Find the right DSH plugin
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Declarative task-DAG orchestration for DSH: validates dependency graphs, runs topological task layers in parallel through workflow-backed subagents, and propagates failures deterministically.

Streams DeepSeek Harness sessions, messages, tool activity, and turn outcomes to a JARVIS Mission Control board with task status and human review.

Task reliability: auto-retry on model timeouts/failures, auto-continue unfinished tasks, completion verification, thinking-repeat interruption, resume after sleep or restart, autopilot mode and a remote trigger endpoint (npm: `dsh-task-reliability`).

Question-Driven Discovery research workflow for DeepSeek Harness with a QDD Agent Preset, durable Human and Auto runs, and an auditable research panel.

Claude Science-style research workbench: ReAct research-loop engine (research_* tools), versioned artifacts with provenance (artifact_* tools), and 10 science skills for genomics/pathogens/bioinformatics.

Verification gate for DSH agents: every acceptance criterion must be backed by server-stamped real tool evidence before the completion gate lets a goal through (advisory audit, enforce gate, durable permits).

Pair a second model that passively reviews each turn and injects notes.

Skill-driven harness/loop engineering workflow agent plugin.

Turns each DSH workspace into a project: every new session auto-injects a project-workspace convention (read code before acting, empty folder means a brand-new project); a per-session Project toggle in the composer downgrades the session to a plain workspace with an explicit notice, with the state kept in the session log.

Kanban board for DSH: task state machine with git branch isolation, agent auto-execution, and review-driven merge.

Per-project scheduled prompts run as fresh headless agent sessions, with one-time, interval, and cron schedules plus durable run history.

Appends ambiguity-handling rules to the dsh system prompt so the agent asks clarifying options before guessing.

Multi-agent math problem-solving & verification framework: brainstorm → solver iteration → multi-verifier debate → Verified knowledge base, with checkpoint resume and manual/auto intervention (also ships as an agent preset).

Visual multi-agent DAG workflows for DeepSeek Harness with live execution, restart recovery, and Feishu integration.

Embeds the active installed Codex Taskboard runtime in the DeepSeek Harness sidebar, using its launcher runtime descriptor instead of a fixed port.

Task board for DSH: create tasks with project and model assignment, run them manually or on cron schedules; new sessions in a project automatically pick up its todo tasks and move them to in-review when done.

Visual node-based workflow canvas for DSH with sub-agent calls, code execution, branching, loops, scheduling, and workflow import/export.

Main-brain orchestration preset: delegation-first persona, root-session execution gate, and portable AI-LAW rule skill.

Config-driven lifecycle hooks, including context injection: declare event, handler and outcome in YAML, where the handler is an in-harness tool (MCP included), a shell command or an HTTP endpoint, and the outcome is model-visible context, a denied tool call, or fire-and-forget.

Deep research as an agent mode: one reviewed loop that plans, researches in adaptive parallel rounds driven by the gaps the researchers themselves declare, synthesises a cited report and then reviews it adversarially, instead of a script the model rewrites per call. Questions the round budget never reached are named in the report rather than dropped.

Auto Mode for DSH: a flash-model router that auto-picks the best agent preset (standard/code/minimal/cordis) for each new session and executes immediately, no confirmation.

ICVO coding-standards kit for DeepSeek Harness: a bundle plugin that registers tools on load and injects the standards only when apply_coding_standards is called, plus a P0 gate CLI (npx dsh-coding-kit) with G1-G7 process commands, skills install and IDE-block refresh.

Calendar and hierarchical task workbench: AI-assisted intake clarification, consultation, decomposition, execution with user acceptance, and review, plus reminders and per-task AI session workspaces.

Captures every plan the agent presents through exit_plan_mode as markdown under <workspace>/.dsh/plans, opens a review window, and keeps a Plans history tab recording the outcome of each plan.