AI Newsletter Digest improvements: fixed QP soft line break decoding, URL extraction, and content cleaning

skills/progressive-memory/SKILL.md

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+# 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 → 200 relevant (6%)
+After:  100 token index → 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 | 🔴 | Auth bug - use browser not CLI | 80 |
+| 2 | 🟢 | Deployed SEO fixes to 5 pages | 120 |
+| 3 | 🟤 | Decided to split content by account | 60 |
+
+---
+
+### #1 | 🔴 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
+## 📋 Index (~100 tokens)
+| ID | Type | Category | Summary | ~Tok |
+|----|------|----------|---------|------|
+| R1 | 🚨 | Rules | Twitter posting protocol | 150 |
+| G1 | 🔴 | Gotcha | CLI auth broken | 60 |
+| D1 | 🟤 | 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 |
+|------|------|-------------|
+| 🚨 | rule | Critical rule, must follow |
+| 🔴 | gotcha | Pitfall, don't repeat this |
+| 🟡 | fix | Bug fix, workaround |
+| 🔵 | how | Technical explanation |
+| 🟢 | change | What changed, deployed |
+| 🟣 | discovery | Learning, insight |
+| 🟠 | why | Design rationale |
+| 🟤 | decision | Architecture decision |
+| ⚖️ | 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** → Agent scans index tables (~100-200 tokens)
+2. **Agent sees types** → Prioritizes 🔴 gotchas over 🟢 changes
+3. **Agent sees costs** → Decides if 400-token entry is worth it
+4. **Fetch on demand** → 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)**
+
+🔗 [GitHub](https://github.com/lxgicstudios/progressive-memory) · [Twitter](https://x.com/lxgicstudios)