git:20260911.973d1bc to git:20260912.01cee81

---

name: setup

description: >-

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".

---

# 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 chat, RAG, memory,

code intelligence, file processing, web fetch, image/video/animation/music-video/film-crew

generation, `get_generation_status` for any queued batch, 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 Claude Code, Cursor, Claude Desktop, Codex, 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, music, 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

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

| Skill | Use for |

|---|---|

| image | one image, edits, cast characters, batch image runs |

| video | one clip, image-to-video, first/last frame, batch video runs, MiniMax H3 with sound |

| music-video | a song in, a beat-cut music video out, with the approval gate |

| film-crew | screenplay to finished short: writer, casting, storyboards, render, edit |

| voice | narration, TTS, consent-gated voice cloning |

| music | full songs with lyrics, instrumentals, sound effects |

| upscale | 2x to 8K upscaling of images and video |

| cast | Cast Library subjects and LoRA training for consistent characters |

| models | add any Hugging Face model or LoRA from a URL |

| swarm | parallel coding agents in git worktrees from a plan file |

| knowledge | the user's indexed documents, memory, web fetch |

| code | code search, repository map, self-improvement status |

| outreach | supervised social drafts (never posts) |

| ops | GPU, logs, Celery, plugins, Interconnector sync, autoresearch, infographics |