📦 @goodandready/dsh-moa
Mixture of Agents (MoA) Multi-Model Collaboration & Synthesis Engine for DeepSeek Harness
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🇨🇳 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.
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⚡ Overview & The Problem
Single-model AI generation often suffers from blind spots, single-perspective biases, hallucinated architectural choices, and inconsistent code quality on challenging engineering tasks. When prompted with ambiguous or complex specifications, a single model may make premature assumptions and produce monolithic, unvetted implementations.
@goodandready/dsh-moa brings the Mixture of Agents (MoA) architecture natively to DeepSeek Harness via the /moa slash command:
- Adaptive Clarification Questionnaire: For broad or underspecified prompts, advisor models formulate clarifying options and the judge synthesizes a structured 2–4 question questionnaire before generating code.
- Parallel Proposers Fan-Out & Workspace Isolation: Multiple independent models evaluate the prompt concurrently. Each candidate's proposed files are written to isolated disk sandboxes (
.moa/candidate-N/), avoiding cross-pollution.
- Frontier Judge Evaluation & File Promotion: A flagship reasoning model critically benchmarks all proposals, selects the winning candidate via machine markers (
WINNER_CANDIDATE_INDEX: N), and promotes the winner's files directly into the project root directory.
- Token-Saving Chat Summarization: Replaces massive code dumps in chat bubbles with compact file listings and clean architectural summaries.
- One-Shot Session Model Restoration: Executes cleanly as a one-shot turn modifier, automatically reverting back to the user's primary session model immediately after completion.
- Dynamic Model Pricing Catalog & Token Estimation: Real-time rate resolution for 300+ models fetched automatically in the background from OpenRouter's public catalog (cached locally in
~/.dsh/storages/dsh-moa-catalog.json for 24h), plus support for direct vendor rates and custom prices overrides in settings.yaml.
- Refinement Mode (Incremental Edits): Automatically detects existing codebase context to generate precise delta modifications instead of destructive full-file rewrites.
- Fast Mode & Custom Judge Criteria: Ultra-fast single-model preset for quick tasks and customizable evaluation guidelines for the judge.
- Run History & Win-Rate Leaderboard: Persistent logging of every run kind (synthesis, fast mode, questionnaire) with built-in REST endpoints (
/dsh-moa/history, /dsh-moa/leaderboard, /dsh-moa/runs/<id>).
- Live Canvas 1-Click Preview (optional): when the
@goodandready/dsh-live-canvas plugin is installed in the same profile, the promoted HTML is pushed to its sandbox and the MoA answer carries a one-click preview link; without it the step is skipped silently.
🏗️ Architecture
graph TD
subgraph Input ["User Interaction (Chat Composer)"]
Cmd["Slash Command: /moa [preset] <prompt>"]
Gate{"Ambiguity Check & Questionnaire"}
QModal["Interactive Clarifying Questions<br/>(Options & Write-in responses)"]
end
subgraph Proposers ["Parallel Proposer Layer (Advisors)"]
P1["Proposer Model 1<br/>(Creative Approach)"]
P2["Proposer Model 2<br/>(Alternative Design)"]
P3["Proposer Model 3<br/>(Performant Strategy)"]
WS1[".moa/candidate-1/<br/>(Isolated Files)"]
WS2[".moa/candidate-2/<br/>(Isolated Files)"]
WS3[".moa/candidate-3/<br/>(Isolated Files)"]
end
subgraph Judge ["Synthesis & Promotion Layer"]
Aggregator["Frontier Judge Model<br/>(Cross-Evaluation & Code Critique)"]
WinnerMarker{"WINNER_CANDIDATE_INDEX"}
Promote["Promote Winner Files<br/>(Move to project root & cleanup sandboxes)"]
Summary["Token-Saving Summary<br/>(File overview & architecture highlights)"]
end
Cmd --> Gate
Gate -->|Broad/Underspecified| QModal
QModal -->|User Answers| P1 & P2 & P3
Gate -->|Explicit/Detailed| P1 & P2 & P3
P1 --> WS1
P2 --> WS2
P3 --> WS3
WS1 & WS2 & WS3 --> Aggregator
Aggregator --> WinnerMarker
WinnerMarker --> Promote
Promote --> Summary
✨ Features & Capabilities
1. Slash Command (/moa) & Autocompletion
Integrated directly into the DeepSeek Harness composer via client input triggers. Typing /moa shows presets and instant autocompletion:
/moa build a real-time reactive dashboard with charts and websocket updates
Or target a specific named preset:
/moa code-review audit the auth middleware and security boundaries
The flag form is equivalent:
/moa --preset=deep-reasoning solve this math problem step by step
2. Adaptive Questionnaire Gate
When prompts are open-ended or lack architectural specifications (e.g. "build a calculator app"), advisor models detect ambiguities and formulate focused clarifying questions (e.g., UI style, persistence backend, framework choice) before generating code.
3. Parallel Fan-Out with Live Heartbeats
- Proposers query concurrently with live heartbeat progress badges (
⏳ [3s] Processing..., per-model completion status).
- Bulky system prompts and tool schemas are cleanly stripped from advisor contexts, eliminating "missing tools" refusals and token bloat.
4. Disk-Level Candidate Isolation & Promotion
Unlike standard chat-only MoA, dsh-moa isolates file generation onto the filesystem:
- Each proposer generates files into
.moa/candidate-1/, .moa/candidate-2/, etc.
- The Judge compares implementations and selects the optimal solution with
WINNER_CANDIDATE_INDEX: N.
- The winner's files are promoted to the workspace root, and temporary candidate directories are pruned automatically.
5. Native Settings Card & Presets
Configure your models in Settings → Plugins → Mixture of Agents:
- Set custom Proposer models (e.g., fast generative models for diverse ideas).
- Set the Aggregator / Judge model (e.g., deep reasoning models for rigorous critique).
- Configure named presets (
default, fast, deep-reasoning), judge criteria and temperatures.
- Enable or disable MoA and see the real host status chip; the telemetry grid shows total runs and average run cost.
6. Live Canvas 1-Click Preview (optional)
If @goodandready/dsh-live-canvas is installed in the same profile, dsh-moa pushes the promoted HTML file to the Live Canvas REST contract (POST /dsh-live-canvas/api/preview, served by the same harness webServer) and appends a one-click preview link (/dsh-live-canvas/sandbox/<id>) to the answer. Without the plugin the step is skipped silently — no errors in the log, no dead links.
📦 Installation
Install into your DeepSeek Harness web profile:
dsh plugin --profile web add @goodandready/dsh-moa
Restart your DeepSeek Harness instance and refresh the browser.
⚡ 10 Specialized Built-in Presets & Candidate Personas
v0.2.13 introduces 10 ready-to-use presets engineered for real-world software workflows:
| Preset Name | Purpose | Default Aggregator | Peer Critique | Blind Eval |
|---|
default | Balanced multi-model generation | codex:gpt-5.6-sol | Optional | Off |
code-review | Thorough peer review & vulnerability detection | codex:gpt-5.6-sol | On | On |
fast-audit | Ultra-fast single-model audit (Fast Mode) | codex:gpt-5.6-sol | Off | Off |
deep-architect | Distributed systems & complex architectures | codex:gpt-5.6-sol | On | Off |
bug-hunter | Root cause discovery & adversarial edge cases | codex:gpt-5.6-sol | On | Off |
refactor-cleanup | Dead-code pruning & standard-library simplicity | codex:gpt-5.6-sol | Off | Off |
frontend-ui | High-fidelity responsive web interfaces | codex:gpt-5.6-sol | Off | Off |
security-audit | Zero-trust threat analysis & sanitization | codex:gpt-5.6-sol | On | On |
math-logic | Deterministic algorithmic proofs & math logic | codex:gpt-5.6-sol |
Candidate Personas (role_persona)
Assign archetypal engineering mentalities to individual candidate slots to ensure genuine perspective divergence:
minimalist (Ponytail Senior): standard library first, zero external dependencies, minimal moving parts.robustness: defensive coding, boundary validation, graceful fallback handling, idempotent operations.performance: algorithmic complexity minimization, memory efficiency, zero-copy operations.tester: test-driven methodology, high branch coverage, explicit assertion design.general: balanced standard engineering approach.
🤝 Consilium Round 2 (Peer Critique) & Syntax Auto-Fix Gate
- Consilium (Round 2): Enable
peer_critique_enabled: true in preset settings. Each candidate receives peer proposals and submits an improved, hardened iteration before judge evaluation.
- Syntax Pre-Check Gate: In-memory JS/MJS and JSON syntax verification runs automatically on all candidate files. If a proposal contains syntax errors, it is flagged with
[⚠️ Syntax Warning] and the judge receives a strict mandate: if this candidate has superior design, auto-correct the syntax in the synthesized deliverable and award them the win.
- User Candidate Override: Enable
allow_candidate_override: true to preserve candidate sandboxes in .moa/candidate-N/. At any time, promote any candidate using /moa promote <runId> <candidateIndex> or the UI button.
⚙️ Configuration (settings.yaml)
Configure presets and model pipelines in settings.yaml or through the Web UI Settings panel (Settings → Plugins → Mixture of Agents):
# settings.yaml
dsh-moa:
enabled: true
default_preset: "default"
prices:
"my-provider/my-model":
input: 0.20
output: 0.80
"ollama/*":
input: 0
output: 0
presets:
- name: default
ask_clarifying_questions: true
reference_models:
- provider: "your-fast-provider"
model: "your-creative-model"
- provider: "your-fast-provider"
model: "your-balanced-model"
aggregator:
provider: "your-reasoning-provider"
model: "your-judge-model"
reference_temperature: 0.6
aggregator_temperature: 0.4
max_tokens: 4096
judge_criteria: ""
- name: fast
ask_clarifying_questions: false
reference_models:
- provider: "your-fast-provider"
model: "your-fast-model"
aggregator:
provider: "your-fast-provider"
model: "your-fast-model"
Configuration Parameters
| Parameter | Type | Default | Description |
|---|
enabled | boolean | true | Master switch for the /moa command, turn routing and POST /dsh-moa/run (editable in the settings card) |
default_preset | string | "default" | Preset invoked when typing /moa <prompt> without an explicit preset |
presets | array | [...] | Named presets; selected via /moa <name> <prompt> or /moa --preset=<name> <prompt> |
presets[].reference_models | array | [...] | Proposer models queried concurrently during the proposal phase |
presets[].aggregator | object | {...} | Judge model responsible for synthesis, critique, and winner selection |
presets[].ask_clarifying_questions | boolean | true | Synthesize a clarifying questionnaire for broad/underspecified prompts (per preset) |
presets[].curator_synthesis | boolean | false | Curator mode: evaluates strongest parts across candidates using the antipatterns rubric and advises an assembler model |
presets[].stream_aggregator | boolean | true | Stream judge/aggregator tokens live in real-time with zero TTFT wait |
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Privacy note: in refinement mode, readable project files (up to ~16k characters; dotfiles such as .env* are excluded) are included in the prompts sent to the configured candidate and judge providers. Avoid running /moa in projects whose non-dotfile files contain secrets.
📊 REST API & Endpoints
| Endpoint | Method | Description |
|---|
/dsh-moa/status | GET | Health/enablement snapshot used by the settings card status chip |
/dsh-moa/presets | GET | Returns the configured MoA presets and default preset |
/dsh-moa/presets | POST | Replaces presets/default preset/enabled after schema validation (400 on invalid payload) |
/dsh-moa/models | GET | Lists models available for candidate/judge slots |
/dsh-moa/history?limit=20&offset=0 | GET | Returns recent MoA runs with candidates, winner, cost, and tokens |
/dsh-moa/leaderboard | GET | Computes model win-rate leaderboard and average execution costs |
/dsh-moa/runs/<id> | GET | Returns a single recorded run by id |
/dsh-moa/run | POST | Runs the full MoA pipeline over HTTP (400 when enabled: false) |
🧪 Testing
Run the automated test suite:
npm test
🛠️ Internal Tooling & Development
For local verification and package integrity validation:
npm test: runs the full test suite (95 tests)./deploy.sh: local infrastructure validation script (verifies package size, identity parity in package.json/cordis.patch.yml/client.js, and tests). Excluded from the published npm package.
📄 License
MIT © GooDAnDReaDY
