GitHub
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
Beschreibung vom Entwickler (englisches Original)
Designs, prices, and deploys AWS/GCP cloud infrastructure from plain-English requirements.
Diese drei werden nie zu einer Punktzahl zusammengefasst: Meinungen, Messungen und Beliebtheit sind verschiedene Dinge.
Für diesen Server haben wir noch keine Zusammenfassung in einfacher Sprache geschrieben. Unten stehen die Fakten, die wir aus der Registry gelesen haben.
Wähle die KI-App, die du nutzt, und kopiere die Einstellungen.
Du brauchst
Welche App nutzt du?
Wähle oben deine App. Wir merken sie uns, damit der Button beim nächsten Mal „In … installieren“ anzeigt.
Funktioniert in Claude im Web, auf dem Desktop und auf dem Handy:
https://app.luthersystems.com/v1/insideout-mcpÖffne Claude Desktop → Settings → Developer → Edit Config und füge dies in claude_desktop_config.json ein
{
"mcpServers": {
"insideout": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/luthersystems/insideout-mcp:v0.36.3"
]
}
}
}Führe das in deinem Terminal aus
claude mcp add --transport http insideout https://app.luthersystems.com/v1/insideout-mcpclaude mcp add insideout -- docker run -i --rm docker.io/luthersystems/insideout-mcp:v0.36.3In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"insideout": {
"url": "https://app.luthersystems.com/v1/insideout-mcp"
}
}
}In .cursor/mcp.json in deinem Projekt oder in ~/.cursor/mcp.json einfügen
{
"mcpServers": {
"insideout": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/luthersystems/insideout-mcp:v0.36.3"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"insideout": {
"type": "http",
"url": "https://app.luthersystems.com/v1/insideout-mcp"
}
}
}In .vscode/mcp.json in deinem Projekt einfügen
{
"servers": {
"insideout": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"docker.io/luthersystems/insideout-mcp:v0.36.3"
]
}
}
}Dein Browser fragt, ob er die App öffnen darf, dann zeigt die App die Einstellungen und bittet dich um Bestätigung. Schlüsselwerte bleiben leer, damit du sie selbst einträgst.
ChatGPT verbindet sich nur mit Servern im Internet:
https://app.luthersystems.com/v1/insideout-mcpMenünamen können sich je nach Tarif und mit der Zeit leicht unterscheiden.
Deine Auswahl bleibt in einem Cookie auf diesem Gerät; kein Konto nötig.
Diese Einstellungen werden aus dem Registry-Eintrag des Entwicklers erzeugt. Wenn in der README etwas anderes steht, folge der README.
Tool-Namen und -Beschreibungen genau so, wie der Server sie am 10. Nov. 2026 gemeldet hat. Geschrieben hat sie der Entwickler, nicht wir; wir zeigen sie als reinen Text.
awsinspectINSPECTION: Inspect AWS infrastructure for a deployed project ⚠️ **PREREQUISITE**: This tool requires a prior deployment ATTEMPT (successful or failed). Check convostatus for hasDeployAttempt=true before calling. Works even after failed deploys to inspect orphaned resources. Insp
awsinspect_batchBATCH INSPECTION: run up to 32 AWS inspect probes in one call. ⚠️ **PREREQUISITE**: Same as awsinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credent
convoawaitWait for a pending response from Riley after a convoreply timeout. 🎯 USE THIS TOOL WHEN: convoreply returned a timeout error. This allows you to continue waiting for the response without resending the message. REQUIRES: - session_id: from convoopen response OPTIONAL: - message_i
convoinspectINSPECTION: View a session's conversation transcript and metadata Returns the full message history (user / assistant / tool turns) plus the session's meta — workflow step, cloud, deployment status, drift state. This is the transcript-reader companion to the other read tools — com
convoopenWORKFLOW: Step 1 of 4 - Start infrastructure design conversation Open an InsideOut V2 session and receive the assistant's intro message. The response contains a clean message from Riley (the infrastructure advisor) - display it to the user. ⚠️ Riley will ask questions - forward t
convoreplyWORKFLOW: Step 2 of 4 - Continue infrastructure design conversation Send a user message to the active InsideOut session and receive the assistant reply. The response contains a clean message from Riley - display it to the user. ⚠️ CRITICAL: DO NOT answer Riley's questions yoursel
convostatusINSPECTION: View the current infrastructure stack for a session Returns the current state of the user's infrastructure design including: **Components** - Selected infrastructure services (VPC, databases, caching, etc.) • Shows what services the user has chosen (e.g., PostgreSQL,
credawaitWait for the user to securely connect their cloud account and subscribe to Luther Systems. Polls until credentials appear on the session. 🎯 USE THIS TOOL WHEN: tfdeploy returns an 'auth_required', 'no_credentials', or 'credentials_expired' error. The user needs to visit the conn
gcpinspectINSPECTION: Inspect GCP infrastructure for a deployed project ⚠️ **PREREQUISITE**: This tool requires a prior deployment ATTEMPT (successful or failed). Check convostatus for hasDeployAttempt=true before calling. Works even after failed deploys to inspect orphaned resources. Insp
gcpinspect_batchBATCH INSPECTION: run up to 32 GCP inspect probes in one call. ⚠️ **PREREQUISITE**: Same as gcpinspect — deploy attempt required. Check convostatus for hasDeployAttempt=true before calling. Use this when you need to check more than ~3 resources. The backend fetches Oracle credent
helpGet workflow guidance for using InsideOut infrastructure tools. Call help() for a compact overview, or help(section=...) for a detailed guide. Sections: workflow, tools, examples, inspect. Responses include hints with next_actions and related_tools.
stackdiffStructured diff showing what would be deployed if the user ran tfdeploy now. Returns component-level changes (added/removed/modified), field-level details, and pricing deltas. Defaults (#1392): with no version arguments, compares the LAST SUCCESSFULLY DEPLOYED version against the
stackrollbackCreate a draft version by reverting to a previous version's config. Copies components, config, and pricing from the target version. If a draft already exists, updates it in-place (single-draft rule). Use `stackversions` first to find available version numbers. REQUIRES: session_i
stackversionsList all stack versions for a session (newest first). Shows version history including version number, status (draft/confirmed/applied), change summaries, and timestamps. Use this tool to see the design history, review what changed between iterations, or find a version number to r
submit_feedbackFEEDBACK: Submit feedback, bug reports, or feature requests to Luther Systems Use this tool to forward user feedback directly to the Luther Systems team. This includes bug reports, feature requests, questions, or general feedback about InsideOut. The agent itself can also use thi
tfdeployWORKFLOW: Step 4 of 4 - Deploy infrastructure to the cloud Deploy infrastructure by starting a Terraform job for an InsideOut session. This tool initiates the actual deployment process after Terraform files have been generated. IMPORTANT: This starts a long-running job (15+ minut
tfdestroyDESTROY: Tear down previously deployed infrastructure Destroys infrastructure by calling the Oracle destroy endpoint for a session that has a prior successful deployment. IMPORTANT: This starts a long-running job. Use tfstatus/tflogs to monitor progress. SINGLE-FLIGHT: only one T
tfdriftDRIFT CHECK: Run a read-only drift detection check Checks whether deployed infrastructure has drifted from the expected Terraform state. This is a read-only operation — it does NOT modify any infrastructure. Returns job_id. Use tflogs to stream the drift check results. SINGLE-FLI
tfgenerateWORKFLOW: Step 3 of 4 - Generate Terraform files from completed design Generate Terraform files from an InsideOut session that has completed infrastructure design. ⚠️ PREREQUISITE: Only call this AFTER convoreply returns with `terraform_ready=true` in the response metadata. DO NO
tflogsMONITORING: Fetch Terraform deployment logs with pagination Fetches logs from a running or completed Terraform deployment job. For **completed jobs**: uses REST endpoint for instant retrieval (supports `tail` for server-side filtering). For **running jobs**: streams via SSE with
tfoutputsINSPECTION: Retrieve Terraform outputs from a completed deployment Returns structured output values (VPC IDs, endpoints, cluster names, etc.) after a successful deploy. Sensitive outputs are redacted (shown as '(sensitive)'). By default returns outputs for the latest successful d
tfplanPREVIEW: Run terraform plan to preview infrastructure changes Runs a terraform plan for an InsideOut session without applying any changes. This lets the user review what will be created/changed/destroyed before committing. Returns job_id, plan_id, and project_id. Use tflogs to st
tfrunsINSPECTION: List all Terraform deployment runs for a session Returns job IDs, statuses, types (apply/destroy), and timestamps for every run. Use this to see deployment history, find job IDs for log inspection, or check which deployments succeeded or failed. REQUIRES: session_id f
tfstatusMONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to s
Wir prüfen Informationen, die jeder sehen kann; den Code haben wir nicht gelesen. Checks, die nicht zutreffen, werden nicht mitgezählt. · Geprüft am 10. Nov. 2026
Der Registry-Name wurde über die Domain insideout.luthersystems.com bestätigt (die Registry prüft, wem die Domain gehört)
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Dieses Repo haben wir noch nicht von GitHub gelesen
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Wir haben 24 Tool-Beschreibungen gelesen und keine versteckten Anweisungen an die KI gefunden
Verglichen mit allen aktiven Servern dieser Kategorie in der Registry. Sterne zählen nur, wenn dem Herausgeber das Repo gehört.
Er hat 3 von 3 zutreffenden Basischecks bestanden. Den Code haben wir nicht gelesen. Bevor du einen Schlüssel oder Token hinzufügst, sieh nach, welche Zugriffe der Server verlangt, und nutze einen Schlüssel, der nur die Berechtigungen hat, die du brauchst.
Die Registry nennt keinen erforderlichen Schlüssel, manche Server bitten dich aber bei der ersten Nutzung, dich anzumelden.
Beides geht: Installiere ihn auf deinem Computer oder verbinde dich mit der Cloud-Version.
Das GitHub-Repo wurde zuletzt – geändert, und die neueste Version (0.36.3) kam vor 6 Monaten in die Registry.
Apps, die MCP unterstützen, etwa Claude Desktop, Claude Code, Cursor und VS Code. Er verbindet sich per URL, also funktionieren auch Apps, die Remote-MCP-Server akzeptieren, zum Beispiel ChatGPT im Entwicklermodus.
In derselben Kategorie: GitHub, Desktop Commander, Cloudflare MCP
Server derselben Kategorie von anderen Entwicklern, sortiert nach Sternen.
So liest du eine Karte:↓ Downloads pro Wochei★ GitHub-SterneiDrei Säulen: Bewertungen · Checks · NutzungiAktualisiert = neueste Version in der Registry veröffentlichti
Connect AI assistants to GitHub - manage repos, issues, PRs, and workflows through natural language.
MCP server for terminal commands, file operations, and process management
Unverändert aus dem Repo des Entwicklers übernommen. PopNx MCP hat den Inhalt weder geprüft noch empfiehlt es ihn. HTML, Bilder und Skripte wurden entfernt. Links führen von dieser Website weg.
Cloud infrastructure automation. Describe your application in natural language and InsideOut will design, configure, deploy, and manage production-ready backend infrastructure -- in minutes, not weeks.
Works standalone or integrated directly into AI app builders via MCP.
npx skills add luthersystems/insideout-agent-skills
Works with Claude Code, Codex, Cursor, Kiro, Windsurf, and 30+ other tools.
[View on Skills.sh](https://skills.sh/luthersystems/insideout-agent-skills/insideout) | [Try the web app](https://insideout.luthersystemsapp.com/) -- no install required.
Setting up cloud infrastructure is slow, error-prone, and requires deep DevOps expertise. Teams spend weeks on plumbing -- networking, security, storage, scaling configuration -- before writing a single line of product code.
InsideOut eliminates this. You describe your application; InsideOut handles the rest: architecture design, Terraform generation, cost estimation, deployment, and ongoing management.
Before InsideOut: Weeks of manual infrastructure setup, Terraform authoring, and cloud configuration. With InsideOut: Backend infrastructure deployed in minutes, managed continuously.
| Capability | Manual Terraform | InsideOut |
|---|---|---|
| Time to deploy | Days to weeks | Minutes |
| Security defaults | Manual | Automatic |
| Cost visibility | Post-deploy billing | Pre-deploy estimates |
| Observability setup | Manual | Day one |
| AI app builder integration | None | Native (Claude Code, Codex, Kiro, Antigravity) |
| Terraform ownership | Yes | Yes (generated, exportable) |
Once installed, just ask:
"Build me a scalable backend for my golang microservices app on AWS with a DB and auth."
InsideOut will:
You: "I need infrastructure for an e-commerce platform, 1k monthly users on AWS"
Riley: "I'd recommend ECS for your containers, RDS PostgreSQL for your database, ElastiCache Redis for sessions, and an ALB. Estimated cost: ~$350/month. Want me to adjust anything?"
You: "Looks good, generate the Terraform"
[Generates production-ready Terraform files]
You: "Deploy it"
[Deploys to AWS, streams logs in real-time]
| User | Use case |
|---|---|
| Solo developers | Ship a production backend without DevOps expertise |
| Startup engineering teams | Move from idea to deployed infrastructure in minutes |
| AI app builders | Integrate infrastructure automation directly into AI-native development workflows |
| Enterprise teams | Standardise infrastructure with best practices and cost controls |
| DevOps / platform engineers | Faster architecture and IaC workflows |
If you'd rather describe what you want than manually wire it together -- this is for you.
| Path | Best for | Install |
|---|---|---|
| This repo (recommended for Codex) | Portable skill across multiple AI tools | npx skills add luthersystems/insideout-agent-skills |
| [Claude Code plugin](https://github.com/luthersystems/insideout-claude-code) | Best native Claude Code experience | /plugin marketplace add luthersystems/insideout-claude-code |
| [Kiro power](https://github.com/luthersystems/insideout-power) | Best native Kiro experience | Add power from GitHub: luthersystems/insideout-power |
| [Web app](https://insideout.luthersystemsapp.com/) | No install required | Open in browser |
npx skills add luthersystems/insideout-agent-skills
For the full plugin experience (slash commands, agents, hooks, auto-approve):
/plugin marketplace add luthersystems/insideout-claude-code
/plugin install insideout
npx skills add luthersystems/insideout-agent-skills --agent codex
Activate by typing $insideout -- Codex will detect the missing MCP server and prompt you to install it automatically.
Fallback: If the auto-install doesn't trigger, run:
codex mcp add insideout --url https://app.luthersystems.com/v1/insideout-mcp
npx skills add luthersystems/insideout-agent-skills --agent cursor
Then add the MCP server to your Cursor MCP config:
{
"mcpServers": { "insideout": { "type": "http", "url": "https://app.luthersystems.com/v1/insideout-mcp" } } }
npx skills add luthersystems/insideout-agent-skills --agent windsurf
Add MCP via Windsurf MCP Marketplace or manually in settings.
For the full power experience (steering files, design pattern guides, auto-approve):
luthersystems/insideout-powernpx skills add luthersystems/insideout-agent-skills --agent antigravity
Then add the MCP server in Antigravity: "..." menu → MCP Servers → Manage MCP Servers → View raw config:
{
"mcpServers": { "insideout": { "serverUrl": "https://app.luthersystems.com/v1/insideout-mcp" } } }
Note: Antigravity uses
serverUrl, noturl.
git clone https://github.com/luthersystems/insideout-agent-skills.git
cp -r insideout-agent-skills ~/.agents/skills/insideout
The ~/.agents/skills/ directory is the standard cross-tool location. Most AI coding tools will detect skills here automatically.
| Category | AWS | GCP |
|---|---|---|
| Compute | EC2, ECS, EKS, Lambda | Compute Engine, Cloud Run, GKE, Cloud Functions |
| Database | RDS PostgreSQL, DynamoDB, ElastiCache, OpenSearch | Cloud SQL, Firestore, Memorystore |
| Networking | VPC, ALB, CloudFront, API Gateway | VPC, Load Balancing, Cloud CDN, API Gateway |
| Storage | S3 | Cloud Storage |
| Security | WAF, KMS, Secrets Manager, Cognito | Cloud Armor, Cloud KMS, Secret Manager, Identity Platform |
| Messaging | SQS, MSK (Kafka) | Pub/Sub |
| Observability | CloudWatch, Managed Grafana | Cloud Logging, Cloud Monitoring |
| AI/ML | Bedrock | Vertex AI |
| CI/CD | CodePipeline, GitHub Actions | Cloud Build |
| Backup | AWS Backup | GCP Backups |
Architecture patterns: AI Stack, Microservices, Serverless. Azure support is upcoming.
Describe your application in natural language. InsideOut generates the architecture, configuration, and Terraform. Review, tweak, and deploy -- all from one place.
Get cost estimates for your exact stack before committing. No surprise bills; pricing is surfaced at design time.
Security best practices applied automatically. Observability configured from day one -- no retroactive setup needed.
You own the generated Terraform files. Use InsideOut defaults or override any setting. No lock-in; the output is standard infrastructure-as-code.
Luther Systems sets up and manages critical infrastructure. Ongoing updates and operational support included.
InsideOut uses a multi-agent AI system behind a single MCP server:
| Agent | Role |
|---|---|
| Riley | Infrastructure advisor -- leads the design conversation |
| Hippo | Cost estimation and pricing optimization |
| Joy | User experience and requirement gathering |
| Etch | Terraform code generation |
| Core | Architecture validation and best practices |
| Axel | Deployment orchestration |
From your perspective, you're talking to Riley. The other agents work behind the scenes. These capabilities are exposed as tools inside your AI environment, so you use them conversationally.
See SECURITY.md for detailed data flow documentation.
InsideOut uses a library of battle-tested, standard Terraform modules composed for common infrastructure patterns.
Can I use InsideOut without the InsideOut UI? Yes. InsideOut integrates with Claude Code, Codex, Kiro, Antigravity, and any MCP-compatible AI development environment, allowing you to design, price, and deploy infrastructure directly from your editor without opening the InsideOut interface.
Do I own the generated Terraform? Yes. InsideOut generates standard Terraform files that you own and can export, modify, or use independently of the platform.
Can I override the default configuration? Yes. InsideOut applies best-practice defaults but every setting is overridable. You control the final configuration before deployment.
What clouds does InsideOut support? AWS and Google Cloud Platform. AWS has the deepest support with the widest range of resource types. Azure support is upcoming.
Is InsideOut suitable for AI application backends? Yes. InsideOut explicitly supports AI stack architectures and is designed to work within AI-native development environments like Claude Code, Codex, Kiro, Antigravity, and other MCP-compatible tools.
How does pricing work? Cost estimates for your exact stack are surfaced before deployment. Contact Luther Systems for platform pricing: contact@luthersystems.com
Luther Systems is an enterprise infrastructure and automation company founded in 2015, headquartered in London, UK. Our mission is to accelerate the advent of the automated enterprise.
Founded by a team who spent years automating complex processes for some of the world's largest financial institutions, Luther Systems builds platforms that bring enterprise-grade infrastructure and operations to teams of every size.
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