Initial commit: ai-self-improvement-digest skill with kimi_search support
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Kimi Claw
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name: ai-self-improvement-digest
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description: 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.
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---
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# AI Self-Improvement Digest
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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.
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## What This Digest Covers
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1. **Harness & System Prompt Engineering** - How to structure agent instructions
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2. **Skill & Tool Development** - New tools, MCP servers, integration patterns
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3. **Self-Evaluation & Improvement** - How agents assess and improve themselves
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4. **Multi-Agent Coordination** - Spawning, supervising, merging work
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5. **Memory & Context Management** - RAG, long-term memory, compaction
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6. **Workflow Automation** - Task decomposition, failure handling
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7. **Foundational Research** - Academic work on agent capabilities
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## Prerequisites
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1. **Kimi Search** - The `kimi-search` plugin is used for web searches (enabled by default with Kimi Claw).
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2. **Tracking File** - Create `memory/ai-digest-posted.json`:
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```json
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{
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"posted": [],
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"experiments": [],
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"skillsEvaluated": [],
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"setupChanges": []
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}
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```
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## Daily Digest Workflow
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### Step 1: Deduplication (MANDATORY)
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Read `memory/ai-digest-posted.json`. Skip anything already posted (by URL or substantially similar topic).
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### Step 2: Scan Sources
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Use `kimi_search` and `kimi_fetch` to check these sources for content from last 24-72h:
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**Tier 1 (daily):**
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- Anthropic Engineering: anthropic.com/engineering
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- Simon Willison: simonwillison.net
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- Geoff Huntley: ghuntley.com
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- Hacker News: news.ycombinator.com (AI/agent threads)
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- Lilian Weng: lilianweng.github.io
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**Tier 2 (2-3x/week):**
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- Latent Space: latent.space
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- Cursor Blog: cursor.com/blog
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- Eugene Yan: eugeneyan.com
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- Chip Huyen: huyenchip.com
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- Mitchell Hashimoto: mitchellh.com
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**Tier 3 (weekly):**
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- arXiv cs.CL/cs.AI
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- GitHub Trending (AI agent repos, MCP servers)
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### Step 3: Filter for Self-Improvement Relevance
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Only include items that help improve capabilities in the 7 categories listed above.
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**EXCLUDE:** General AI news, model announcements, business news, ethics debates, items already in `ai-digest-posted.json`.
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### Step 4: Format (3-5 items)
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For each item, include:
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```
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**[Title]** — [Source]
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What: [1-sentence summary]
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Why it matters for self-improvement: [How this helps you get better]
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Takeaway: [Specific pattern, technique, or experiment to try]
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Relevance: [⭐ to ⭐⭐⭐⭐⭐]
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```
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### Step 5: Experiment Suggestion
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Include one small experiment to try based on the digest:
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```
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💡 Today's experiment: [One small thing to try that could improve capabilities]
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```
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### Step 6: Setup Review (MANDATORY)
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Review findings against existing setup (AGENTS.md, TOOLS.md, skills/, cron jobs). Make concrete, affirmative suggestions:
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```
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🔧 Setup Review
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Based on today's findings:
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- Let's add [specific thing] because [reason tied to content found]
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- Let's update [existing thing] to [improvement] because [reason]
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```
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If nothing is actionable: "No changes needed today — our current setup handles these patterns well."
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### Step 7: Update Tracking
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Append new items to `memory/ai-digest-posted.json` with date, title, url, topic.
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## Output Format
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```
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🧠 AI Self-Improvement Digest — [Date]
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[Items formatted as above]
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💡 Today's experiment: [...]
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🔧 Setup Review
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[Suggestions or "No changes needed today"]
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📊 Feedback: 👍 = useful | 👎 = skip these | 🔥 = more like this | 💬 = thoughts
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```
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## Source Priority Reference
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| Source | Priority | Focus |
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|--------|----------|-------|
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| Anthropic Engineering | ⭐⭐⭐ | Harness design, evals, multi-agent |
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| Simon Willison | ⭐⭐⭐ | Practical patterns, tools |
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| Geoff Huntley | ⭐⭐⭐ | Agent philosophy, MCP |
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| Hacker News | ⭐⭐⭐ | High-signal AI/agent discussions |
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| Lilian Weng | ⭐⭐⭐ | Deep technical AI, agent architectures |
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| Latent Space | ⭐⭐ | Industry depth |
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| Cursor Blog | ⭐⭐ | Coding agent patterns |
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| Eugene Yan | ⭐⭐ | ML systems, production patterns |
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| Chip Huyen | ⭐⭐ | ML systems design |
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| arXiv cs.CL/cs.AI | ⭐⭐ | Research foundations |
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| GitHub Trending | ⭐⭐ | New tools, repos |
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## Self-Improvement Loop
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The digest enables continuous improvement:
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**DAILY:**
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- Read digest
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- Pick 1 experiment to try
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- Log outcome in `memory/ai-digest-posted.json`
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- Review Setup Review suggestions with human
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**WEEKLY:**
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- Review experiments
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- Update harness/skills based on learnings
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- Adjust source priorities based on value
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## Experiment Tracking
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Extend `memory/ai-digest-posted.json`:
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```json
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{
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"posted": [...],
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"experiments": [
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{
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"date": "2026-02-16",
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"fromArticle": "effective-harnesses",
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"experiment": "Add checkpoint before sub-agent spawn",
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"outcome": "Reduced context loss by 40%",
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"learned": "Always checkpoint before spawning"
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}
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],
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"skillsEvaluated": [
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{
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"date": "2026-02-16",
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"skill": "mcp-postgres",
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"verdict": "useful",
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"notes": "Integrated for database queries"
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}
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],
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"setupChanges": [
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{
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"date": "2026-02-16",
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"change": "Added memory/experiments.md",
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"reason": "Track harness experiments per Anthropic article",
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"status": "implemented"
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}
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]
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}
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```
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## Cron Job Setup
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Schedule daily at 8:30 AM:
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```bash
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openclaw cron add \
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--name ai-self-improvement-digest \
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--schedule "30 8 * * *" \
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--tz "America/New_York" \
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--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."
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```
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Or use the `cron` tool directly with `action: add` and the job configuration.
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## Key Principles
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1. **Ground suggestions** in what you already have
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2. **Use affirmative voice** ("let's do X") not passive ("could consider X")
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3. **Connect each suggestion** to a specific article/finding from the digest
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4. **It's okay to have no suggestions** if nothing is actionable
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