn repeated failures, corrections, and recurring requests into durable follow-up work.
- When an agent hits the same failure twice, create a short memory note with the trigger, failed approach, corrected approach, and verification result.
- Convert important corrections into board tasks or scheduled follow-ups instead of leaving them only in chat history.
- After a user changes a preference, record the new preference in memory and mention the update in the next relevant run.
- Pair with scheduler for check-ins such as "retest this flow tomorrow" or "review this integration every Monday".
- Pair with hierarchy goals when the lesson belongs to a team, project, or organization rather than one agent.
- Keep entries concise: problem, correction, evidence, next trigger.
- Prefer updating an existing memory cluster over creating duplicate notes for the same issue.
- If a correction changes a standard operating pattern, recommend enabling or updating the matching skill pack.
Signals
- GitHub stars
- 100
- Forks
- 2
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
proactive-memory- Source
- github.com/aaronnat23/disp8ch