Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Deploy your own Open-Inspect instance. Use when the user wants to set up, deploy, or onboard to Open-Inspect. Guides through repository setup, credential collection, Terraform deployment, and verification with user handoffs.
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查看安装方法安装前可以查看交给 AI 代理的指令内容。
You are guiding the user through deploying their own instance of Open-Inspect. This is a multi-phase process requiring user interaction for credential collection and external service configuration.
Use TodoWrite to create a checklist tracking these phases:
First, generate a random suffix suggestion for the user:
echo "Suggested deployment name: $(openssl rand -hex 3)"
Use AskUserQuestion to gather:
open-inspect-{deployment_name}.vercel.app and must be unique across all Vercel users.Execute these commands (substitute values from Phase 1):
mkdir -p {directory_path}
gh repo create {github_account}/open-inspect-{name} --private --description "Open-Inspect deployment"
cd {directory_path}
git clone git@github.com:ColeMurray/open-inspect.git .
git remote rename origin upstream
git remote add origin git@github.com:{github_account}/open-inspect-{name}.git
git push -u origin main
npm install
npm run build -w @open-inspect/shared
Hand off to user for each service. Use AskUserQuestion to collect credentials.
Tell the user:
*.YOUR-SUBDOMAIN.workers.devCheck wrangler login status, then create bucket:
wrangler whoami
wrangler r2 bucket create open-inspect-{name}-tf-state
Tell user to create R2 API Token at R2 → Overview → Manage R2 API Tokens with "Object Read & Write" permission.
team_)modal token newThen set the token:
modal token set --token-id {token_id} --token-secret {token_secret}
modal profile current
sk-ant-)Guide user through creating a GitHub App (handles both OAuth and repo access):
Open-Inspect-{YourName} (globally unique)https://open-inspect-{deployment_name}.vercel.apphttps://open-inspect-{deployment_name}.vercel.app/api/auth/callback/github
After receiving the .pem path, convert to PKCS#8:
openssl pkcs8 -topk8 -inform PEM -outform PEM -nocrypt -in {pem_path} -out /tmp/github-app-key-pkcs8.pem
cat /tmp/github-app-key-pkcs8.pem
Guide user:
app_mentions:read, chat:write, channels:history,
channels:read, groups:history, groups:read, im:history, im:read, reactions:writexoxb-...)echo "token_encryption_key: $(openssl rand -base64 32)"
echo "repo_secrets_encryption_key: $(openssl rand -base64 32)"
echo "internal_callback_secret: $(openssl rand -base64 32)"
echo "nextauth_secret: $(openssl rand -base64 32)"
echo "modal_api_secret: $(openssl rand -hex 32)"
echo "github_webhook_secret: $(openssl rand -hex 32)" # Only if GitHub bot enabled
Create terraform/environments/production/backend.tfvars:
access_key = "{r2_access_key}"
secret_key = "{r2_secret_key}"
bucket = "open-inspect-{name}-tf-state"
endpoints = {
s3 = "https://{cloudflare_account_id}.r2.cloudflarestorage.com"
}
Create terraform/environments/production/terraform.tfvars with all collected values. Set:
enable_durable_object_bindings = false
enable_service_bindings = false
If GitHub bot is enabled, also set:
enable_github_bot = true
github_webhook_secret = "{generated_value}"
github_bot_username = "{app-slug}[bot]"
Important: Build the workers before running Terraform (Terraform references the built bundles):
npm run build -w @open-inspect/control-plane -w @open-inspect/slack-bot -w @open-inspect/github-bot
Phase 1 (bindings disabled):
cd terraform/environments/production
terraform init -backend-config=backend.tfvars
terraform apply
Phase 2 (after Phase 1 succeeds): Update tfvars to set both bindings to true, then:
terraform apply
After Terraform deployment, guide user:
The App Home provides a settings interface where users can configure their preferred Claude model.
https://open-inspect-slack-bot-{deployment_name}.{subdomain}.workers.dev/eventsapp_home_opened, app_mention, message.imhttps://open-inspect-slack-bot-{deployment_name}.{subdomain}.workers.dev/interactions/invite @BotNameAfter Terraform deployment, guide user:
https://open-inspect-github-bot-{deployment_name}.{subdomain}.workers.dev/webhooks/githubgithub_webhook_secret valueThe bot username is the App's slug with [bot] appended. E.g., if the app is My-Inspect-App, the
bot username is my-inspect-app[bot]. Confirm this matches github_bot_username in
terraform.tfvars.
npx vercel link --project open-inspect-{deployment_name}
npx vercel --prod
curl https://open-inspect-control-plane-{deployment_name}.{subdomain}.workers.dev/health
curl https://{workspace}--open-inspect-api-health.modal.run
curl -I https://open-inspect-{deployment_name}.vercel.app
Present deployment summary table. Instruct user to test: visit web app, sign in with GitHub, create session, send prompt.
Ask if user wants GitHub Actions CI/CD. If yes, use gh secret set for all required secrets.
enable_github_bot = true, and
github_bot_username matches the App's bot loginnpm run build -w @open-inspect/control-plane -w @open-inspect/slack-bot -w @open-inspect/github-botnpm run build -w @open-inspect/shared还没有评价。欢迎分享你的使用感受。
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Set up and use 1Password CLI (op). Use when installing the CLI, enabling desktop app integration, signing in, and reading/injecting secrets for commands.
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Use this skill when the user requests to review, analyze, critique, or summarize academic papers, research articles, preprints, or scientific publications. Supports comprehensive structured reviews covering methodology assessment, contribution evaluation, literature positioning, and constructive feedback generation. Trigger on queries involving paper URLs, uploaded PDFs, arXiv links, or requests like "review this paper", "analyze this research", "summarize this study", or "write a peer review".
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Add descriptions for new models from the HuggingFace router to chat-ui configuration. Use when new models are released on the router and need descriptions added to prod.yaml and dev.yaml. Triggers on requests like "add new model descriptions", "update models from router", "sync models", or when explicitly invoking /add-model-descriptions.
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Automates browser interactions for web testing, form filling, screenshots, and data extraction. Use when the user needs to navigate websites, interact with web pages, fill forms, take screenshots, test web applications, or extract information from web pages.
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Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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Create and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through experience.
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