MultAI · v0.3.0
Synthesize insights from seven AI platforms with a single command.
MultAI · v0.3.0
One skill. Seven AI platforms. Instant synthesis.
/multai is a Claude Cowork/Code plugin skill that submits your research prompt to Claude.ai, ChatGPT, Microsoft Copilot, Perplexity, Grok, DeepSeek, and Google Gemini simultaneously — then synthesizes the results into structured deliverables. Market landscape reports, capability comparison matrices, product deep-dives, or a direct answer from all seven platforms at once.
How It Works
You → /multai → 7 AI Platforms in parallel → Synthesized report
You type one prompt. /multai figures out what you need, runs it across all platforms, and hands back a consolidated result. No flags, no routing decisions, no platform management.
| Capability | Detail |
|---|---|
| Parallel submission | All 7 platforms run concurrently |
| Intelligent routing | Analyzes your intent and selects the right workflow automatically |
| Market landscape reports | 9-section structured reports — top 20 commercial + OSS, positioning matrices, trends |
| Product deep-dives | Capabilities, integrations, pricing, competitive context, XLSX scoring |
| XLSX comparison matrix | Capability matrix auto-scored and reordered across platforms |
| DEEP mode | Activates Deep Research on each platform where available |
| Rate limiting | Per-platform budget tracking across sessions; never silently skips a platform |
| Agent fallback | Vision-based fallback via browser-use when a UI selector fails |
| Login retry | Real-time sign-in notification; 90-second countdown + automatic retry for platforms that need login |
| Popup dismissal | Auto-accepts browser dialogs; dismisses cookie banners, GDPR notices, and modal overlays |
| Chat readiness | Detects unexpected UI states (error pages, redirects) and hands control to browser-use for recovery |
| Verified install | Playwright import + headless Chromium launch verified on setup; cached for fast subsequent runs |
| Tab reuse | Existing browser tabs reused across runs; --followup continues open conversations |
| Report viewer | Ālo Design System report viewer with light/dark toggle, gradient accents, and interactive charts |
Supported Platforms
| Platform | Notes |
|---|---|
| Claude.ai | Pro plan recommended for DEEP mode |
| ChatGPT | Plus plan for Deep Research |
| Microsoft Copilot | Free tier works |
| Perplexity | Pro for Deep Research |
| Grok | X/Twitter account required |
| DeepSeek | Free tier works |
| Google Gemini | Google account required |
Quick Start
1 — Prerequisites
- Claude Code v1.0.33 or later — check with
claude --version, update withbrew upgrade claude-codeornpm update -g @anthropic-ai/claude-code - Python 3.11+, Google Chrome
2 — Install
# Register the marketplace (one-time):
/plugin marketplace add alo-exp/multai
# Install:
/plugin install multai@multai
Run
/reload-pluginsif/multaidoesn't appear immediately.
Python dependencies (playwright, openpyxl, Chromium) are installed and verified automatically on first session start via a SessionStart hook. The engine confirms Playwright imports correctly and Chromium launches headlessly — no manual setup required.
Agent fallback (optional): For the vision-based
browser-usefallback:bash "$(find ~/.claude/plugins/cache -name setup.sh | head -1)" --with-fallback
Alternative — Local / Dev Install
git clone https://github.com/alo-exp/multai.git
cd multai
bash setup.sh # creates .venv, installs deps + Playwright Chromium
# optional agent fallback:
bash setup.sh --with-fallback
claude --plugin-dir ./multai
3 — Log in to platforms
Open Chrome and sign in to each platform. The engine reuses your existing Chrome profile — no credentials are stored.
4 — Set optional API keys
# ~/.zshrc or ~/.bashrc
export GOOGLE_API_KEY="..." # free from aistudio.google.com — enables Gemini agent fallback
export ANTHROPIC_API_KEY="..." # from console.anthropic.com — enables Claude agent fallback
5 — Use the skills
/multai — research, landscape analysis, direct multi-AI queries, and matrix operations:
/multai Run a market landscape analysis on DevOps platforms for SMBs
/multai Research humanitec.com
/multai Add Harness to the comparison matrix
/multai What are the main trade-offs between Rust and Go for backend services?
/comparator — standalone head-to-head comparisons without a prior research run:
/comparator Compare Humanitec vs Port.io
/comparator Which is better for a startup — Backstage or Cortex?
/comparator Compare these two products and give me a weighted score
/consolidator — merge any set of content sources into a unified, structured report:
/consolidator Consolidate these three research papers into a summary report
/consolidator Summarize these five customer interview transcripts into themes
/consolidator Combine these meeting notes from four teams into a single overview
All skills announce their plan before acting — you can always override or adjust.
What /multai Can Do
Market landscape reports
"Run a landscape analysis on API gateway platforms" "Give me a market map for observability tools for startups"
Produces a 9-section structured Market Landscape Report: market definition, size & CAGR, competitive positioning (2×2, Wave-style, Value Curve), key trends, top 20 commercial + OSS solutions, buying guidance, and future outlook.
Output: reports/{task-name}/{Category} - Market Landscape Report.md + auto-launched browser preview
Product deep-dives
"Research humanitec.com" "Evaluate Backstage" "Analyze Port.io — how does it compare to Cortex?"
Deep research on a specific product — capabilities, integrations, pricing, competitive context — optionally scored in the comparison matrix.
Output: reports/{task-name}/{Product} - Consolidated Intelligence Report.md
Head-to-head comparisons — /comparator
"Compare Humanitec vs Port.io" "Which is better for SMBs — Backstage or Cortex?" "Compare these two products and score them"
Standalone skill for comparing any two (or more) solutions. Derives a capability framework from available evidence (CIRs, documents, or LLM knowledge), optionally lets you set feature priorities, scores each solution with priority-weighted ticks, and produces both an XLSX matrix and a readable Markdown summary with per-category winners and key differentiators. No prior research run required — works from LLM knowledge alone if needed.
Output: reports/{domain}/{domain}-matrix.xlsx + reports/{domain}/{task-name}-comparison-summary.md
Can also be triggered via /multai — it routes automatically when comparison intent is detected.
Multi-source consolidation — /consolidator
"Consolidate these three research papers into a summary" "Summarize these five customer interviews into themes and recommendations" "Combine these meeting notes from four teams into one overview"
Standalone skill for synthesizing content from any set of sources — documents, transcripts, notes, URLs, pasted text, or AI platform responses — into a unified, well-structured report. Detects the content type and auto-derives an appropriate report structure (research synthesis, theme extraction, decision log, etc.), or follows a consolidation guide you provide.
When invoked from within a /multai workflow, operates in AI-Responses mode and produces a CIR (Consolidated Intelligence Report) from raw platform outputs.
Output: [Topic] - Consolidated Report.md (generic) or [Topic] - Consolidated Intelligence Report.md (AI-Responses mode)
Comparison matrix operations
"Add Harness to the comparison matrix" "Update the score for Cortex on the developer portal capability" "Reorder the matrix by score"
Maintains an existing XLSX capability matrix — adding platforms, updating scores, applying combo columns, reordering, and verifying coverage.
Direct multi-AI queries
"What are the emerging consensus patterns for LLM memory management?" "Summarize the current state of WebAssembly for server-side workloads"
For anything that isn't a landscape, deep-dive, or matrix operation, /multai submits directly to all 7 platforms and synthesizes a consolidated answer.
Project Structure
multai/
├── .claude-plugin/
│ ├── plugin.json ← Plugin manifest
│ └── hooks.json ← SessionStart hook (auto-installs deps)
├── skills/
│ ├── orchestrator/ ← /multai skill — router + engine owner
│ │ ├── SKILL.md
│ │ ├── platform-setup.md
│ │ └── engine/ ← Playwright automation engine
│ │ ├── cli.py ← CLI entry point & arg parsing
│ │ ├── orchestrator.py ← Parallel dispatch coordinator
│ │ ├── engine_setup.py ← Chrome/CDP launch & lifecycle
│ │ ├── tab_manager.py ← Tab creation, reuse & cleanup
│ │ ├── prompt_loader.py ← Prompt loading & echo-sig extraction
│ │ ├── status_writer.py ← status.json serialisation
│ │ ├── retry_handler.py ← Per-platform retry & error classification
│ │ ├── config.py
│ │ ├── rate_limiter.py
│ │ ├── agent_fallback.py
│ │ ├── collate_responses.py
│ │ └── platforms/ ← per-platform automation classes
│ │ ├── base.py
│ │ ├── inject_utils.py ← prompt injection helpers
│ │ ├── browser_utils.py ← navigation & popup handling
│ │ ├── chatgpt_extractor.py ← ChatGPT extraction mixin
│ │ └── claude_ai.py chatgpt.py copilot.py grok.py …
│ ├── consolidator/ ← /consolidator skill — multi-source synthesis + CIR
│ ├── landscape-researcher/ ← Market landscape workflow (internal)
│ ├── solution-researcher/ ← Product deep-dive workflow (internal)
│ └── comparator/ ← /comparator skill — head-to-head comparisons + XLSX matrix
├── domains/ ← Shared domain knowledge (enriched per run)
├── reports/
│ └── preview.html ← Report viewer
├── docs/ ← Architecture, SRS, test & CI/CD plans
├── tests/ ← pytest suite
├── setup.sh ← Bootstrap — venv, deps, Playwright Chromium
├── pyproject.toml
├── requirements.txt
├── USER-GUIDE.md
└── CONTRIBUTOR-GUIDE.md
Rate Limiting
The engine tracks per-platform usage across sessions and warns when a budget is low, but never skips a platform based on budget alone. A platform is excluded from a round only if:
- A sign-in page is detected (
needs_login— 🔑) - The platform is unreachable (network error)
- Actual quota exhaustion is detected on-page
Resilience
Agent Fallback
When a Playwright selector fails, a browser-use vision agent takes over automatically:
ANTHROPIC_API_KEYset → Claude Sonnet is the agent LLMGOOGLE_API_KEYset → Gemini 2.0 Flash (free tier at aistudio.google.com)- Neither key → fallback disabled; Playwright exception propagates
If all Playwright steps fail for a platform, a full agent-driven run is attempted as a last resort.
Login Handling
When a platform requires sign-in, the engine notifies you immediately (not after all platforms finish) and retries automatically after a 90-second countdown. No manual re-runs needed.
Popup & Dialog Handling
Browser alert()/confirm() dialogs are auto-accepted. CSS overlays (cookie banners, GDPR notices, sign-up modals) are dismissed automatically via scoped selectors targeting modal and consent containers. The engine handles up to 3 layered popups per lifecycle step.
Chat Readiness Check
Before interacting with any platform, the engine verifies the chat UI is in the expected state — checking for sign-in redirects, HTTP error pages (404, 500, 502, 503), and blank tabs. If the UI is unexpected and browser-use is available, the agent takes over to navigate back to the chat interface.
Documentation
| Document | Description |
|---|---|
USER-GUIDE.md | Installation, usage, viewing reports |
CONTRIBUTOR-GUIDE.md | CLI flags, platform internals, tests, CI/CD |
docs/Architecture-and-Design.md | System topology and design decisions |
docs/SRS.md | Software Requirements Specification |
CHANGELOG.md | Version history |
Requirements
| Requirement | Version |
|---|---|
| Python | ≥ 3.11 |
| Google Chrome | latest |
| Claude Code | ≥ v1.0.33 |
License
MIT — see LICENSE.