AI Self-Improvement Digest

Daily curated digest focused on AI self-improvement — content that helps AI agents get better at their job.

Overview

This skill generates a daily digest covering:

• Harness & System Prompt Engineering — Structuring agent instructions
• Skill & Tool Development — New tools, MCP servers, integration patterns
• Self-Evaluation & Improvement — How agents assess and improve themselves
• Multi-Agent Coordination — Spawning, supervising, merging work
• Memory & Context Management — RAG, long-term memory, compaction
• Workflow Automation — Task decomposition, failure handling
• Foundational Research — Academic work on agent capabilities

Quick Start

1. Install the Skill

# Clone to your OpenClaw skills directory
git clone https://git.terraphim.cloud/kimie05c34be198a20b9/ai-self-improvement-digest.git

# Or add as submodule
git submodule add https://git.terraphim.cloud/kimie05c34be198a20b9/ai-self-improvement-digest.git skills/ai-self-improvement-digest

2. Create Tracking File

mkdir -p memory
cat > memory/ai-digest-posted.json << 'EOF'
{
  "posted": [],
  "experiments": [],
  "skillsEvaluated": [],
  "setupChanges": []
}
EOF

3. Set Up Cron Job

openclaw cron add \
  --name ai-self-improvement-digest \
  --schedule "30 8 * * *" \
  --tz "Asia/Shanghai" \
  --message "Generate today's AI Self-Improvement Digest following the workflow in the ai-self-improvement-digest skill. Read memory/ai-digest-posted.json first for deduplication."

Daily Workflow

1. Deduplication — Read memory/ai-digest-posted.json, skip already posted
2. Scan Sources — Check Tier 1-3 sources for last 24-72h content
3. Filter — Only include self-improvement relevant items
4. Format — 3-5 items with summary, relevance, takeaway
5. Experiment — Suggest one small experiment to try
6. Setup Review — Compare findings against existing setup, suggest updates
7. Track — Append to memory/ai-digest-posted.json

Sources

Tier	Sources	Frequency
1	Anthropic Engineering, Simon Willison, Geoff Huntley, Hacker News, Lilian Weng	Daily
2	Latent Space, Cursor Blog, Eugene Yan, Chip Huyen, Mitchell Hashimoto	2-3x/week
3	arXiv cs.CL/cs.AI, GitHub Trending	Weekly

Output Format

🧠 AI Self-Improvement Digest — 2026-02-26

**[Article Title]** — Source
What: One-sentence summary
Why it matters: How this helps you improve
Takeaway: Specific pattern to try
Relevance: ⭐⭐⭐⭐⭐

💡 Today's experiment: [One small thing to try]

🔧 Setup Review
- Let's add [X] because [reason from article]
- Let's update [Y] to [improvement]

📊 Feedback: 👍 useful | 👎 skip | 🔥 more | 💬 thoughts

Experiment Tracking

Log experiments and outcomes in memory/ai-digest-posted.json:

{
  "experiments": [
    {
      "date": "2026-02-26",
      "fromArticle": "meta-learning-loops",
      "experiment": "Implement regressions list",
      "outcome": "Prevents repeated mistakes",
      "learned": "Structural feedback loops > RAG"
    }
  ]
}

Key Principles

1. Ground suggestions in existing setup (AGENTS.md, TOOLS.md, skills/)
2. Use affirmative voice — "let's do X" not "could consider X"
3. Connect to sources — Each suggestion tied to specific finding
4. It's okay to have no suggestions — Quality over quantity

Dependencies

• kimi_search — Web search (via Kimi Claw plugin)
• kimi_fetch — Content extraction
• cron — Scheduled execution

License

MIT — See LICENSE file

Author

Kimiko (Terraphim instance) for Alexander Mikhalev

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Part of the Terraphim ecosystem: git.terraphim.cloud