Self-Update Skill
Updates the AGI Agent Kit framework components (skills, agents, workflows) to the latest published version.
When to Use
- When a new version of
@techwavedev/agi-agent-kitis available - When you want to sync your local installation with the latest templates
- After the user asks to "update", "upgrade", or "get latest skills"
How It Works
- Check Current Version: Reads
.agi.jsonfor installed version (if present) - Fetch Latest: Runs
npm view @techwavedev/agi-agent-kit version - Update: Re-runs init with
--pack=fullto overlay new templates
Quick Commands
# Check installed version
cat .agi.json 2>/dev/null || echo "No version metadata found"
# Check latest NPM version
npm view @techwavedev/agi-agent-kit version
# Update to latest (re-init with full pack)
npx @techwavedev/agi-agent-kit@latest init --pack=full
Update Script
Use the provided script for a guided update:
python3 skills/self-update/scripts/update_kit.py
What Gets Updated
| Component | Updated? | Notes |
|---|---|---|
| Skills | ✅ Yes | All skills from templates |
| Agents | ✅ Yes | .agent/ directory |
| Workflows | ✅ Yes | .agent/workflows/ |
| Skill Creator | ✅ Yes | skill-creator/ |
| AGENTS.md | ✅ Yes | Overwrites with latest |
| User's .env | ❌ No | Never touched |
| User's directives | ❌ No | Preserved |
Notes
- The update process overwrites skill scripts. If you've customized them, back up first.
- Your
.envand custom directives are always preserved. - After updating, run
/checkupto verify everything is working.
AGI Framework Integration
Qdrant Memory Integration
Before executing complex tasks with this skill:
python3 execution/memory_manager.py auto --query "<task summary>"
Decision Tree:
- Cache hit? Use cached response directly — no need to re-process.
- Memory match? Inject
context_chunksinto your reasoning. - No match? Proceed normally, then store results:
python3 execution/memory_manager.py store \
--content "Description of what was decided/solved" \
--type decision \
--tags self-update <relevant-tags>
Note: Storing automatically updates both Vector (Qdrant) and Keyword (BM25) indices.
Agent Team Collaboration
- Strategy: This skill communicates via the shared memory system.
- Orchestration: Invoked by
orchestratorvia intelligent routing. - Context Sharing: Always read previous agent outputs from memory before starting.
Local LLM Support
When available, use local Ollama models for embedding and lightweight inference:
- Embeddings:
nomic-embed-textvia Qdrant memory system - Lightweight analysis: Local models reduce API costs for repetitive patterns
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