@kanonouta/dsh-captain English | 中文 Captain is a dual-face DeepSeek Harness plugin that exposes one synthetic captain model route. GPT is the planner and independent reviewer, while DeepSeek workers make the incremental repository changes. ## Install into DeepSeek Harness Captain ships its bundle manifest and built Host/Client entry points, so a Web profile can install it directly from GitHub: powershell dsh plugin --profile web add github:KanoNoUta/dsh-captain For source development inside a DeepSeek Harness checkout: powershell cd F:\path\to\deepseek-harness git submodule add https://github.com/KanoNoUta/dsh-captain.git packages/extensions/captain git apply packages/extensions/captain/patches/deepseek-harness-integration.patch pnpm install pnpm exec tsc -b packages/extensions/captain/tsconfig.host.json --pretty false pnpm --filter @kanonouta/dsh-captain run bundle The optional source integration patch adds both TypeScript compiler faces and keeps official DeepSeek and OpenCode DeepSeek routes distinguishable in the native model selector. Normal dsh plugin add installation uses cordis.patch.yml and does not require that patch. ## Composition The Host registers captain through the normal LLM adapter directory, so the existing model selector renders a route such as GPT-5.6 Terra -> DeepSeek V4 Flash beside ordinary providers. The route id is captain:<planner-model>-><worker-model> and the model entry advertises text and image input plus Balanced, High Quality, and Ultra reasoning efforts. Captain resolves each nested provider/model route before dispatch. A policy effort is downgraded to the strongest effort that route advertises, while routes with no selectable reasoning effort receive no effort field. Captain calls the configured provider routes through the existing OpenAI-compatible LLM adapters. It does not implement OAuth or a second credential store; configure relay providers in the normal LLM settings and reference their provider and model ids from the Captain settings namespace. The planner returns a JSON dependency DAG. Workers with disjoint file ownership run concurrently under the configured token budget and adaptive parallel limit. A worker response that contains only unexecuted DSML tool-call markup receives one correction attempt; a second such response fails the task. Worker execution failures stop before review and repair. A reviewer receives the acceptance criteria, worker reports, and the current incremental Git diff, and receives one strict JSON correction attempt after a malformed response. A failed code review selects only tasks named by findings for repair; an unscoped finding rechecks the full plan. A passing review advances the in-memory checkpoint used by the next run. Native tool-result continuations skip GPT planning and do not re-inject the full Captain DAG into the DeepSeek worker. They receive only a compact continuation instruction, so the worker can continue from the latest native tool state without repeatedly restating the plan. Directly selected non-Captain model routes are unchanged. Git review always runs from the parent Agent Session's cwd. A request without that workspace metadata reports the incremental diff as unavailable instead of reading changes from the Harness Host process directory. Checkpoints are isolated by workspace path within the running orchestrator. Short conversational greetings take a direct GPT planner response without starting workers or reviewers, so a social turn cannot trigger a repository diff review. A short image-identification or description request returns the vision notes directly; image turns that ask for code, fixes, deployment, commits, or releases continue through planning and implementation. Targeted repair rounds include prerequisite tasks so the scheduler always receives a runnable dependency subgraph. Image attachments remain native ImageAttachmentRef blocks. Captain sends them only to the independently selected OpenAI-compatible vision route, converts that response into text notes, and gives the notes to the GPT planner and DeepSeek workers. Vision calls omit reasoningEffort so the provider applies its own default. If the selected model explicitly advertises text-only input, Captain selects an image-capable model from the same provider, preferring Terra and then Luna; a provider with no declared image model fails before dispatch with the missing input: [text, image] correction. ## Settings The browser half contributes a card to Settings -> Plugins -> Captain. Provider and model selects read the live host-wide llm.models catalog. GPT relay routes expose their supported low, medium, high, and xhigh controls even when a relay omits reasoning metadata; other routes use their exact advertised efforts plus an automatic provider-default choice. The Vision selector prefers dedicated Luna, Terra, vision, VL, and omni model names and has no reasoning-effort control because vision calls use the provider default. Captain policy and scheduling mode are selects; numeric limits use bounded number controls. The reviewer toggle switches between the dedicated GPT reviewer route and the current DeepSeek worker route. The card stages planner, worker, reviewer, vision, policy, reviewer toggle, and orchestration settings and writes them through the Host settings namespace captain. The composition entry is intentionally relay-oriented: yaml - id: captain name: '@kanonouta/dsh-captain' config: planner: provider: gpt-relay model: gpt-5.6-sol reasoningEffort: max worker: provider: deepseek-official model: deepseek-v4-flash reasoningEffort: high reviewer: provider: gpt-relay model: gpt-5.6-terra reasoningEffort: ultra vision: provider: gpt-relay model: gpt-5.6-terra reasoningEffort: '' reviewerEnabled: true The normal llm-pi-ai provider profile must declare image input for gateway models that are absent from its built-in catalog: yaml llm-pi-ai: providers: gpt-relay: models: - id: gpt-5.6-luna input: [text, image] - id: gpt-5.6-sol - id: gpt-5.6-terra input: [text, image] maxAgents is a ceiling, not a fixed fan-out. mode: auto and adaptiveConcurrency: true grow parallelism after successful work and reduce it after provider rate limits or timeouts; maxParallel: 0 uses the adaptive ceiling. Token budgets remain explicit because parallel requests improve wall-clock latency only while the relay has spare capacity. ## Package faces The root export is the Host plugin and pure orchestration helpers. The /client export is the browser plugin and settings-card types. pnpm --filter @kanonouta/dsh-captain run bundle produces both faces. ## Model Experience ### Captain task turn #### What the model sees The selected captain route receives the user's task, the GPT planner's JSON DAG, DeepSeek worker reports, and the independent GPT review result; worker tool calls remain on their child Agent sessions. #### Token effect Planner, worker, repair, and reviewer calls consume separate configured budgets, while the final Captain response is one assembled assistant message in the parent session. #### KV Cache effect Each nested role call has its own provider/model prefix; changing a role route can reduce provider-side cache reuse for that role without rewriting the parent session history. ### Captain image turn #### What the model sees User ImageAttachmentRef blocks are forwarded to the resolved image-capable vision route through the existing LLM content vocabulary. Its text response becomes Vision companion notes for the planner and workers; no browser path or base64 value is inserted into prompt text, and the Sol planner never receives the original image. #### Token effect Image and text usage is charged by the selected vision provider, and the planner/worker/reviewer budgets remain independent of that request. #### KV Cache effect Adding or replacing an image changes the affected provider request suffix and can invalidate that provider's cached suffix. ## Known Limitations and Deferred Work - The current checkpoint stores a Git HEAD and diff metadata in process memory; it is deliberately advanced only after a passing review and is not a durable session event. - Untracked files are listed for the reviewer, while Git's normal binary diff remains the patch source. - A worker without an attached parent agent uses a direct LLM call and therefore cannot modify the workspace through tools.