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GooDAnDReaDY /

GooDAnDReaDY/dsh-agent-orchestrator

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Multi-agent task decomposition, DAG workflow orchestration, and prompt caching optimizer for DeepSeek Harness

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📦 @goodandready/dsh-agent-orchestrator

Multi-Agent Task Decomposition, DAG Workflow Orchestration & Prompt Caching Engine for DeepSeek Harness

npm version license DSH Plugin Node version

All Author Projects

🇬🇧 English • 🇷🇺 Русский • 🇨🇳 中文说明

⭐ If you like this plugin, please star it on GitHub — it shows me that the plugin is useful to you and motivates me to keep developing it.

🐛 If you find a bug or would like to request a feature, open a GitHub issue in any language — I will review your proposal and implement useful suggestions in a future plugin version.

⚡ Overview & The Problem

Single-agent software engineering architectures suffer from cognitive overload when tasked with complex multi-stage projects: monolithic prompts conflate architecture, styling, core business logic, testing, and documentation into a single generation pass, leading to hallucinated contracts, regression bugs, and excessive token expenditure.

Furthermore, executing multiple subagents independently often resets the LLM KV-cache on each turn, forfeiting prefix cache reuse and incurring substantial latency and financial overhead.

@goodandready/dsh-agent-orchestrator introduces an autonomous multi-agent orchestration framework to DeepSeek Harness:

  1. Intelligent Triage & Decomposition: Analyzes high-level objectives from chat or Kanban cards and breaks them down into fine-grained stages across 12 specialized agent roles.
  2. DAG Execution Engine: Schedules tasks based on Directed Acyclic Graph dependencies, running independent stages concurrently while strictly enforcing blocker gates.
  3. KV-Cache / Prompt Caching Optimizer: Guarantees byte-level prefix invariance for subagents sharing identical models, unlocking 80–90% prompt token cache hits and near-instant TTFT.
  4. Strict Separation of Duties: Backend implementation, UI interface design, and frontend client assembly are strictly isolated into distinct personas and execution stages.
  5. Dual Surface Integration: Dispatched natively via /orchestrate in DSH chat (with a sticky header milestone card) or via @goodandready/dsh-kanban task boards.

🏗️ Architecture

graph TD
    Trigger["Input Task<br/>(/orchestrate in Chat or Kanban Card)"] --> Main["Lead Orchestrator (Triage)"]
    
    subgraph Engine ["DAG Engine & Prompt Caching"]
        L1["Layer 1: Canonical Base Anchor (>1024 tokens)"]
        L2["Layer 2: Shared Task Anchor"]
        L3["Layer 3: Cumulative Artifacts (Append-Only)"]
        L4["Layer 4: Role Suffix Directive"]
    end

    Main --> Engine

    subgraph AgentPool ["Configured Agent Personas (Self-Contained in Settings)"]
        R1["Technical Spec Analyst"]
        R2["System Architect"]
        R3["UI/UX Interface Designer"]
        R4["Backend Developer"]
        R5["Frontend Developer"]
        R6["QA Automation Specialist"]
        R7["Documentation Specialist"]
    end

    Engine --> AgentPool
    AgentPool --> Delivery["Orchestrated Delivery<br/>(Header Utility Card & Kanban Sync)"]

👥 12 Built-In Specialized Agent Roles

All agent profiles are completely self-contained within plugin settings (no external file dependencies):

Role ID Title Specialization Strict Boundaries
spec Technical Spec Analyst Requirements, Acceptance Criteria (DoD), Schemas Never writes implementation or styling
architecture System Architect System Design, DESIGN.md, ADR, Modular Contracts Never implements production code or deploys
ui_design UI/UX Interface Designer Layouts, Theme Tokens (--dsw-alias-*), Slots Never writes backend Cordis services
frontend Frontend Developer React Components, Client Hooks, DOM Events Never alters backend routes or DB schemas
backend Backend Developer Cordis Services, WebServer Routes, Data Store Never writes client React JSX or styles
fullstack Fullstack Integrator Client-Server Contract Wiring, End-to-End Flow Adheres strictly to modular limits
qa_tests QA Automation Specialist Unit Tests (node:test), Boundary Verification Verifies without external network calls
bugfix Hotfix & Triage Engineer Root-Cause Diagnosis, Minimal Blast Radius Fixes Never refactors unrelated code
docs Documentation Specialist Trilingual Documentation (en/ru/zh), Releases Never overwrites previous documentation
refactoring Refactoring Specialist Complexity Reduction (YAGNI), Bundle Compression Preserves backwards compatibility
research Research & Spike Engineer Technology Evaluation, Library Trade-Offs Delivers analysis; never merges spike code
devops DevOps & Tooling Engineer Package Manifests, Build Verification, Systemd Never exposes private network credentials

🔄 Complexity Scenarios

  1. Hotfix / Trivial (1 Stage): Instant defect elimination or single-parameter tweak.
  2. Simple (2 Stages): Discussion & Spec $\rightarrow$ Targeted Execution.
  3. Medium (3–4 Stages): Spec $\rightarrow$ UI Design $\rightarrow$ Frontend Code $\rightarrow$ QA Tests.
  4. Complex (5–6 Stages): Spec $\rightarrow$ Architecture $\rightarrow$ UI Design $\rightarrow$ Implementation $\rightarrow$ QA $\rightarrow$ Trilingual Docs.
  5. Enterprise / Deep R&D (7 Stages): Spike Research $\rightarrow$ Spec $\rightarrow$ Architecture $\rightarrow$ Parallel Backend & UI Design $\rightarrow$ Frontend Assembly $\rightarrow$ Comprehensive QA $\rightarrow$ Documentation Gate.
  6. Custom DAG Scenarios: Fully configurable in plugin settings with custom stages and blocker checkboxes.

⚡ Prompt Caching Mechanics

Modern LLMs (DeepSeek-V3, Claude 3.5 Sonnet, vLLM) cache prompt KV states strictly from the first token forward. If non-deterministic timestamps or random IDs are placed in the prompt header, cache hit rate drops to 0%.

dsh-agent-orchestrator enforces a 4-layer canonical layout:

  1. Layer 1: Static Base Anchor (>1024 tokens): Byte-identical guidelines and tools definition common across all agents.
  2. Layer 2: Shared Task Anchor: Stable description of user objective and target repository.
  3. Layer 3: Cumulative Context (Append-Only): Outputs of predecessor stages appended in a deterministic sequence, preserving 100% of the preceding KV-cache.
  4. Layer 4: Role Directive (Suffix): Role persona prompt, skills, and subtask-specific scope appended at the end.

This architecture delivers 80–95% cache hits across subagents utilizing the same model, reducing TTFT and cutting token costs by ~90%.


💻 Usage

1. In DSH Chat via Slash Command

/orchestrate Design and build a settings card for the finance plugin

Explicit scenario selection:

/orchestrate complex Build a multi-tenant authentication provider
/orchestrate hotfix Fix null reference in store.js

Short alias:

/orc Refactor state management

2. In @goodandready/dsh-kanban

  • Open any card on the board.
  • Click [Собрать пайплайн / Assemble Pipeline].
  • Select the complexity preset or allow auto-triage.
  • The card automatically reflects stage transitions and advances to Review upon completion.

🧪 Verification & Automated Testing

Execute the native test suite (121 passing tests across 53 suites, zero network dependencies):

node --test test/*.test.mjs

Verify npm package bundle size compliance (<256 KiB threshold):

npm pack --dry-run --json

Visual verification

Production acceptance of v0.1.6 — Settings card, Dark and Light themes side by side:

dsh-agent-orchestrator v0.1.6 visual verification


📄 License

MIT © GooDAnDReaDY

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