dsh-crew

Why DSH Crew

DSH Crew is a plugin for DeepSeek Harness (DSH) — an open-source agent harness. It makes DSH agents dispatchable from Claude Code and Codex: the orchestrator keeps its own model, the work runs on a real DSH agent with that harness's tools, sandbox, presets and session history, and the host still shows it as a native subagent with live progress.

What runs the work is a DSH agent, not a bare model call. Tiers (flash / pro) select how much capability that agent gets from the harness's configured roster — DeepSeek V4 Flash and V4 Pro today — so a change of model in DSH needs no change here.

🧵 Native Progress UI

Workers appear as regular subagents in Claude Code / Codex — dispatch count, running step, tool calls and token usage all show up in the host's own task panel, plus a claude-hud statusline segment: ⚙dsh 1▶pro 2m14s 21.7k/606 ✓3.

🎚️ Tier Policy and Escalation

flash for mechanical work, pro for reasoning, effort from off to max. tier_policy can clamp every dispatch to one tier at the tool layer, and escalate_on_failure retries a failed flash run once on pro — based on evidence, not on guessing difficulty up front.

🏛️ In-Host DSH Sessions

With the bundle installed in a DSH profile, each worker is a first-class DSH session: visible in the Web UI, grouped by working directory, mounted with the Agent preset you choose per tier. Without DSH running, dispatch falls back to a standalone DSH runtime, so CI and headless environments still work.

👁️ Vision and Image Generation

DSH's models are text-only. describe_image and generate_image borrow the eyes and brush of the CLIs you already have — Claude, Codex, Grok, Antigravity — or of any OpenAI-compatible API you configure. Pasted images stay visible in the conversation and reach the model as text.

🔌 Custom Providers

Bring your own endpoint (Base URL + API key + models) or a local command template. Each provider has a connectivity test that checks reachability and auth, then makes one real vision call so you find out now, not mid-task.

📦 One-Click Install

The settings page installs and updates the Claude Code plugin and the Codex role files for you — marketplace registration, permission allowlist, HUD wiring, absolute paths rendered for this machine — and restores them just as easily. Every settings file is backed up first.

Related MCP server: claude-code-mcp

How it works

Claude Code / Codex (orchestrator, keeps its own model)
  └─ ds-flash / ds-pro  ← native subagent shell (progress shows in the host's task UI)
       └─ MCP: dsh_run_worker(tier, effort, cwd)
            ├─ hub reachable → session inside DSH (visible in the Web UI, grouped by cwd)
            └─ otherwise     → dsh-jsonrpc-agent runtime (worker.cordis.yml)
                 └─ DeepSeek V4 Flash / Pro (DSH SDK, event stream → progress and token stats)

One run, two views

Dispatch fans out. Below, eighteen workers translate this README in parallel: the host counts them as its own subagents, while the harness runs them as real sessions.

Install

Install into a DSH profile from npm:

dsh plugin --profile web add @zseven-w/dsh-crew@latest
dsh web

Or, for local development straight from the source tree:

dsh plugin --profile web add link:/path/to/dsh-crew
dsh web

The link: protocol symlinks the profile dependency to this repository, so rebuilds are visible immediately.

Configure DeepSeek credentials

Obtain an API key from platform.deepseek.com and write it to ~/.config/dsh-crew/.env:

DEEPSEEK_API_KEY=sk-...

The worker runtime keeps its own credentials, independent of the DSH web app.

Verify

node scripts/smoke.mjs

Within about ten seconds you should see smoke test passed — configuration OK. On failure the reason is printed; usually the key is missing or invalid.

Then open Settings → DSH Crew and install the Claude Code / Codex integrations with one click.

Background and terminology

• DSH (DeepSeek Harness): DeepSeek's open-source agent harness, a code agent in Web UI form, similar to Claude Code but driving DeepSeek models.

• MCP (Model Context Protocol): Anthropic's AI tool integration protocol, enables LLMs to safely call external tools and data sources.

• Cordis bundle: DSH's plugin format; this project can run standalone as an MCP service or install into DSH Web as hub mode.

• tier: capability tier — which slot of DSH's configured model roster a worker gets. flash is fast and cheap (simple tasks), pro reasons harder (complex problems). Today they map to DeepSeek V4 Flash and V4 Pro; swap models in DSH and nothing changes here.

• worker: the DSH agent doing the work — a full session with its own tools, sandbox and preset, not a bare model call.

• effort: reasoning strength, off = no reasoning, high = high reasoning investment, max = maximum reasoning investment.

Claude Code

Installation

One-click installation (choose one):

• DSH settings page (when hub mode is installed): Settings → DSH Crew → "Install to Claude Code"

• Command line: node src/install/cli.mjs all

Both do the same thing: register local marketplace (parent directory dsh-plugins/ as marketplace root) + claude plugin install + MCP tool permission allowlist + claude-hud worker status segment config (auto-backup settings.json before changes, idempotent). Restart the session after installation for changes to take effect.

Usage

• Directly in conversation, say "dispatch X to ds-flash" or "dispatch X to ds-pro", and subagent executes the task

• Dispatch count and real-time progress shown in Claude Code task UI

• HUD status line segment: ⚙dsh 1▶pro 2m14s 21.7k/606 ✓3 (current tier / elapsed time / token usage / completion count)

  • For local development, statusline/statusline.sh or statusline/worker-segment.sh can be independently integrated

• Long-running tasks: CC has timeout limits on MCP calls (MCP_TOOL_TIMEOUT adjustable), long tasks can have orchestrator use dsh_spawn_worker + dsh_worker_result(wait_seconds) polling

• Local development and debugging: claude --plugin-dir /path/to/dsh-crew to temporarily load

Session commands

These override the global defaults for the current session only, and are enforced at the tool layer rather than by prompting:

Codex

Installation

Recommended to use the installer (auto-renders paths for this machine, copies /dsh-config, /dsh-status commands):

node src/install/cli.mjs codex

Or manually copy (requires manual path modification after copying):

cp codex/agents/*.toml ~/.codex/agents/    # global or project-level .codex/agents/

Role files come pre-configured with:

• MCP server mounting configuration

• default_tools_approval_mode = "approve" (required, otherwise tool calls are auto-cancelled in exec mode)

• tool_timeout_sec = 3600

Note: When manually copying, absolute paths in the args field must be updated to match actual installation location; the installer handles this automatically.

Usage

• In interactive TUI, select "spawn ds-pro to ..." to dispatch tasks; Active/Done panels show progress

• codex exec mode can also directly call dsh_run_worker

Session commands

The same two prompts are installed for Codex:

MCP tools

Progress is simultaneously mirrored to ~/.config/dsh-crew/status.d/ (one shard file per writer, can be read by statusline / external monitoring).

Multimodal: vision and image generation

DeepSeek is a text-only model and does not support image input or generation. This plugin sources these capabilities externally through MCP tools:

Session image pasting: In DSH, switch model to DeepSeek (vision) ◉ to directly paste images. Images remain in session and display normally; the plugin appends transcribed text after them and strips images before sending—you see the image, the model reads the text.

Configuration

In DSH settings page → DSH Crew → Multimodal (or directly edit ~/.config/dsh-crew/config.json):

Vision provider (image viewing):

• claude-code (default, uses haiku, inexpensive)

• codex (uses GPT, can specify specific model)

• grok (uses Grok)

• agy (Antigravity)

• custom (OpenAI-compatible API or local command)

• off (disabled)

Image generation provider (image generation):

• codex ($imagegen, gpt-image-2)

• agy (Nano Banana)

• grok (Imagine)

• custom (OpenAI-compatible API or local command)

• off (disabled)

Custom provider

Two integration methods:

API: Any OpenAI-compatible endpoint

• Fill Base URL, API Key, model list

• Vision uses /chat/completions with inline base64 images

• Image generation uses /images/generations

• Must specify "image generation model" to have generation capability, otherwise provider only appears in vision selection

CLI: Local command template, placeholders substituted with safe references

• Vision: {image} {question} {model} → stdout as answer

• Image generation: {prompt} {output} {size} → command must write file to {output}

• Fill at least one command; whichever is filled determines capability

Connectivity test: Each custom provider has a test button

• API: Check endpoint reachability, auth, send real vision request to verify

• CLI: Check executable file, run real command to verify

• Image generation: Validate config only, no actual image output

Borrowed subscription CLIs (claude / codex / grok / agy) require you to be logged in locally; the plugin won't bypass their permissions for you.

Hub mode

This package is also a valid DSH bundle (dsh.bundle + cordis.patch.yml). After installing into DSH Web profile with dsh plugin add dsh-crew:

• Worker sessions become first-class citizens: run as first-class sessions in DSH host (agents.create + per-session model/effort waterfall + default preset), appear in Web UI session list, can be opened anytime to view complete execution

• Organize by working directory: manage worker sessions by cwd in Web UI

• Loopback API:

  • POST/GET /_dsh/dsh-crew/jobs: spawn tasks, list, long-poll results, cancel

  • GET /_dsh/dsh-crew/ping: health check (MCP shim uses this to detect if hub is running)

  • POST /_dsh/dsh-crew/install: one-click install Claude Code / Codex integration (backend of src/install/)

• Auto-detection: CC/Codex's MCP shim auto-detects hub (DSH_CREW_HUB env var, default http://127.0.0.1:3080)

  • DSH Web running → jobs enter hub mode (mode: "hub")

  • Not running → fall back to standalone runtime

Solution selection and limitations

Regular subscribers → shell subagent approach (recommended)

• Current state: Claude Code subagent shell uses haiku as intermediary; each dispatch adds hundreds to thousands of tokens

• Trade-off: Use small amount of Anthropic token in exchange for native task UI, real-time progress display, no extra configuration

• Recommendation: If you already subscribe to Claude Pro or use Claude Code, use this approach—convenient and transparent

Pay-as-you-go / CI environments → direct router approach

• Current state: Claude Code subagent frontmatter doesn't support direct third-party model connection; this repo's router experiment in scratchpad requires API-key credentials for Claude Code, but subscription OAuth is blocked upstream by Anthropic with 403

• Recommendation:

  • If using API-key credentials (not OAuth) and want to save Anthropic tokens, can run local router for direct DeepSeek connection

  • CI environments typically also use API keys; this approach is more economical (all DeepSeek tokens)

  • Requires self-testing of router integration (not officially supported)

Running DSH Web → hub mode auto-enabled

• Current state: If dsh plugin add dsh-crew installed into DSH Web profile, jobs run as first-class sessions in host, appear in Web UI session list

• Recommendation: During local development iteration, recommend enabling hub mode; worker progress can be fully observed in Web UI; for cross-machine collaboration or environments without Web UI, use Claude Code / Codex shell approach

Known items

• Codex role can theoretically try model_provider pointing directly to DeepSeek (unverified); this bridge doesn't depend on it

• Image generation output is flat bitmap; layer editing requires OpenPencil

• Runtime dependencies: Only @modelcontextprotocol/sdk and zod; @deepseek-ai/* are peerDependencies (provided by DSH host)

• Codex must configure: default_tools_approval_mode = "approve", otherwise tool calls are auto-cancelled

Develop

pnpm install
node_modules/.bin/tsdown src/client/index.tsx --format cjs --platform browser \
  --target es2022 --tsconfig tsconfig.client.json --out-dir .client-build --clean
node scripts/build-client.mjs   # wraps the bundle for the DSH module loader
node scripts/smoke.mjs          # dispatches one real flash task end to end

Runtime dependencies are only @modelcontextprotocol/sdk and zod; every @deepseek-ai/* package is a peer dependency provided by the DSH host, which keeps the plugin inside the host's single module realm.

Ecosystem

• DSH Noema — long-term memory for DSH

• DSH OpenPencil — inspect and edit .op design documents inside a conversation

License

MIT