Plugins

Multi-agent orchestration for DeepSeek Harness -- specialized agents, task orchestration, workflow skills

Project documentation scaffolding skill: question-driven setup of project standards, AI-collaboration workflows, and memory discipline across DSH, Reasonix, Claude Code, and Codex.

Interactive canvas for visualizing Agent, Subagent, Workflow, Phase, and tool relationships in DSH Web.

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.

Adds persistent execution contracts, recursive work graphs, critical clarification, and evidence gates for long or underspecified Harness tasks.

Rigorous open mathematics research suite: four agent skills (rigorous-open-math-research, manage-math-research-program, math-research-workflow, lean-verify) for theorem solving with adversarial audit, research program management, pipeline orchestration, and Lean 4 formalization audit; CI-verified tests and mechanical upstream sync.

Evidence-first vulnerability research workbench for DeepSeek Harness: native audit views, 29 OMV tools, Evidence.v1 findings, reproducible runs, Campaign workflows, and isolated PoC evidence.

Evidence-first operating kit: six read-only tools (capability catalog, staged workflow plans, packaged skill reader, bounded local memory search, repository release audit, release checklist) and five packaged skills - plans and audits, never remote writes.

First-class Git tools for the model: status / diff / log / commit / branch with validated messages and paths - no bare-shell git calls.

Remake a viral short video end to end in one workbench: source clip, character sheet behind a human approval gate before any paid render, AI face swap, frame-sample QA and the final cut, plus a media library, a three-track timeline, and any RunningHub workflow connected by its ID.

Multi-agent software engineering and security audit workflow with 8 role presets (Captain, CEO, Designer, Eng Manager, Reviewer/CSO, Builder, QA Lead, Release), 4-Wave DAG scheduling across 6 default concurrency slots (1-12), PostgreSQL Wire Protocol v3 + SQLite WAL fact store, 29 skills, and a docked right-panel web console. A fork of Jueze-2019/dsh-redteam-mode that keeps its package name, mount ids and redteam_* tool names, so the two cannot be installed together. The fact store uses PostgreSQL when REDTEAM_PG_URL, DSH_PG_URL, DATABASE_URL or PGHOST is set in the environment and a local SQLite file under $DSH_HOME/redteam otherwise; role presets are written to $DSH_HOME/.agent-presets/redteam.

Links source material and main-chat instructions, and coordinates translation and refinement tasks with the companion translation plugin. Linked submission needs submissions protocol 1: when the host lacks it, Windows Desktop installs a bundled host patch on first use (a full exit and reopen is required); other platforms need a host that already provides it.

Research workflow suite that registers 10 domain skills and 15 tools, curates 450 bundled research skills into 103 capability cards with an auditable per-skill ledger, and takes over research requests automatically through a system-prompt section. Tools cover real R/Python execution (off by default, two-phase preview then confirm), data gates, figure and docx auditing, .docx generation in Chinese-journal style, live PubMed search, field-level DOI verification, and cross-session project memory.

AI drafts, you review: text, files, screenshots, and whole folders translated with your guidelines attached; scores and comments become AI rewrites. An end-to-end AI translation workflow.

Save reusable prompt snippets with categories and {{placeholders}}; manage them with /snippet commands (add, list, rm, optimize) and insert into the composer from a draggable dock panel, with one-click AI refinement through the default model.

Superpowers skills for the DeepSeek Harness (dsh): a full engineering methodology served through dsh's own filesystem skill provider, with an always-on bootstrap.

Native roundtable panel with a product manager, engineer and critical user, two discussion rounds, follow-up questions, votes, answer retries and summaries.

Resumable JavaScript workflows with native child tasks, shared budgets and worktree isolation.

Finds wasted GitHub Actions runner time and proposes small, reviewable workflow patches, with explicit approval before any edit and a DSH settings page for analysis rules, history limits, storage, and cost estimation.

Read-only Skill inspector for DeepSeek Harness. It shows which Skills a conversation actually loaded — from observed loading evidence only — and which Skills the current environment can discover, and gives each Skill a detail page whose left column switches between the framework, the evidence, validation, evaluation, step evidence, run logic, the before/after of a modification when there is one, the definition facts and the raw SKILL.md. Declaration and run stay apart: the body is split into structure, declared workflow and progressive disclosure, the run logic is read from local receipts, and every declared step carries its own evidence — no observed evidence is reported as exactly that, not as executed or not executed, and the run's instruction hash sits beside the current definition as match, mismatch or unavailable. The evidence view gives each subject one of three states (declared, observed, unavailable), walks definition → load → use → outcome, names what each stage cannot reach, declines to attribute an outcome to the Skill, and can copy the snapshot out as byte-stable JSON. Validation checks the Agent Skills open standard and the platform profiles separately, each finding on a rule id and labelled standard compliance or platform compatibility. Evaluation keeps repeatable cases with the conditions each run was recorded under and the four stages of runtime evidence, comparing two runs of one case item by item without scoring them. The trigger boundary keeps should-trigger and should-not-trigger prompts, has you run them in a real new session, then records what was observed — whether a load record appeared and whether it was model-invoked or user-explicit; a typed /name is not a natural trigger, and an absent load record is reported as not observed, not as not triggered. The same projections are exposed to the agent as a read-only skill_review tool, which returns a structured review packet and says what it omitted when the packet will not fit whole. The inspector, the evidence chain and these reviews are computed on this machine from the definition and the session receipts; the Chinese reading version sends the body through the host's model when you ask for it, keeping code fences, paths and frontmatter verbatim and storing the result on this machine. A Skill can also be cloned into a local copy. It does not write back to the source Skill and does not run its scripts.

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.

Task monitor plus remote chat for DeepSeek Harness. Read sessions, background jobs, subagents, goal rounds, workflows and pending approvals from a phone-friendly page, and talk to any session from it: streamed replies, tool-call progress, stop at any time, switch sessions and models. Also ships a GUI sidebar panel.

Captain-led multi-agent teams that split a goal into tasks and dispatch them along an execution chain to members running in their own sub-sessions, each with its own model.

Process management: brings ntd-style processes (multi-stage, multi-step agent workflow templates) into the dsh web UI — browse, edit, validate, import/export and AI-generate processes; the built-in library is read-only while the personal library is writable with live file sync, and agents read the library through process_* tools and advance work stage by stage.