deploy
Build and deploy the realtime-api Docker container with full verification that deployed code matches local source.
- License 许可
- MIT
- 最近更新
- 2026-04-09
- Stars
- ★ 246
- Forks
- 15
01 前言 Front Matter
- name
- deploy
- triggers
- [ "deploy", "redeploy", "rebuild", "restart container", "ship it" ]
- description
- Build and deploy the realtime-api Docker container with full verification that deployed code matches local source.
03 SKILL.md 正文
Deploy Realtime-API
Build, deploy, and verify the realtime-api Docker container.
When to use
- After making code changes to
src/realtime_api/files - When the user says "deploy", "redeploy", "rebuild", "restart"
- After implementing fixes or features that need testing against the live system
Instructions
Run the deploy script:
cd /home/spark/git/autonomous-intelligence/realtime-api && bash scripts/deploy.sh
The script will:
- Verify all required source files exist
- Build the Docker image with
--no-cache(guarantees fresh copy) - Restart only the
realtime-apicontainer (preserves TTS/STT/LLM) - Wait for the container to be running
- Verify deployed code matches local source (hash comparison + feature checks)
- Show recent container logs
After deploying
After a successful deploy, speak a brief confirmation:
/home/spark/git/autonomous-intelligence/realtime-api/scripts/speak.sh "Deploy complete. All checks passed."
Troubleshooting
- If hash mismatch: Docker build cache may be stale — the script already uses
--no-cache - If container won't start: check
docker compose logs realtime-api - If dependencies (TTS/STT/LLM) are down:
docker compose up -dto start all services
04 同仓库更多 Skills
test
Run TTS round-trip tests — pure-logic unit tests and optional integration tests against live TTS/STT services.
speak
Speak text aloud using the Magpie TTS container in the realtime-api Docker stack. Zero external dependencies — just curl + aplay.
memory
Unified memory management for notes, knowledge graph, RAG search, and file analysis. Use when working with: (1) Core memory — protected identity, projects, relationships, and system facts that should never be forgotten, (2) Working notes — per-session ephemeral notes organized by section, (3) MongoDB RAG — vector-search-enabled notes with importance scoring, decay, deduplication, and archival, (4) Neo4j knowledge graph — entities, relationships, merge duplicates, reinforce mentions, Cypher queries, (5) File analysis — deep file reading that extracts knowledge into all memory layers, (6) Service initialization — health-check, start/stop MongoDB, Neo4j, TEI embeddings via docker-compose with partial setup support.
coding
Enhanced coding assistance
memory
Unified memory management for notes, knowledge graph, RAG search, and file analysis. Use when working with: (1) Core memory — protected identity, projects, relationships, and system facts that should never be forgotten, (2) Working notes — per-session ephemeral notes organized by section, (3) MongoDB RAG — vector-search-enabled notes with importance scoring, decay, deduplication, and archival, (4) Neo4j knowledge graph — entities, relationships, merge duplicates, reinforce mentions, Cypher queries, (5) File analysis — deep file reading that extracts knowledge into all memory layers, (6) Service initialization — health-check, start/stop MongoDB, Neo4j, TEI embeddings via docker-compose with partial setup support.
test-on-finish
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