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guaardvark / setup

Connect this agent to a running Guaardvark (self-hosted AI studio) and check what it can do right now. Use first when the user mentions Guaardvark, asks to generate images/video/music/voice locally, run a Film Crew, train a LoRA, launch a swarm, or when a guaardvark MCP tool is missing or a call fails with "not reachable" / "plugin offline".

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SKILL.md
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Guaardvark: connect and check

Guaardvark runs on the user's own machine. Everything below is local; nothing leaves the box.

Where it is

  • Backend URL: ${GUAARDVARK_URL:-http://localhost:5000}. The macOS default port is 5055
    (AirPlay owns 5000). The web UI is on port 5173 in dev, or the same port as the backend in Docker.

  • Health: !curl -sf ${GUAARDVARK_URL:-http://localhost:5000}/api/health || curl -sf http://localhost:5055/api/health || echo "backend not reachable on 5000 or 5055"
  • Plugins: !curl -sf ${GUAARDVARK_URL:-http://localhost:5000}/api/plugins/status || echo "plugin status unavailable"

If the backend is not reachable, tell the user to start it from the Guaardvark checkout
(./start.sh, or docker compose up), then retry. Do not try to start it yourself.

Two ways to drive it

  1. MCP tools (preferred when present): the guaardvark MCP server exposes RAG, memory,
    code intelligence, file processing, web fetch, image/video/animation/music-video/film-crew
    generation, songs and speech (generate_music, generate_speech),
    get_generation_status for any queued batch or song, outreach drafting and GPU/log
    inspection. Generation tools queue by default over MCP and return a batch id. In Claude Code they appear as
    mcp__guaardvark__<tool> after python -m backend.mcp install, or as
    mcp__plugin_guaardvark_guaardvark__<tool> when the plugin was installed from the marketplace. Install once from the checkout:
    `bash
    python -m backend.mcp install # writes the server entry into every detected client: Claude Code, Codex,
    # Cursor, Grok, Antigravity, opencode, Claude Desktop, Zed, Gemini
    python -m backend.mcp doctor # self-test + stale-config scan
    python -m backend.mcp list-tools # what is exposed right now
    `
    Restart the client after installing so it re-reads its MCP config.

  2. REST for everything the MCP policy does not expose (voice cloning, sound effects, upscaling, batches,
    cast/LoRA training, swarm launch, interconnector, plugins). Use curl against the backend URL.
    Responses are wrapped as {"success": bool, "data": {...}, "message": str} on most routes; a few
    older routes return the bare object. Read data when it is present.

Before generating anything

  • GPU services are plugins. Check GET /api/plugins/status; a generation route answers 503 when
    its plugin is not running. Start one with POST /api/plugins/<id>/start where <id> is
    comfyui (image + video), audio_foundry (voice, music, FX), upscaling, lora_trainer,
    swarm. Only one heavy model owns the GPU at a time; the orchestrator evicts Ollama for video
    and vice versa, so a first call after a switch is slow. inspect_gpu (MCP) shows who holds it.

  • Installed models: GET /api/batch-video/models and GET /api/batch-image/models list every
    registry entry with capabilities; check is_ready before naming a model. Nothing downloads
    without an explicit Install, so if a model is missing say so and offer
    POST /api/batch-video/models/download {"model_id": "..."} (or the image route with
    {"model_path": "..."}).

  • Outputs land under data/outputs/ in the checkout and are also served read-only over MCP as
    guaardvark://outputs/... resources.

Skills in this pack

SkillUse for
imageone image, edits, cast characters, batch image runs
videoone clip, image-to-video, first/last frame, batch video runs, MiniMax H3 with sound
music-videoa song in, a beat-cut music video out, with the approval gate
film-crewscreenplay to finished short: writer, casting, storyboards, render, edit
voicenarration, TTS, consent-gated voice cloning
musicfull songs with lyrics, instrumentals, sound effects
upscale2x to 8K upscaling of images and video
castCast Library subjects and LoRA training for consistent characters
modelsadd any Hugging Face model or LoRA from a URL
swarmparallel coding agents in git worktrees from a plan file
knowledgethe user's indexed documents, memory, web fetch
codecode search, repository map, self-improvement status
outreachsupervised social drafts (never posts)
opsGPU, logs, Celery, plugins, Interconnector sync, autoresearch, infographics