Skill: daeva

Use when: The user asks to transcribe audio, generate images, run OCR/vision jobs, manage local AI pods (Whisper, ComfyUI, etc.), or interact with the local orchestrator.

Overview

daeva

http://127.0.0.1:8787

This skill covers:

• Checking pod/service status
• Submitting and polling jobs
• Installing pod packages from the registry
• Interacting via the MCP server (if configured)

Base URL

ORCHESTRATOR_BASE_URL=http://127.0.0.1:8787   # default

Check if it's running:

curl -s http://127.0.0.1:8787/health
# → {"ok":true}

Key Endpoints

/health

/pods

/pods/aliases

/pods/installed

/pods/create

/status

/status/scheduler

/jobs

/jobs/:id

/jobs/:id/result

Common Tasks

Check status

curl -s http://127.0.0.1:8787/status | jq .

List pods & aliases

curl -s http://127.0.0.1:8787/pods | jq .
curl -s http://127.0.0.1:8787/pods/aliases | jq .

Install a pod from registry alias

curl -s -X POST http://127.0.0.1:8787/pods/create \
  -H 'Content-Type: application/json' \
  -d '{"alias":"whisper"}' | jq .

Submit a transcription job

curl -s -X POST http://127.0.0.1:8787/jobs \
  -H 'Content-Type: application/json' \
  -d '{
    "type": "transcribe-audio",
    "capability": "speech-to-text",
    "input": {
      "filePath": "/tmp/audio.wav",
      "contentType": "audio/wav"
    }
  }' | jq .
# Returns: {"job":{"id":"...","status":"queued",...}}

Poll a job

JOB_ID="<id from above>"
curl -s "http://127.0.0.1:8787/jobs/$JOB_ID" | jq .job.status
curl -s "http://127.0.0.1:8787/jobs/$JOB_ID/result" | jq .

Submit an image generation job

curl -s -X POST http://127.0.0.1:8787/jobs \
  -H 'Content-Type: application/json' \
  -d '{
    "type": "generate-image",
    "capability": "image-generation",
    "input": {
      "prompt": "a red fox on a snowy mountain, photorealistic",
      "width": 1024,
      "height": 1024
    }
  }' | jq .

MCP Server (for AI clients)

The orchestrator ships an MCP stdio server exposing 8 tools:

list_pods

list_aliases

list_installed

get_status

get_scheduler

enqueue_job

get_job

create_pod

MCP client config (e.g. for Claude Desktop / OpenClaw)

{
  "mcpServers": {
    "daeva": {
      "command": "node",
      "args": [
        "/path/to/daeva/dist/src/mcp-cli.js",
        "--base-url", "http://127.0.0.1:8787"
      ]
    }
  }
}

Or via environment variable:

ORCHESTRATOR_BASE_URL=http://127.0.0.1:8787 node dist/src/mcp-cli.js

Starting the Orchestrator

Quick start (foreground):

cd ~/daeva
PORT=8787 node dist/src/cli.js

Via systemd user service (if installed):

systemctl --user start daeva
systemctl --user status daeva
journalctl --user -fu daeva

Install script (server setup):

./scripts/install-server.sh              # full setup
./scripts/install-server.sh --skip-podman   # no Podman setup
./scripts/install-server.sh --skip-service  # no systemd unit
./scripts/install-server.sh --dry-run       # see what would happen

Capabilities & Job Types

speech-to-text

transcribe-audio

filePath

url

contentType

image-generation

generate-image

prompt

ocr

extract-text

filePath

url

vision

describe-image

detect-objects

filePath

url

Troubleshooting

• /health

  returns connection refused → orchestrator isn't running. Start it or check

  systemctl --user status daeva

  .
• Job stays

  queued

  → no pod registered for that capability. Check

  /pods

  and ensure a pod is running.

• 404 alias not found

  → use

  /pods/aliases

  to list valid aliases, or install from a direct source.
• Podman pull fails → ensure Podman is installed and the user has network access.

Notes

• Data lives in

  ~/.local/share/daeva/

  by default (override with

  DATA_DIR

  env var).
• All times are ISO 8601. Job results are ephemeral (in-memory) — not persisted across restarts.
• For GPU pods, ensure the container runtime has access to the GPU (e.g.

  --device nvidia.com/gpu=all

  in quadlet).