Plugins

Writing council: parallel multi-model drafting, judge consensus & critique synthesis, AI prompt refinement, visual concept illustration, and Markdown file preview.

Auditable token-cost ledger for DeepSeek Harness: wraps the llm/stream waterfall and records every call with provider, model, input/output/cache-hit/cache-write tokens, usage source and integer nano-CNY cost from an explicit price table. Aggregates by hour, day and session; exports JSON or CSV with a no-write dry-run preview; keeps a manually entered official platform bill in a parallel column with actual-minus-estimate diff. Unknown provider/model routes stay explicitly unpriced. Zero runtime dependencies; no balance query, notifications or animations.

Beta: a project supervisor for DeepSeek Harness — standing rules plus a Web panel (three switches, dispatch modes) that splits a goal into parallel teammate dialogs, checks engineering baselines, interviews new-project requirements and protects beginners. The standing rules come from the repo kit installer, an optional separate step.

SOLIDWORKS for DeepSeek Harness: dsh reads, edits, rebuilds, renders and exports the models in a SOLIDWORKS 2016 or later, on the same Windows PC or in a Windows VM on your Mac. Read-only by default; the Windows side decides what may be written.

By default, blocks marking a team task complete without a passed independent verification record whose commit matches the worktree HEAD, and adds a dependency DAG board and a settings page.

In-conversation workflow run timeline for DSH Web: a horizontal rail with phase stations, agent pills, loop arcs and parallel bands replaces the stock workflow tool card and workflow-run chat node. Planned stations render before the run starts via client-side script scanning; status dots and icons follow DSH official primitives.

Investment research bundle: 10 tools (search, page fetch, source audit, claim verification, numeric rigor, channel health, workflow loader, chart engine, PDF renderer, PDF gate) and 23 skills adapted from ai-berkshire. Runs a six-phase workflow where four parallel agents (Duan Yongping, Buffett, Munger, Li Lu) research a company, cross-examine each other, and ship a chart-rich PDF with cover, tables, captions and page numbers. Requires Python 3.10+; the chart and PDF tools also need python-docx and matplotlib.

Fork of fourzkw/dsh-drawai that keeps the editable .drawio canvas and its two agent tools, and adds a second vectorization engine beside the original tracer: hierarchical color clustering with Bezier spline fitting and per-channel color precision, selectable per drawing task in three fidelity levels (measured about 18, 38 and 46 dB PSNR on the same 1920x1080 frame (the upstream engine scores about 26 dB on that frame)), with worker parallelism raised to half the logical cores instead of a cap of four. The original tracer stays the default and remains the right choice for whole videos, where the new engine produces files hundreds of times larger.

Ports the obra/superpowers methodology as 14 native DSH skills — brainstorming, writing-plans, executing-plans, subagent-driven development, test-driven development, systematic debugging, parallel agent dispatch, git worktrees, code review, verification before completion and skill authoring — served through the DSH skills registry with their scripts, references and cross-links intact (frontmatter folded into a manifest, prefixes realigned to DSH skill names). Requires DSH session format v4 (official DeepSeek Harness 0.1.7+): the /superpowers slash command declares the producer-owned source kind plugin:dsh-plugin-superpowers.

Ships a skill about handing work to subagents instead of doing it inline - which tasks are worth fanning out, how many to run at once, how to send them in parallel, and how to check what they returned instead of trusting the summary.

Intelligent sub-agent orchestration for DeepSeek Harness, with model discovery, routing, planning, and parallel delegation.

AnySearch-backed search tools for agents: general and vertical-domain search, parallel batch queries, vertical-domain capability discovery, and page-to-Markdown extraction, plus an API key panel in Settings. It registers its own tools instead of a web_search provider, so it cannot be installed alongside @anysearch/anysearch-dsh.

Multi-role parallel argumentation for DeepSeek Harness: runs the real Codex and Claude CLIs in parallel, has the DSH model (Judge) write a third-party verdict back into the conversation, plus Obsidian-style single-agent direct chat and per-role model config.

Quality suite: background tsc --noEmit diagnostics fed back after the agent edits TypeScript, plus a /code-review command running five parallel review lenses with confidence scoring; absorbs dsh-code-check and dsh-command-code-review.

Multi-agent isolated debate for DeepSeek Harness: debaters of different temperaments research independently (no herding), then cross-examine, then judges converge to a conclusion. Runs as a background job with per-agent progress, token accounting, wave-parallel execution, and session-referenced resume.

System parameter adjustment UI: grouped settings page for timeouts, parallelism, LLM retry policy, model params, web search, permissions and presets; safe profile-config editing with line-level YAML matching and post-write validation.

Parallel cognitive runtime for DeepSeek Harness: heartbeat-scheduled shadow agents that review, fact-check, and work alongside the main agent

dsh plugin that turns the iterate skill into an autonomous closed-loop harness: plan -> parallel review xN -> atomic fixes -> validate -> loop -> auto-stop, plus a dry-run pure-review mode with multi-round convergence and a meta-review that audits the rep

Adversarial code review — parallel finders inspect a diff through separate lenses (correctness, lifecycle, contract, security), then each finding goes to independent verifiers tasked with refuting it, and one refutation drops it.

Adversarial multi-lens code review: parallel correctness/security/maintainability lenses attack a diff, with deterministic severity grading, suppression rules and incremental review history.

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

Nexterm fleet maintenance in a DSH session: 6 agent tools (auth with password/TOTP/device-code login, fleet listing, single-host exec, parallel batch exec, preset health checks, CPU/memory/disk monitoring) over the Nexterm REST API, plus a minimal settings page; no SSH private keys needed on the DSH machine.