ai-self-improvement-digest/SKILL.md

name, description

name	description
ai-self-improvement-digest	Create a daily digest focused on AI self-improvement material - content that helps an AI agent get better at its job. Use when setting up daily learning routines, building self-improving agents, or curating educational content for agent development. Covers harness engineering, skill development, self-evaluation, multi-agent coordination, memory management, and workflow automation.

AI Self-Improvement Digest

This skill creates a daily digest focused on AI self-improvement material, not general AI news. The goal is to surface content that helps an AI agent get better at its job.

What This Digest Covers

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

Prerequisites

1. Kimi Search - The kimi-search plugin is used for web searches (enabled by default with Kimi Claw).

2. Tracking File - Create memory/ai-digest-posted.json:

   {
     "posted": [],
     "experiments": [],
     "skillsEvaluated": [],
     "setupChanges": []
   }
   

Daily Digest Workflow

Step 1: Deduplication (MANDATORY)

Read memory/ai-digest-posted.json. Skip anything already posted (by URL or substantially similar topic).

Step 2: Scan Sources

Use kimi_search and kimi_fetch to check these sources for content from last 24-72h:

Tier 1 (daily):

• Anthropic Engineering: anthropic.com/engineering
• Simon Willison: simonwillison.net
• Geoff Huntley: ghuntley.com
• Hacker News: news.ycombinator.com (AI/agent threads)
• Lilian Weng: lilianweng.github.io

Tier 2 (2-3x/week):

• Latent Space: latent.space
• Cursor Blog: cursor.com/blog
• Eugene Yan: eugeneyan.com
• Chip Huyen: huyenchip.com
• Mitchell Hashimoto: mitchellh.com

Tier 3 (weekly):

• arXiv cs.CL/cs.AI
• GitHub Trending (AI agent repos, MCP servers)

Step 3: Filter for Self-Improvement Relevance

Only include items that help improve capabilities in the 7 categories listed above.

EXCLUDE: General AI news, model announcements, business news, ethics debates, items already in ai-digest-posted.json.

Step 4: Format (3-5 items)

For each item, include:

**[Title]** — [Source]
What: [1-sentence summary]
Why it matters for self-improvement: [How this helps you get better]
Takeaway: [Specific pattern, technique, or experiment to try]
Relevance: [⭐ to ⭐⭐⭐⭐⭐]

Step 5: Experiment Suggestion

Include one small experiment to try based on the digest:

💡 Today's experiment: [One small thing to try that could improve capabilities]

Step 6: Setup Review (MANDATORY)

Review findings against existing setup (AGENTS.md, TOOLS.md, skills/, cron jobs). Make concrete, affirmative suggestions:

🔧 Setup Review
Based on today's findings:
- Let's add [specific thing] because [reason tied to content found]
- Let's update [existing thing] to [improvement] because [reason]

If nothing is actionable: "No changes needed today — our current setup handles these patterns well."

Step 7: Update Tracking

Append new items to memory/ai-digest-posted.json with date, title, url, topic.

Output Format

🧠 AI Self-Improvement Digest — [Date]

[Items formatted as above]

💡 Today's experiment: [...]

🔧 Setup Review
[Suggestions or "No changes needed today"]

📊 Feedback: 👍 = useful | 👎 = skip these | 🔥 = more like this | 💬 = thoughts

Source Priority Reference

Source	Priority	Focus
Anthropic Engineering	⭐⭐⭐	Harness design, evals, multi-agent
Simon Willison	⭐⭐⭐	Practical patterns, tools
Geoff Huntley	⭐⭐⭐	Agent philosophy, MCP
Hacker News	⭐⭐⭐	High-signal AI/agent discussions
Lilian Weng	⭐⭐⭐	Deep technical AI, agent architectures
Latent Space	⭐⭐	Industry depth
Cursor Blog	⭐⭐	Coding agent patterns
Eugene Yan	⭐⭐	ML systems, production patterns
Chip Huyen	⭐⭐	ML systems design
arXiv cs.CL/cs.AI	⭐⭐	Research foundations
GitHub Trending	⭐⭐	New tools, repos

Self-Improvement Loop

The digest enables continuous improvement:

DAILY:

• Read digest
• Pick 1 experiment to try
• Log outcome in memory/ai-digest-posted.json
• Review Setup Review suggestions with human

WEEKLY:

• Review experiments
• Update harness/skills based on learnings
• Adjust source priorities based on value

Experiment Tracking

Extend memory/ai-digest-posted.json:

{
  "posted": [...],
  "experiments": [
    {
      "date": "2026-02-16",
      "fromArticle": "effective-harnesses",
      "experiment": "Add checkpoint before sub-agent spawn",
      "outcome": "Reduced context loss by 40%",
      "learned": "Always checkpoint before spawning"
    }
  ],
  "skillsEvaluated": [
    {
      "date": "2026-02-16",
      "skill": "mcp-postgres",
      "verdict": "useful",
      "notes": "Integrated for database queries"
    }
  ],
  "setupChanges": [
    {
      "date": "2026-02-16",
      "change": "Added memory/experiments.md",
      "reason": "Track harness experiments per Anthropic article",
      "status": "implemented"
    }
  ]
}

Cron Job Setup

Schedule daily at 8:30 AM:

openclaw cron add \
  --name ai-self-improvement-digest \
  --schedule "30 8 * * *" \
  --tz "America/New_York" \
  --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."

Or use the cron tool directly with action: add and the job configuration.

Key Principles

1. Ground suggestions in what you already have
2. Use affirmative voice ("let's do X") not passive ("could consider X")
3. Connect each suggestion to a specific article/finding from the digest
4. It's okay to have no suggestions if nothing is actionable