Plugin directory / AI Models / dsh-atlascloud
dsh-atlascloud
Verified · install-tested on dsh AtlasCloudAI
What it does
Atlas Cloud skills and opt-in MCP tools for DeepSeek Harness
Works — verified, early-stage project
Atlas Cloud skills and opt-in MCP tools for DeepSeek Harness It installs cleanly and boots without issues in our testing. It's early-stage but functional.
“Verified” means our automated CI actually ran dsh plugin add in a clean profile and it booted — nothing more. Feature descriptions and version compatibility are the author’s claims. This is not a security audit and not an endorsement of third-party code.
README
dsh-media-gen
Turn one media brief into a model choice, production-ready prompt, and—when you opt in to MCP—an executable Atlas Cloud workflow.
dsh-media-gen is an independent DeepSeek Harness profile bundle maintained by AtlasCloudAI. It covers image, video, audio, and 3D workflows. The three Skills become discoverable after installation; the optional Atlas Cloud MCP bridge is included but disabled by default.
Quick start
Install the bundle into the DSH profile where you want to use it:
dsh plugin --profile web add 'github:AtlasCloudAI/dsh-media-gen#v0.2.0'
dsh --profile web --dump-config
Use --profile headless instead for a headless profile. Pin a release tag or full commit for reproducible installations.
Try it
Start that DSH profile and paste this no-submit demo:
Plan an 8-second coffee product video. Compare Seedance with one available alternative, recommend a model, and produce a three-shot storyboard, final prompt, and exact parameters. Do not submit generation; wait for my confirmation.
Depending on the request, DSH can use the bundled Skills to compare model approaches, turn the brief into a coherent storyboard, and prepare the selected model's input. The demo does not ask DSH to submit a generation request.
What each component does
| Component | Problem it solves | Typical result |
|---|---|---|
atlas-cloud Skill |
Which Atlas Cloud model, API, and schema should I use for image, video, audio, 3D, ASR, or LLM work? | A model ID, validated parameters, and a REST, CLI, or MCP execution path. |
seedance-2-5-skill Skill |
How do I plan a controllable, consistent Seedance video across shots and references? | A storyboard, continuity plan, and Seedance-ready prompt and parameters. |
universal-video-prompt-skill Skill |
How do I reuse one video brief across different generation models? | One model-neutral prompt specification plus model-specific compilations. |
Optional [email protected] |
How can DSH call supported Atlas Cloud operations without hand-wiring each request? | Tools for model and schema lookup, media upload, generation, polling, and account usage checks. |
In short: Skills teach DSH how to plan and integrate; MCP gives it callable execution tools.
Credentials and execution
Installing and discovering the Skills requires no Atlas Cloud credential and submits no Atlas Cloud API request.
For execution, obtain a key from the Atlas Cloud console and set it in the process that starts DSH:
export ATLASCLOUD_API_KEY="<your-key>"
Do not paste the key into chat or commit it to this repository.
Enable MCP execution (optional)
The bundle's atlascloud-mcp row is disabled by default because a stdio MCP server is a trusted child process that runs outside the agent sandbox.
To opt in, add this later-layer override to the target profile's $DSH_HOME/profiles/<profile>/cordis.patch.yml:
- id: atlascloud-mcp
disabled: false
Restart the profile and inspect the resolved configuration:
dsh --profile web --dump-config
DSH exposes qualified tool names such as mcp__atlascloud__atlas_list_models; the underlying MCP tool name remains atlas_list_models. Operations that submit generation or transcription can be billable, so review the exact model and parameters before approving a submission.
Compatibility
OpenAI/Codex plugins and DSH bundles use different host manifests. The Skill content can be reused, but the host integration cannot be installed unchanged: DSH requires package.json with dsh.bundle.patch and a Cordis MCP row. This repository uses the current DSH profile-bundle format, not the retired .dsh-plugin format.
See the full compatibility decision.
Provenance and verification
The Skills are synchronized from AtlasCloudAI/atlas-cloud-skills. skills/SOURCE.json records the exact source commit and local DSH adaptations.
npm test
npm pack --dry-run
The checks validate the bundle manifest, default-off MCP policy, pinned MCP executable, Skill frontmatter, public-language guard, source pin, and packaged relative resources. They do not submit generation, upload media, transcribe audio, or call a billable Atlas Cloud endpoint.
License
Install
Install the catalog once, then DeepSeek Harness can find and install any plugin from this site automatically:
dsh plugin add dshbase-catalog Then say "install dsh-atlascloud for me" — your agent finds it in the directory and installs it. Docs: dshbase-catalog · verified packs.
This plugin is GitHub source (not published to npm) — install it straight from the repo:
Web profile:
dsh plugin --profile web add github:AtlasCloudAI/dsh-atlascloud Headless (CLI) profile:
dsh plugin --profile headless add github:AtlasCloudAI/dsh-atlascloud Test report
Verified: L1 install + L2 load + L3 runtime from GitHub source on dsh 0.1.0-rc.6.
When to use it
Bring a new model, provider, or routing policy into the loop so dsh can pick the right brain for the job.
Who it's for
Users juggling multiple models or providers who want cost, quality, and latency balanced automatically.
For developers — extending it
Provider adapters and routing heuristics are the seams — add a backend, tune the fallback chain, or add per-task model selection.