Workflow & Automation

Durable DAG workflows for DeepSeek Harness with Agent authoring tools, SQLite persistence, resumable runs, and a visual Canvas Studio.

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

A self-evolving plugin for DeepSeek Harness: it observes how the agent runs, proposes improvements to its own assets via the LLM, validates each proposal inside a sandboxed trial agent, and applies only verified mutations — with a versioned ledger and automatic rollback on regression.

Local-first AI project orchestration workbench and CLI plugin for DeepSeek Harness: approval-gated planning, Git worktrees, task execution, Issues, and auditable evidence.

Interval heartbeat that wakes each root agent itself (followup injection) to proactively report progress, risks or blockers: busy-safe queuing, no stacking, auto-pauses when no human replies, re-arms on the next user message.

Full local-first issue board embedded in the DSH web GUI: Kanban / list / Gantt / workflow / dashboard views, AI chat per task, attachments and labels, auto-claim of todo tasks driven by dsh-routines — data lives in local SQLite (dsh plugin add @ttmouse/dsh-taskboard).

Google Antigravity CLI (agy) as a DSH product subagent: delegate tasks to a Gemini coding agent with your Antigravity login (no API key), with conversation follow-ups, background streaming progress, image analysis by path, and a pop-up login window.

Publish articles to CSDN / Juejin / CNBlog directly from the session, no external server.

Folio (兰亭) @nyantused/folio-dsh-tools: 15 schema-validated tools (memory + quality gates) plus an L0 guard of the consulting document-generation engine; pair with @nyantused/folio-dsh-events for the full session protocol.

Folio (兰亭) @nyantused/folio-dsh-events: session-protocol events — auto session-start reminder + auto-save on session close; pair with @nyantused/folio-dsh-tools.

Proactive scheduling: an adaptive heartbeat plus a value network (urgency, relevance, interruption cost) decides when the agent should speak unprompted.

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.

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

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

Feynman learning-mode plugin: teach → teach-back → judge → re-explain loop rendered as rich HTML lesson cards (mermaid diagrams + shiki code highlighting).

DSH port of TaskPlane: dependency-ordered waves run in parallel git-worktree lanes, with task packets, cross-model review, and crash recovery.

Loopback-first multi-agent message relay: HMAC-authenticated broker plus dsh plugin (relay_send/recv/peers/history), zero-dependency CLI and Python clients, wire protocol v1.0.

Task planning with experience muscle-memory: condition-reflex recall of past solutions, LLM capability matching, and auto-persisted lessons.

Host-level proactive wake-ups for DeepSeek Harness: let the model schedule its own alarms (once / interval / cron with random jitter), wake cold sessions on time, gate model-initiated follow-ups with quiet hours and a daily budget, and reclaim silent wake

Ultra-lean daily coding agent preset for DeepSeek Harness (dsh) that strips heavy orchestration for fast, direct tool execution.

QA orchestrator plugin for DeepSeek Harness: an agent explores your app and leaves evidence, then the explored path replays deterministically. Drives the browser, macOS desktop, iOS and Android drivers.

A faster, more efficient, and more accurate coding Agent preset. On 100 hard SWE-bench Pro tasks selected using multidimensional difficulty criteria, average elapsed time decreased by 36%, average input tokens per task decreased by 17%, and accuracy increased by 5 percentage points.