progressive-memory
# Progressive Memory
What this skill does
# Progressive Memory Token-efficient memory system for AI agents. Scan an index first, fetch details on demand. Based on progressive disclosure principles from claude-mem. ## The Problem Traditional memory dumps everything into context: - Load 3500 tokens of history - 94% is irrelevant to current task - Wastes attention budget, causes context rot ## The Solution **Progressive disclosure:** Show what exists first, let the agent decide what to fetch. ``` Before: 3500 tokens loaded โ 200 relevant (6%) After: 100 token index โ fetch 200 needed (100%) ``` ## Memory Format ### Daily Files (`memory/YYYY-MM-DD.md`) ```markdown # 2026-02-01 (AgentName) ## Index (~70 tokens to scan) | # | Type | Summary | ~Tok | |---|------|---------|------| | 1 | ๐ด | Auth bug - use browser not CLI | 80 | | 2 | ๐ข | Deployed SEO fixes to 5 pages | 120 | | 3 | ๐ค | Decided to split content by account | 60 | --- ### #1 | ๐ด Auth Bug | ~80 tokens **Context:** Publishing via CLI **Issue:** "Unauthorized" even with fresh tokens **Workaround:** Use browser import instead **Status:** Unresolved ``` ### Long-Term Memory (`MEMORY.md`) ```markdown ## ๐ Index (~100 tokens) | ID | Type | Category | Summary | ~Tok | |----|------|----------|---------|------| | R1 | ๐จ | Rules | Twitter posting protocol | 150 | | G1 | ๐ด | Gotcha | CLI auth broken | 60 | | D1 | ๐ค | Decision | Content split by account | 60 | --- ### R1 | Twitter Posting Protocol | ~150 tokens - POST ALL tweets in ONE session - NEVER post hook without full thread - VERIFY everything before reporting done ``` ## Observation Types | Icon | Type | When to Use | |------|------|-------------| | ๐จ | rule | Critical rule, must follow | | ๐ด | gotcha | Pitfall, don't repeat this | | ๐ก | fix | Bug fix, workaround | | ๐ต | how | Technical explanation | | ๐ข | change | What changed, deployed | | ๐ฃ | discovery | Learning, insight | | ๐ | why | Design rationale | | ๐ค | decision | Architecture decision | | โ๏ธ | tradeoff | Deliberate compromise | ## Token Estimation | Content Type | Tokens | |--------------|--------| | Simple fact | ~30-50 | | Short explanation | ~80-150 | | Detailed context | ~200-400 | | Full summary | ~500-1000 | ## How It Works 1. **Session starts** โ Agent scans index tables (~100-200 tokens) 2. **Agent sees types** โ Prioritizes ๐ด gotchas over ๐ข changes 3. **Agent sees costs** โ Decides if 400-token entry is worth it 4. **Fetch on demand** โ Only load what's relevant to current task ## Benefits - **Token savings:** ~65,000 tokens/day with 20 memory checks - **Faster scanning:** Icons enable visual pattern recognition - **Precise references:** IDs like #1, G3, D5 for exact lookup - **Cost awareness:** Token counts for ROI decisions ## Integration Works with any markdown-based memory system. No database required. For Clawdbot users: 1. Update `AGENTS.md` with format instructions 2. Restructure `MEMORY.md` with index 3. Use format in daily `memory/YYYY-MM-DD.md` files --- **Built by [LXGIC Studios](https://lxgicstudios.com)** ๐ [GitHub](https://github.com/lxgicstudios/progressive-memory) ยท [Twitter](https://x.com/lxgicstudios)
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