panopticon-cli
Advanced tools
Multi-agent orchestration for AI coding assistants (Claude Code, Codex, Cursor, Gemini CLI)
Multi-agent orchestration for AI coding assistants
"The Panopticon had six sides, one for each of the Founders of Gallifrey..."
| Without Panopticon | With Panopticon |
|---|---|
| Manually juggle multiple AI agents | Automatic orchestration - spawn, monitor, and coordinate agents from a dashboard |
| Agents start fresh every session | Persistent context - skills, state files, and beads track work across sessions |
| Simple tasks eat Opus credits | Smart model routing - Haiku for simple, Sonnet for medium, Opus for complex |
| Stuck agents waste your time | Automatic recovery - detect stuck agents and hand off to specialists |
| AI tools have separate configs | Universal skills - one SKILL.md works across Claude, Codex, Cursor, Gemini |
 |  |  |
| Start planning | Discovery phase | Active session |
| Feature | Description |
|---|---|
| Multi-Agent Orchestration | Spawn and manage AI agents in tmux sessions via dashboard or CLI |
| Cloister Lifecycle Manager | Automatic model routing, stuck detection, and specialist handoffs |
| Universal Skills | One SKILL.md format works across all supported AI tools |
| Workspaces | Git worktree-based feature branches with Docker isolation |
| Convoys | Run parallel agents on related issues with auto-synthesis |
| Specialists | Dedicated review, test, and merge agents for quality control |
| Heartbeat Monitoring | Real-time agent activity tracking via Claude Code hooks |
| Mission Control | Unified monitoring view — see all active features, agent activity, and planning artifacts at a glance |
| Shadow Engineering | Monitor existing workflows before transitioning to AI-driven development |
| Real-Time Dashboard | Socket.io push with multi-layer caching (in-memory + SQLite) for instant loads |
| Legacy Codebase Support | AI self-monitoring skills that learn from your codebase |
| Tool | Support |
|---|---|
| Claude Code | Full support |
| Codex | Skills sync |
| Cursor | Skills sync |
| Gemini CLI | Skills sync |
| Google Antigravity | Skills sync |
"AI works great on greenfield projects, but it's hopeless on our legacy code."
Sound familiar? Your developers aren't wrong. But they're not stuck, either.
AI coding assistants are trained on modern, well-documented open-source code. When they encounter your 15-year-old monolith with:
snake_case, some camelCase, some SCREAMING_CASE)processUser() function directly")accounts table is actually users")...they stumble. Repeatedly. Every session starts from zero.
Panopticon includes two AI self-monitoring skills that no other orchestration framework provides:
| Skill | What It Does | Business Impact |
|---|---|---|
| Knowledge Capture | Detects when AI makes mistakes or gets corrected, prompts to document the learning | AI gets smarter about YOUR codebase over time |
| Refactor Radar | Identifies systemic code issues causing repeated AI confusion, creates actionable proposals | Surfaces technical debt that's costing you AI productivity |
Session 1: AI queries users.created_at → Error (column is "createdAt")
→ Knowledge Capture prompts: "Document this convention?"
→ User: "Yes, create skill"
→ Creates project-specific skill documenting naming conventions
Session 2: AI knows to use camelCase for this project
No more mistakes on column names
Session 5: Refactor Radar detects: "Same entity called 'user', 'account', 'member'
across layers - this is causing repeated confusion"
→ Offers to create issue with refactoring proposal
→ Tech lead reviews and schedules cleanup sprint
| Week | Without Panopticon | With Panopticon |
|---|---|---|
| 1 | AI makes 20 mistakes/day on conventions | AI makes 20 mistakes, captures 8 learnings |
| 2 | AI makes 20 mistakes/day (no memory) | AI makes 12 mistakes, captures 5 more |
| 4 | AI makes 20 mistakes/day (still no memory) | AI makes 3 mistakes, codebase improving |
| 8 | Developers give up on AI for legacy code | AI is productive, tech debt proposals in backlog |
When one developer learns, everyone benefits.
Captured skills live in your project's .claude/skills/ directory - they're version-controlled alongside your code. When Sarah documents that "we use camelCase columns" after hitting that error, every developer on the team - and every AI session from that point forward - inherits that knowledge automatically.
myproject/
├── .claude/skills/
│ └── project-knowledge/ # ← Git-tracked, shared by entire team
│ └── SKILL.md # "Database uses camelCase, not snake_case"
├── src/
└── ...
No more repeating the same corrections to AI across 10 different developers. No more tribal knowledge locked in one person's head. The team's collective understanding of your codebase becomes permanent, searchable, and automatically applied.
New hire onboarding? The AI already knows your conventions from day one.
What gets measured gets managed. Panopticon's Refactor Radar surfaces the specific patterns that are costing you AI productivity:
Each proposal includes:
ROI is simple:
Panopticon's learning system:
This isn't "AI for greenfield only." This is AI that learns your business.
Different teams have different ownership boundaries. Individual developers have different preferences. Panopticon respects both:
# In ~/.claude/CLAUDE.md (developer's personal config)
## AI Suggestion Preferences
### refactor-radar
skip: database-migrations, infrastructure # DBA/Platform team handles these
welcome: naming, code-organization # Always happy for these
### knowledge-capture
skip: authentication # Security team owns this
The AI adapts to your org structure, not the other way around.
npm install -g panopticon-cli && pan install && pan sync && pan up
That's it! Dashboard runs at https://pan.localhost (or http://localhost:3010 if you skip HTTPS setup).
gh) or GitLab CLI (glab) for Git operationspan installpan install📖 Platform support and detailed requirements →
# Create config file
~/.panopticon.env
# Add API keys
LINEAR_API_KEY=lin_api_xxxxx
GITHUB_TOKEN=ghp_xxxxx # Optional
Register your projects:
pan project add /path/to/your/project --name myproject
📖 Complete configuration guide → 📖 Work types and model routing → 📖 Detailed usage examples →
The default landing view. A two-panel layout with a resizable sidebar showing your project tree (grouped by project, filtered to active features) and a main area displaying agent activity, planning artifacts (PRD, STATE.md, transcripts, discussions), and status reviews.
A mode for teams adopting AI incrementally. Register existing projects as "shadow" workspaces to monitor ongoing development without AI agents making changes.
pan workspace create --shadow PAN-XXXSpawn and manage AI agents in tmux sessions, monitored by the Cloister lifecycle manager.
Git worktree-based feature branches with optional Docker isolation. Supports both local and remote (exe.dev) execution.
Dedicated agents for code review, testing, and merging. Automatically triggered by the Cloister manager.
Universal SKILL.md format works across Claude Code, Codex, Cursor, and Gemini. Distributed via pan sync.
📖 Architecture overview → 📖 Specialist workflow →
# Start dashboard
pan up
# Create workspace and spawn agent
pan workspace create PAN-123
# Check agent status
pan status
# View agent logs
pan logs agent-pan-123
# Stop dashboard
pan down
📖 Complete command reference →
| Document | Description |
|---|---|
| docs/INDEX.md | Master documentation index (start here) |
| docs/USAGE.md | Detailed usage guide, examples, troubleshooting |
| docs/CONFIGURATION.md | Model routing, API setup, presets |
| AGENTS.md | Agent architecture |
| docs/ARCHITECTURE-CACHING.md | Dashboard caching and real-time push |
| CONTRIBUTING.md | Contribution guidelines |
| CLAUDE.md | Agent development guidance |
| docs/MISSION-CONTROL.md | Mission Control and Shadow Engineering guide |
Contributions welcome! See CONTRIBUTING.md for guidelines.
MIT License - see LICENSE for details.
Made with ❤️ by the Panopticon team
GitHub · npm · Documentation
FAQs
Multi-agent orchestration for AI coding assistants (Claude Code, Codex, Cursor, Gemini CLI)
The npm package panopticon-cli receives a total of 284 weekly downloads. As such, panopticon-cli popularity was classified as not popular.
We found that panopticon-cli demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Research
/Security News
Socket uncovered four malicious NuGet packages targeting ASP.NET apps, using a typosquatted dropper and localhost proxy to steal Identity data and backdoor apps.
Research
An emerging npm supply chain attack that infects repos, steals CI secrets, and targets developer AI toolchains for further compromise.
Company News
Socket is proud to join the OpenJS Foundation as a Silver Member, deepening our commitment to the long-term health and security of the JavaScript ecosystem.