Best DSH Plan Review Plugins

Preset engineering workflows for DeepSeek Harness: a dev_task tool that gates stage transitions, artifacts, verification, review and commit scope, plus a web workbench for installing project or user skills and rules.

Role-based multi-agent team for DeepSeek Harness — your digital workforce. One goal assembles a team of product, architect, frontend, backend, reviewer and QA roles that deliver through a nine-phase gated pipeline, plus a persistent team you can keep directing across sessions. Zero runtime dependencies, no build step.

Engineering-discipline guard: requirements grill before the first edit, red/green test-evidence gates, forked adversary review, and a delivery report with a per-dimension verification workflow.

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.

Visual Kanban board for DeepSeek Harness with Gitea-backed tasks, workflow columns and a dedicated agent session per task.

Multi-agent orchestration agent preset for DeepSeek Harness: one chief orchestrator that never implements itself — it triages every request, has a PM expert plan the work, and delegates implementation to domain experts via a merged expert roster (list and summon tools), scheduling parallel work through a dependency-DAG taskboard and a file-based message bus, with delivery gated by an independent review. Ships 11 core experts; more expert teams (Agency agents EN/ZH, classic collections) are one-click downloads from the settings page over dual GitHub/CDN channels with sha256 verification. Published on npm as dsh-expert-orchestrator (requires DSH >= 0.1.7-alpha.2).

Advisor - Pair a second model that passively reviews each turn and injects notes. 搭配一个会在每轮对话被动注入见解和审查的副模型。

A governed swarm of six specialist DSH agents — orchestrator, planner, knowledge-base bridge, developer and two reviewers — turns a requirement into a strict phase pipeline where every delivery must prove itself with structured evidence before the next phase may start; reviewers are read-only, code merges only after a review pass, and an append-only event log gives full replay and audit on a live kanban board. Its design draws inspiration from the Hermes Agent kanban.

Coordinates Codex planning and independent review while the original DSH session implements and tests changes.

A quality command center for the DeepSeek Harness (dsh) desktop client: run the iterate review/fix loop natively, follow its convergence dashboard and triage panel live, enforce quality gates, and carry forward what earlier iterations learned.

Agent governance kingdom for DeepSeek Harness: territories, role bindings (bind/unbind/session with model/agent identity), and a plan→assign→execute→review task loop where Worker claims never auto-complete (Claim ≠ Fact), with a local GUI operations console.

Tender search and bid intelligence in DeepSeek Harness with proposed project search, rule screening, human review and Excel/PDF exports using user-authorized Qichacha MCP.

Multi-agent workflow for math-modeling contests: the "UltraMath" preset runs a problem from reading through framework, derivation, coding, verification, paper and review, plus 4 single-stage role presets (mathematician, engineer, writer, reviewer) and 33 model-library skill docs.

Local SQLite taskboard as a native overlay page: projects, tasks, comments, relations, attachments and workflow automation, with backlog → todo → in_progress → in_review → done statuses, `taskboard_*` Agent tools that can only submit work to in_review, a headless JSON CLI and a packaged skill.

Installs a multi-agent novel-writing preset for DeepSeek Harness: one conductor agent dispatches five specialized subagents — world-building, plotting, character management, prose writing, and QA review.

Local-first content production and operations workspace for DSH: manage ideas, scripts, media assets, schedules, goals, publishing status, and post-publication reviews.

QA pipeline as 10 agent skills: requirement analysis, test strategy, case writing and review, E2E (Playwright) and API automation, exploratory, regression scope and bug analysis, backed by a shared knowledge base with format rules, risk model and schema validators.

Adds specification artifacts, skills, commands, goal-backed implementation, and task-progress context.

Agent-driven kanban board: one board per workspace, per-task agent sessions, and a guarded human/agent workflow (Backlog → To Do → In Progress → Review → Done).

Four GitHub workflow skills for PR triage, review feedback, GitHub Actions diagnosis, and safe draft-PR publishing, reusing existing DSH capabilities with gh/git fallbacks.

Drives the GME Test Agent from inside Harness: three tools list interface catalogs, start and repair autonomous test generation in a local Python backend, poll the task until review, and keep PR, skip and cleanup actions behind explicit user consent.

Drives the GME Test Agent from inside Harness: three tools list interface catalogs, start and repair autonomous test generation in a local Python backend, poll the task until review, and keep PR, skip and cleanup actions behind explicit user consent.

Verifiable issue-to-PR delivery chain: an 11-stage pipeline (analysis, search, code understanding, hypothesis, planning, implementation, patch ledger, tests, review, failure classification, PR build) with 4 human review gates, GitHub/GitLab/CodeArts credential connections, a 5-page workbench and a draggable AI assistant panel.

Registers a SpecWorkflow skill pack for requirement clarification, implementation specs, execution, delivery review, repair planning, bug diagnosis, and source-backed research.

Policy-driven Git sync for DSH settings and profile configuration, with secret-aware projections, conflict review, and per-line apply controls.

OMC-style multi-agent orchestration for DeepSeek Harness: 29 omd-* skills and 12 /omd-* slash commands over the plan-execute-review-verify pipeline and team/autopilot/ralph modes.

Literature review stage — search, synthesis, and citation-chain tracking for asset pricing and machine learning research. Ships one skill.

Adversarial review for DeepSeek Harness: put one settled plan on the stand, send read-only red-team experts at it by dimension, then split their findings into single defects you endorse or reject one by one. It never rewrites the plan and never fixes code.

Checks each step of a plan before you approve it, using command rules plus Jev or an OpenAI-compatible endpoint with logprobs, and marks steps that can't be undone, affect things outside the workspace, weren't asked for or are too vague on the plan review card.

OMX-style workflow skills for DeepSeek Harness: deep-interview, ralplan, ralph, autopilot, team, code-review and 19 more, rewritten on native DSH tools, without vision skills.

Six engineering skills for Claude Code and DSH: research, architecture scouting, implementation orchestration, three-axis diff review, complexity audit and mutation testing. Skills delegate through the host’s generic subagent tools when available.

Declares a spec-driven development agent preset with a Chinese persona and four static Claude Code/Codex delegation tools for planning, implementation, review and knowledge distillation; roles are prompt-guided, not an automatic state machine.

AI can write resume text, but templates may still look wrong, pages may overflow, whitespace may remain, and small edits can destabilize the layout. dsh-resume provides an end-to-end DSH workflow for truthful content refinement, template selection, A4 layout tuning, visual review, and tailored resume versions for different job-search scenarios.

Visual round-table meetings for DeepSeek Harness — expert nodes on a ring with directed edges, an aggregation gateway, proxy thinking for black-box worker models, and adversarial review (red-team) of settled plans with per-viewpoint endorse/reject.

Stages exact DSH tool calls without dispatch, presents complete canonical arguments in a durable Batch Review Inbox, invalidates approvals after edits or restarts, and commits only a matching SHA-256 digest plus single-use nonce through the normal DSH tool pipeline.

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

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

Opens the official Plannotator app (plannotator.ai) so you can review the agent's plan — annotate, approve, or send it back.

Multi-agent validation for any repository: independent theses, adversarial critics, and a seal whose verdict is computed from real command exit codes — never from an agent's prose. One body, three runtimes: DeepSeek Harness, Claude Code, ZCode.

Synchronizes DeepSeek Harness third-party plugin lists through S3-compatible storage with diff preview, local snapshots, rollback, and optional LLM provider configuration sync.

21 read-only lifecycle governance tools in one self-contained DSH plugin: task tiering, state/plan/evidence validation, gate precheck and aggregation, release/install audits, context and progress analysis, critical path, resume reconciliation, and a mutation preview that always denies execution. Installs from npm as @240xu/dsh-tech-lead.

Novel-writing workbench for DSH with an authoring preset, outline and character management, manuscript annotations, review workflows, and a managed local Python backend.

Ported from the ARS Claude Code plugin (44k+ stars on GitHub): academic research skills for DeepSeek Harness — a deep-research agent team, a paper-writing pipeline, a multi-perspective peer-review panel, and an end-to-end orchestrator, plus sixteen /ars-* commands.

Runs work as a crew of role agents: a product manager writes the PRD or DoD, waits for your confirmation, then starts architect, engineer, QA and reviewers whose tool sets are locked per role, and shares work through files on disk.

TaskSwarm (蜂群) — DeepSeek Harness 上的多智能体任务编排插件：waves/lanes 并行执行、git worktree 隔离、任务包与跨模型评审、崩溃可恢复 | Multi-agent task orchestration for DeepSeek Harness: waves/lanes parallel execution, git worktree isolation, task packets, cross-model review, crash recovery

Advisor - Pair a second model that passively reviews each turn and injects notes. 搭配一个会在每轮对话被动注入见解和审查的副模型。

Peer-to-peer colleague-agent consultation for DSH: ask one or several configured peers, queue asynchronous questions, discover peers through signed friend cards, and review inbound requests in the Web UI.

A project-agnostic multi-agent collaboration protocol for the DeepSeek Harness: loop guards that stop runaway agent loops, a six-field handoff format, risk-tiered review routing, and a fixed verify→commit→push→review→close sequence. One skill instead of the same rules copy-pasted into every agent's instructions.

Stages exact DSH tool calls without dispatch, presents complete canonical arguments in a durable Batch Review Inbox, invalidates approvals after edits or restarts, and commits only a matching SHA-256 digest plus single-use nonce through the normal DSH tool pipeline.

Long-task orchestration loop: Codex plans/scores/reviews, DeepSeek executes, Kimi supplements (vision acceptance, summarization, cross-check).

Chinese multi-agent workflow with per-role model fallback, versioned Mermaid task plans, independent Agent and Jev reviews, persistent expert collaboration, and pure-function calculation tools.

Advisor reviews that converge over rounds, an engineering mode whose writes require a design token, escalation of a task to a stronger model, and parallel multi-model consultation; a settings page edits the model routes, timeouts and budgets.

A code review workflow that reviews uncommitted changes, changes against a base branch, a single commit, a GitHub pull request, or a file.

Goal lifecycle management for dsh with an interactive kanban board: goals, criteria, attempts and reviews live in the workspace, driven by graph_* tools, with a two-lane board rendered in the conversation view.

Self-media workbench plugin: 8-tab dashboard (daily brief, narrative lines, topics, drafts, knowledge-card distill review, sources, publish, advice) with hard guards (48h window, event dedup, card/line existence, read-only advice) and a read-only intel module following the four red lines.

Capture selected DSH, local MCP, or native WebMCP calls as reviewed, parameterized routines, expose typed tools, and replay with fresh approval.

Security-audit methodology skill pack plus the plugin_vet supply-chain gate: eight agent skills (secret scan, dependency audit, supply-chain review, prompt-injection review, audit orchestration, threat modeling, vuln intel, incident response) in Chinese and English editions, with an npm provider bundle that mounts the skills and registers the automated plugin_vet pre-install scanner.

Agent preset that turns multi-stage objectives into resumable flows: refereed stages, SHA-256 lock identity, an HMAC-signed terminal review gate and event-sourced run logs; ships 15 agent tools.

Adds dangerous-operation policy checks, output redaction, and a security-review workflow.

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.

Dependency-ordered multi-agent team runner for DeepSeek Harness: define roles with their own model, persona and tools, run a node graph with an independent review gate and bounded rework, and watch progress in the web UI.

发现问题(checkup) → 修复交付(fix) → 质量复查(review) 的对抗式闭环插件：基于官方 workflow 引擎的检查/修复/复查工具集

AI is great at doing one file. Ditto is for doing the same thing to 30 files without babysitting all 30. Review-first batch automation plugin for DeepSeek Harness: 3 reviewed samples → full preview → approve → safe apply.

Recurring Excel template fill and reconciliation review: declare the file, sheet, header row, match key and amount columns once, preview every cell write before it lands, compare amounts as exact two-decimal values, list the unmatched rows as exceptions, and export a filled template copy with an HTML report and a JSON trace back to the source rows.

21 read-only lifecycle governance tools in one self-contained DSH plugin: task tiering, state/plan/evidence validation, gate precheck and aggregation, release/install audits, context and progress analysis, critical path, resume reconciliation, and a mutation preview that always denies execution. Installs from npm as @240xu/dsh-tech-lead.

Lifecycle orchestration engine for AI agent teams with task-scoped Git worktree isolation, state-gated delivery transitions, and off-context memory.

DSH 计划批注插件：选中计划原文、逐条批注，并把结构化反馈送回 Agent。 / A DSH plan-review plugin for anchored annotations and structured Agent feedback.

Persistent multi-model squads configured in Settings and selected in the Composer, with per-member model and tool policies. By default the lead Agent completes simple work itself and delegates only when the saved squad is needed; delegated work runs as a bounded DAG with optional review and repair. Run Center tracks retries and per-member tokens from official Harness usage events.

DeepSeek Harness bundle registering one skill that turns long-form production (academic papers, industry analysis, business commentary) into a 9-role pipeline: T1 literature / T2 data / T3 case scouts running in parallel, T4 analyst, T5 writer, T6 critical companion, T7 auditor, T8 finalizer run by the coordinator, T9 peer reviewer — across 6 phases with triangular evidence verification, a 23-check M-gate, a G0-G14 audit including a Chinese AI-trace gate, peer-review scoring with journal matching, and 4 human checkpoints. Installs with `dsh plugin add lunheng-article-pipeline` (package.json#dsh.bundle.patch plus a lib/index.js entry that registers the skill); ships role cards for T1-T9, templates and 11 zero-dependency verification scripts, with five-language READMEs.

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.

Whole-book translation production line for DeepSeek Harness: deterministic chapter detection and segmentation, per-chapter subagent translation, glossary control, full-book review, targeted revision, and auditable meta.json evidence.

Hermes-style skill self-evolution plugin for DeepSeek Harness (DSH): crystallizes reusable agent skills from successful turns via signal-triggered LLM review, progressively improves them, and manages everything in a Settings panel.

DSH self-discipline companion with recurring tasks, a gaze-tracking desktop mascot, evidence logs, final AI review, reminders, and window intervention on Windows and GNOME Wayland.

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

Plan-and-execute orchestration over ReactLoopAgent — turns a goal into reviewable steps with a web UI plan card.

A structured multi-panelist review workflow for the DeepSeek Harness: N panelists score in isolated subagents, blind to each other; a chair reconciles disagreement by anchor match then Δ-level adjudication; an independent critic audits the process. Works for design, code, paper or project review.

AI Look-Up: detects aimless grinding (repeated commands, failure loops, long runs with no output) and injects mid-run self-review nudges, plus a real local-clock anchor so the agent stops estimating elapsed time.

AI-for-Science research workflow skill for DeepSeek Harness, aimed at researchers and grad students using agentic AI: multi-angle literature review with per-angle files, experiment design with a caveat list, figures and paper reading; each job returns files plus a cross-verification brief for a second model. Zero deps, MIT.

Automatically orchestrates complex DeepSeek Harness requests. It detects when delegation is useful, builds a dependency-aware plan, assigns bounded researcher, architect, backend, frontend, tester, and documentation workers, collects structured evidence, and sends the results to a final reviewer. The bundle atomically enables the workflow engine and model-facing subagent tools; disabling the plugin disables its complete tool group as well.

Role-based AI swarms for DeepSeek Harness: an architect plans, parallel builders execute, reviewers gate quality, and an integrator ships. Per-role model pinning with fallback chains, review loops, evidence contracts, human-gated reviews, quota-aware pause/resume, and a live kanban + flow chart in the web GUI. 40 tests; installs from GitHub or npm.

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.

Session-scoped research evidence ledger with sources, linked claims, open questions, and explicit user review; provides a tool, slash command, and conversation panel, with selected-claim Markdown export that omits excerpts, review notes, and local paths by default; does not fetch sources or verify their truth.

GPT plans dependency DAGs, DeepSeek workers execute tasks with adaptive parallelism, and an optional GPT reviewer audits incremental Git diffs and drives repair rounds.