[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-datadog-labs-dd-azure-integration":3,"mdc--ehqkk3-key":39,"related-org-datadog-labs-dd-azure-integration":8900,"related-repo-datadog-labs-dd-azure-integration":9072},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":11,"stars":28,"repoUrl":29,"updatedAt":30,"license":31,"forks":32,"topics":33,"repo":34,"sourceUrl":37,"mdContent":38},"dd-azure-integration","set up Datadog Azure integration","Set up the Datadog Azure integration with Terraform - creates an Entra ID app registration and service principal, assigns Monitoring Reader across the chosen subscriptions and management groups, grants the Microsoft Graph permissions Datadog needs for resource discovery, and registers the tenant so Azure metrics and resource collection start flowing. Use when the user wants to monitor Azure VMs, App Service, SQL Database, or AKS, wants to connect an Azure subscription or management group or tenant to Datadog, or asks to set up or repair the Azure integration. Does not set up log forwarding.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},"datadog-labs","Datadog Labs","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fdatadog-labs.png",[12,16,19,22,25],{"name":13,"slug":14,"type":15},"Azure","azure","tag",{"name":17,"slug":18,"type":15},"Monitoring","monitoring",{"name":20,"slug":21,"type":15},"Datadog","datadog",{"name":23,"slug":24,"type":15},"Terraform","terraform",{"name":26,"slug":27,"type":15},"Infrastructure as Code","infrastructure-as-code",145,"https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills","2026-09-01T08:30:38.814838",null,19,[],{"repoUrl":29,"stars":28,"forks":32,"topics":35,"description":36},[],"Public repository for Datadog Agent Skills","https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\u002Ftree\u002FHEAD\u002Fdd-azure-integration","---\nname: dd-azure-integration\ndescription: Set up the Datadog Azure integration with Terraform - creates an Entra ID app registration and service principal, assigns Monitoring Reader across the chosen subscriptions and management groups, grants the Microsoft Graph permissions Datadog needs for resource discovery, and registers the tenant so Azure metrics and resource collection start flowing. Use when the user wants to monitor Azure VMs, App Service, SQL Database, or AKS, wants to connect an Azure subscription or management group or tenant to Datadog, or asks to set up or repair the Azure integration. Does not set up log forwarding.\nmetadata:\n  version: \"1.0.0\"\n  author: datadog-labs\n  repository: https:\u002F\u002Fgithub.com\u002Fdatadog-labs\u002Fagent-skills\n  tags: datadog,azure,integration,terraform,entra,cloud\n  alwaysApply: \"false\"\n  tools: terraform\n---\n\n# Datadog Azure Integration\n\nYou are helping a user set up the Datadog Azure integration using Terraform.\n\nThe integration creates an Azure AD app registration with a service principal, assigns the Monitoring Reader\nrole to the user's subscriptions and\u002For management groups, grants Microsoft Graph API permissions for\nresource discovery, and registers the integration with Datadog.\n\nThis is a hands-on setup: run the commands yourself as part of the conversation rather than handing the\nuser a list, keep them in the loop, and pause for confirmation before `terraform apply`.\n\n## Phase 0: Preflight\n\n**Terraform or OpenTofu.** Every command in this skill is written as `terraform`, but OpenTofu is a\ndrop-in substitute - the providers and module sources used here resolve the same way on both registries.\nCheck which binary the user actually has before Phase 1:\n\n```bash\ncommand -v terraform tofu\n```\n\nIf only `tofu` is on the PATH, read every `terraform \u003Csubcommand>` below as `tofu \u003Csubcommand>`. If both\nare present, ask which one the user wants rather than guessing.\n\n**Datadog credentials.** Load `DD_SITE` \u002F `DD_API_KEY` \u002F `DD_APP_KEY` from the environment (falling back\nto `.env.local` \u002F `.env`) and validate both keys - they fail independently:\n\n```bash\nfor f in .env.local .env; do [ -f \"$f\" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval \"[ -n \\\"\\${$k:-}\\\" ]\" && continue; v=$(grep -E \"^$k=\" \"$f\" | head -1 | cut -d= -f2- | sed 's\u002F^[\"'\\'']\u002F\u002F;s\u002F[\"'\\'']$\u002F\u002F'); [ -n \"$v\" ] && export \"$k=$v\"; done; done\n: \"${DD_SITE:=datadoghq.com}\"\necho \"DD_SITE=${DD_SITE}\"\necho \"DD_API_KEY=$([ -n \"${DD_API_KEY:-}\" ] && echo set || echo UNSET)   DD_APP_KEY=$([ -n \"${DD_APP_KEY:-}\" ] && echo set || echo UNSET)\"\nprintf 'DD-API-KEY: %s\\n' \"$DD_API_KEY\" \\\n  | curl -sS --max-time 20 -o \u002Fdev\u002Fnull -w \"validate:     HTTP %{http_code}\\n\" \\\n      -H @- \"https:\u002F\u002Fapi.${DD_SITE}\u002Fapi\u002Fv1\u002Fvalidate\"\nprintf 'DD-API-KEY: %s\\nDD-APPLICATION-KEY: %s\\n' \"$DD_API_KEY\" \"$DD_APP_KEY\" \\\n  | curl -sS --max-time 20 -o \u002Fdev\u002Fnull -w \"current_user: HTTP %{http_code}\\n\" \\\n      -H @- \"https:\u002F\u002Fapi.${DD_SITE}\u002Fapi\u002Fv2\u002Fcurrent_user\"\n```\n\n| Result | Meaning | What to do |\n|---|---|---|\n| Both `200` | Keys are good for this site | Continue to Phase 1 |\n| `validate` is `403` | The **API key** is invalid, or belongs to a different region than `DD_SITE` | Ask which site the key belongs to, fix `DD_SITE`, re-check |\n| `validate` `200`, `current_user` `403` | The **app key** is wrong or from another region - not the API key | Get one from `\u003CAPP_BASE>\u002Forganization-settings\u002Fapplication-keys` |\n| Either key unset | Nothing to validate | On a commercial site, run the **dd-account-setup** skill, then come back. On `ddog-gov.com` or `us2.ddog-gov.com`, ask the user for the keys directly - that skill validates `DD_SITE` against a list that excludes both government sites and will reject them |\n\n**App URL.** The Datadog app host is **not** `app.${DD_SITE}` for every site. It is\n`https:\u002F\u002Fapp.datadoghq.com` (US1), `https:\u002F\u002Fapp.datadoghq.eu` (EU1), `https:\u002F\u002Fapp.ddog-gov.com` (Gov),\nand for every other site it is `https:\u002F\u002F${DD_SITE}` itself - `https:\u002F\u002Fus3.datadoghq.com`,\n`https:\u002F\u002Fus5.datadoghq.com`, `https:\u002F\u002Fap1.datadoghq.com`, `https:\u002F\u002Fap2.datadoghq.com`,\n`https:\u002F\u002Fuk1.datadoghq.com`. Resolve it once and substitute it wherever `\u003CAPP_BASE>` appears below.\nFull list: https:\u002F\u002Fdocs.datadoghq.com\u002Fgetting_started\u002Fsite\u002F\n\n**Remember the resolved `DD_SITE`.** Most agent runtimes start a fresh shell per command, so the\n`export` above is gone by the next block. That is why the loader line is repeated verbatim at the top\nof every later block that needs credentials - it is deliberate, not drift; don't strip it. `DD_SITE` is not a secret, so every later block re-establishes it\nitself with an explicit `DD_SITE='\u003Csite>'; export DD_SITE` - substitute the site confirmed in Phase 0.\nIt is a plain assignment rather than `: \"${DD_SITE:=...}\"` on purpose: `:=` only fills in an *unset or\nempty* value, so a wrong non-empty `DD_SITE` sitting in `.env` would survive it and every call would go to\nthe wrong region. The keys are guarded with `:?` instead, so a missing key aborts loudly rather than\nsending an empty header. **Never inline the key values** - they must always\narrive through the loader as `$DD_API_KEY` \u002F `$DD_APP_KEY`. Two consequences follow, and both are\ndeliberate:\n\n- **Datadog calls pass headers on stdin**, as `printf 'DD-API-KEY: %s\\n' \"$DD_API_KEY\" | curl -H @- ...`.\n  `printf` is a shell builtin, so the key never becomes an argument of any process and never appears in\n  `ps`. Writing `-H \"DD-API-KEY: $DD_API_KEY\"` instead would put it in curl's argv. (`-H @-` needs\n  curl 7.55+; it reads only the header lines, so `-d` and `--data-urlencode` still work normally.)\n- **Terraform never receives the keys as values at all** for AWS, Azure, and GCP: the Datadog provider\n  reads `DD_API_KEY` \u002F `DD_APP_KEY` from the environment, so there are no root variables, no `-var=`\n  arguments, and nothing for Terraform to record in state or a saved plan. (OCI is the exception - its\n  module needs them as inputs, so there they travel as `TF_VAR_*`.)\n\nTogether with the loader, that keeps both keys out of the transcript, out of shell history, and out of\nthe process list.\n\n**Tools.** `terraform` is required; the `az` CLI is optional (it only discovers subscriptions and\nmanagement groups):\n\n```bash\ncommand -v terraform || echo \"MISSING terraform - https:\u002F\u002Fdeveloper.hashicorp.com\u002Fterraform\u002Finstall\"\ncommand -v az >\u002Fdev\u002Fnull 2>&1 && echo \"az: available\" || echo \"az: not installed\"\n```\n\nThe `azurerm` and `azuread` providers read the ambient Azure credentials, so the user must be signed in\n(`az login`) with permission to create app registrations and assign roles.\n\n**The snippets here are POSIX shell.** Under PowerShell or `cmd`, use the Windows equivalents\n(`Get-Command`, `$env:VAR`, `2>$null`, `curl.exe`) - same calls, same order.\n\n## Phase 1: Determine Scope\n\nAsk the user if they already know which Azure subscription IDs and\u002For management group names they want\nDatadog to monitor.\n\nIf they do, collect:\n- The **tenant ID** - always, even on this path. The duplicate check and `datadog_integration_azure` are\n  both keyed on the tenant, so you cannot skip it. With `az` available:\n  `az account show --query tenantId -o tsv`; otherwise portal.azure.com → **Microsoft Entra ID** →\n  **Overview** → **Tenant ID**.\n- The list of subscription IDs to monitor\n- The list of management group names to monitor (optional)\n\nIf they don't know or want help figuring it out:\n\n**If the `az` CLI is available**, offer to discover their Azure environment.\nExplain that you will use `az` to list their subscriptions and management groups so they can pick\nwhich ones to monitor. This is best-effort - run each command independently and work with whatever\nsucceeds:\n\nFirst, get the current tenant ID:\n```bash\naz account show --query \"tenantId\" -o tsv\n```\n\n**Subscriptions:**\n```bash\naz account list --query \"[?tenantId=='\u003CTENANT_ID>'].{id:id, name:name}\" -o table\n```\n\n**Management Groups:**\n```bash\naz account management-group list --query \"[?tenantId=='\u003CTENANT_ID>'].{name:name, displayName:displayName}\" -o table\n```\n\nIf any individual command fails (e.g., the user lacks permission to list management groups),\ninform the user which command failed and why, but continue with whatever information was\nsuccessfully retrieved.\n\n**Otherwise** (`az` not installed or not authenticated), ask the user to gather the IDs from the\nAzure portal:\n- **Tenant ID**: portal.azure.com → **Microsoft Entra ID** → **Overview** → **Tenant ID**.\n- **Subscription IDs**: portal.azure.com → **Subscriptions**.\n- **Management Group names** (optional): portal.azure.com → **Management groups**.\n\nPresent whatever results were gathered in a readable format and let the user choose:\n- **Specific subscriptions**: list of subscription IDs\n- **Management groups**: list of management group names (Datadog gets Monitoring Reader on the group scope)\n- **Both**: a combination of explicit subscriptions and management groups\n\n## Phase 2: Generate and Apply Terraform\n\n### Check what already exists - local Terraform first, then Datadog\n\nDo this **before** generating or applying anything, and in this order. Local state first, because it\ndecides whether an existing integration is something you can update or something you must not touch:\n\n```bash\nfind . -maxdepth 1 -type f \\( -name '*.tf' -o -name 'terraform.tfstate' \\) -print\n# A project is \"present\" if it has configuration - .terraform\u002F may simply not exist yet on a fresh clone\n# with a remote backend, and terraform.tfstate does not exist at all when state is remote.\nif [ -n \"$(find . -maxdepth 1 -type f \\( -name '*.tf' -o -name '*.tf.json' \\) -print -quit)\" ]; then\n  terraform init -input=false >\u002Fdev\u002Fnull || { echo \"terraform init failed - resolve that before concluding anything about existing state\"; exit 1; }\n  out=$(terraform state list 2>&1); rc=$?\n  if [ \"$rc\" -ne 0 ]; then\n    case $out in\n      *'No state file'*|*'no state'*|*'Backend initialization required'*)\n        echo \"project is initialized but has no state yet - treat as a clean install\" ;;\n      *)\n        printf '%s\\n' \"$out\"\n        echo \"could not read state (backend or credentials problem) - do NOT treat this as 'nothing exists'\"; exit 1 ;;\n    esac\n  elif [ -z \"$out\" ]; then\n    echo \"state is empty - treat as a clean install\"\n  else\n    printf '%s\\n' \"$out\" | grep -F 'datadog_integration_azure' || echo \"state exists but holds no datadog_integration_azure resource\"\n  fi\nelse\n  echo \"no Terraform configuration here yet - clean install\"\nfi\n```\n\nMatch the **exact** resource address `datadog_integration_azure`, not a loose `grep -i datadog`: unrelated Datadog\nresources, or cloud IAM left behind by a partial apply, would otherwise read as a managed integration.\n\nThen ask Datadog what it already has:\n\n```bash\nfor f in .env.local .env; do [ -f \"$f\" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval \"[ -n \\\"\\${$k:-}\\\" ]\" && continue; v=$(grep -E \"^$k=\" \"$f\" | head -1 | cut -d= -f2- | sed 's\u002F^[\"'\\'']\u002F\u002F;s\u002F[\"'\\'']$\u002F\u002F'); [ -n \"$v\" ] && export \"$k=$v\"; done; done\nDD_SITE='datadoghq.com'; export DD_SITE   # \u003C- replace with the site confirmed in Phase 0.\n# Explicit assignment, not ':=': a wrong non-empty DD_SITE in .env would otherwise survive.\n: \"${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"; : \"${DD_APP_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"\nresp=$(printf 'DD-API-KEY: %s\\nDD-APPLICATION-KEY: %s\\n' \"$DD_API_KEY\" \"$DD_APP_KEY\" \\\n  | curl -sS -w '\\n%{http_code}' -X GET -H @- \"https:\u002F\u002Fapi.${DD_SITE}\u002Fapi\u002Fv1\u002Fintegration\u002Fazure\")\ncode=$(printf '%s' \"$resp\" | tail -1); body=$(printf '%s' \"$resp\" | sed '$d')\n[ \"$code\" = \"200\" ] || { echo \"lookup failed with HTTP $code - do not assume 'not connected':\"; printf '%s\\n' \"$body\"; exit 1; }\nprintf '%s\\n' \"$body\"\n```\n\nNow reconcile the two answers before doing anything:\n\n- **Not in Datadog, nothing in local state** - a clean install. Continue.\n- **In Datadog *and* present in local state** - this is the update\u002Frepair case, not a duplicate.\n  Continue into the Terraform below as a change to the existing resources, and let the plan show\n  what it will alter.\n- **In Datadog but *absent* from local state** (the tenant from Phase 1 is already registered) - stop. Applying would either create a\n  duplicate or fight with whatever manages it. Say so plainly and offer the options: import the existing\n  object into this project (`terraform import datadog_integration_azure.datadog_integration \"${tenant_name}:${client_id}\"`,\n  with the existing app's secret supplied as the `CLIENT_SECRET` environment variable), manage it where it is already managed, or delete it in Datadog first.\n  Only continue if the user picks one and confirms.\n  Two things about importing, in this order. **Generate the configuration first** (the Terraform below,\n  adapted to the identity that already exists - same role\u002Fapp\u002Fservice-account name), because `terraform\n  import` binds an existing object to a *configured* resource address and fails without one. And importing\n  the Datadog registration alone is not enough: the cloud-side identity (the IAM role, the app registration,\n  the service account) is still outside state, so either import those too or reference them with data\n  sources, or the next apply will try to create them again and collide.\n\n   ```bash\n   # the existing app's secret must be in the environment for the import to validate\n   CLIENT_SECRET='\u003Cexisting-app-secret>' terraform import datadog_integration_azure.datadog_integration \"\u003Ctenant_name>:\u003Cclient_id>\"\n   ```\n- **Present in local state but *absent* from Datadog** - a partial or rolled-back install. The cloud-side\n  resources may exist while the registration does not. Do not start from scratch: run the plan and let it\n  show what is missing, and expect it to re-create only the registration.\n\n### Check for Existing Terraform\n\nBefore generating a new Terraform configuration, check if the user already has a Terraform project\nin the current directory or nearby:\n\n```bash\nfind . -maxdepth 1 -type f \\( -name '*.tf' -o -name 'terraform.tfstate' \\) -print\n```\n\n**If existing `.tf` files are found:**\n- Read them to understand what providers and resources are already configured.\n- If a `datadog` provider already exists, reuse its configuration - do not create a duplicate.\n- If `azurerm` or `azuread` providers already exist, reuse them.\n- Only add the **new resources** needed (app registration, role assignments, `datadog_integration_azure`)\n  to the existing project. Do not regenerate providers, variables, or terraform blocks that\n  already exist.\n- If the user has a modular layout, create a new file like `datadog-azure-integration.tf` for the\n  Datadog resources.\n\n**Before generating anything, settle where state will live.** This template creates a client secret that\nis stored in state (see Important Notes), so a default local `terraform.tfstate` means a plaintext secret\non disk. Confirm with the user that state goes to an encrypted, access-controlled remote backend, and\nconfigure that backend **before** `terraform init` - moving state afterwards leaves the plaintext copy\nbehind. **If they will not use an encrypted remote backend, stop here.** Explain that the generated client\nsecret would sit in cleartext in a local `terraform.tfstate`, and offer the alternative: configure the\nintegration through the Azure integration tile in the Datadog UI, which stores the secret server-side and\nwrites no state file. An acknowledgement is not a substitute for the backend - this matches the family rule\nfor this family of skills, and it holds however this skill was invoked.\n\n**If no existing Terraform is found**, generate a standalone configuration.\n\nThe full HCL template - providers, the app registration and rotating secret, the Monitoring Reader\nassignments for both scopes, the Graph API grants, and the `datadog_integration_azure` registration -\nis in **`references\u002Fterraform.md`**, along with the subscriptions-only and management-groups-only\nvariants. Read it now and emit it with the placeholders filled in.\n\n## Applying the Terraform\n\n1. Replace all `\u003CPLACEHOLDER>` values in the template with the actual values gathered:\n   - `\u003CTENANT_ID>` from Phase 1\n   - `\u003CUSER_SUBSCRIPTION_IDS>` from Phase 1\n   - `\u003CUSER_MANAGEMENT_GROUP_NAMES>` from Phase 1\n   - `\u003CDD_SITE>` from Phase 0\n2. If the user selected only subscriptions (no management groups), set `management_group_names = []` and remove the `azurerm_role_assignment.monitoring_reader_management_group` resource.\n3. If the user selected only management groups (no explicit subscriptions), they still need at least one subscription ID for the `azurerm` provider - use a subscription from within one of their management groups.\n4. Run `terraform init` to install providers.\n5. Plan, and **save the plan to a file**. The Datadog provider reads `DD_API_KEY` \u002F `DD_APP_KEY`\n   straight from the environment, so there are no root variables and no `-var=` arguments - nothing secret\n   ends up in the plan file, in state, or on a command line:\n\n   ```bash\n   for f in .env.local .env; do [ -f \"$f\" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval \"[ -n \\\"\\${$k:-}\\\" ]\" && continue; v=$(grep -E \"^$k=\" \"$f\" | head -1 | cut -d= -f2- | sed 's\u002F^[\"'\\'']\u002F\u002F;s\u002F[\"'\\'']$\u002F\u002F'); [ -n \"$v\" ] && export \"$k=$v\"; done; done\n   DD_SITE='datadoghq.com'; export DD_SITE   # \u003C- replace with the site confirmed in Phase 0.\n   # Explicit assignment, not ':=': a wrong non-empty DD_SITE in .env would otherwise survive.\n   : \"${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"; : \"${DD_APP_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"\n   umask 077          # tighten permissions on the plan file anyway\n   terraform plan -out=tfplan\n   ```\n\n   Show the plan output to the user and wait for explicit confirmation.\n6. Apply **that saved plan**, only after the user confirms it. Applying the file is what makes the\n   approval meaningful: `terraform apply` with no plan file computes a brand-new plan, and `-auto-approve`\n   would execute it without anyone seeing it, so anything changed since the plan would go in unreviewed:\n\n   ```bash\n   for f in .env.local .env; do [ -f \"$f\" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval \"[ -n \\\"\\${$k:-}\\\" ]\" && continue; v=$(grep -E \"^$k=\" \"$f\" | head -1 | cut -d= -f2- | sed 's\u002F^[\"'\\'']\u002F\u002F;s\u002F[\"'\\'']$\u002F\u002F'); [ -n \"$v\" ] && export \"$k=$v\"; done; done\n   DD_SITE='datadoghq.com'; export DD_SITE   # \u003C- replace with the site confirmed in Phase 0.\n   # Explicit assignment, not ':=': a wrong non-empty DD_SITE in .env would otherwise survive.\n   : \"${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"; : \"${DD_APP_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"\n   trap 'rm -f tfplan' EXIT HUP INT TERM   # the plan file goes away even if this is interrupted\n   if terraform apply tfplan; then\n     echo \"apply complete\"\n   else\n     echo \"terraform apply FAILED - do NOT verify or report success\"\n     exit 1\n   fi\n   ```\n\n   The `trap` removes the plan file on every exit path, including Ctrl-C while the user is deciding. The\n   `if`\u002F`else` around the apply matters because a cleanup command as the block's last line would make a\n   failed apply exit 0, and the agent would go on to \"verify\" a deployment that never happened. (It is an\n   `if` rather than `status=$?` on purpose: `status` is a read-only variable in zsh.)\n\n   The plan file holds no key material at all, because the keys never become Terraform values - the provider\n   reads them from the environment. That is what makes `-out` safe here, on any Terraform version, and it is\n   why an existing project needs no variable changes either.\n\n7. After `terraform apply` succeeds, verify the integration registered with Datadog:\n\n   ```bash\n   for f in .env.local .env; do [ -f \"$f\" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval \"[ -n \\\"\\${$k:-}\\\" ]\" && continue; v=$(grep -E \"^$k=\" \"$f\" | head -1 | cut -d= -f2- | sed 's\u002F^[\"'\\'']\u002F\u002F;s\u002F[\"'\\'']$\u002F\u002F'); [ -n \"$v\" ] && export \"$k=$v\"; done; done\n   DD_SITE='datadoghq.com'; export DD_SITE   # \u003C- replace with the site confirmed in Phase 0.\n   # Explicit assignment, not ':=': a wrong non-empty DD_SITE in .env would otherwise survive.\n   : \"${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"; : \"${DD_APP_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"\n   resp=$(printf 'DD-API-KEY: %s\\nDD-APPLICATION-KEY: %s\\n' \"$DD_API_KEY\" \"$DD_APP_KEY\" \\\n     | curl -sS -w '\\n%{http_code}' -X GET -H @- \"https:\u002F\u002Fapi.${DD_SITE}\u002Fapi\u002Fv1\u002Fintegration\u002Fazure\")\n   code=$(printf '%s' \"$resp\" | tail -1); body=$(printf '%s' \"$resp\" | sed '$d')\n   [ \"$code\" = \"200\" ] || { echo \"lookup failed with HTTP $code - do not assume 'not connected':\"; printf '%s\\n' \"$body\"; exit 1; }\n   printf '%s\\n' \"$body\"\n   ```\n\n   Confirm the response lists the tenant and client_id just provisioned. If the integration is missing, surface the response to the user so they can debug.\n\n## Getting the Most Out of Your Integration\n\nOnce `terraform apply` completes successfully, congratulate the user and let them know metrics typically\narrive within 5-10 minutes. Then check for early metrics and show a widget.\n\n### Checking for Metrics\n\nGive it a few seconds, then make a single query to the metrics API:\n\n```bash\nfor f in .env.local .env; do [ -f \"$f\" ] || continue; for k in DD_SITE DD_API_KEY DD_APP_KEY; do eval \"[ -n \\\"\\${$k:-}\\\" ]\" && continue; v=$(grep -E \"^$k=\" \"$f\" | head -1 | cut -d= -f2- | sed 's\u002F^[\"'\\'']\u002F\u002F;s\u002F[\"'\\'']$\u002F\u002F'); [ -n \"$v\" ] && export \"$k=$v\"; done; done\nDD_SITE='datadoghq.com'; export DD_SITE   # \u003C- replace with the site confirmed in Phase 0.\n# Explicit assignment, not ':=': a wrong non-empty DD_SITE in .env would otherwise survive.\n: \"${DD_API_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"; : \"${DD_APP_KEY:?not set - run dd-account-setup (commercial sites) or supply it directly (government sites)}\"\nsleep 10\nresp=$(printf 'DD-API-KEY: %s\\nDD-APPLICATION-KEY: %s\\n' \"$DD_API_KEY\" \"$DD_APP_KEY\" \\\n  | curl -sS -w '\\n%{http_code}' -G -H @- \"https:\u002F\u002Fapi.${DD_SITE}\u002Fapi\u002Fv1\u002Fquery\" \\\n  --data-urlencode \"from=$(($(date +%s) - 900))\" \\\n  --data-urlencode \"to=$(date +%s)\" \\\n  --data-urlencode \"query=avg:azure.vm.percentage_cpu{*} by {name}\")\ncode=$(printf '%s' \"$resp\" | tail -1); body=$(printf '%s' \"$resp\" | sed '$d')\n[ \"$code\" = \"200\" ] || { echo \"metric query failed with HTTP $code - that is NOT 'metrics still propagating':\"; printf '%s\\n' \"$body\"; exit 1; }\nprintf '%s\\n' \"$body\"\n```\n\n### Rendering the Widget\n\n**If the `series` array is non-empty**, render an ASCII chart from the real data:\n- Use the `pointlist` values to plot the line, scaling Y-axis to actual min\u002Fmax.\n- Use box-drawing characters (`╭`, `╰`, `─`, `│`, `┤`) for the line.\n- List the VM names from each series `scope` at the bottom.\n- Show the top 3 series by average value if multiple are returned.\n\n**If the `series` array is empty**, show this static preview instead and let the user know\nmetrics are still propagating:\n\n    ┌─────────────────────────────────────────────────────────┐\n    │  azure.vm.percentage_cpu            ▂▃▅▆▇▆▅▃▂▁▂▃▅▆▇█  │\n    │  100% ┤                                          ╭──╮   │\n    │   75% ┤                    ╭───╮              ╭──╯  │   │\n    │   50% ┤              ╭────╯   ╰──╮     ╭────╯     │   │\n    │   25% ┤    ╭────────╯            ╰────╯           │   │\n    │    0% ┤────╯                                       │   │\n    │       └────────────────────────────────────────────┘   │\n    │                                                         │\n    │  Metrics are on their way - check back in a few minutes │\n    └─────────────────────────────────────────────────────────┘\n    Metrics Explorer: \u003CAPP_BASE>\u002Fmetric\u002Fexplorer?exp_metric=azure.vm.percentage_cpu\n\nConfirm to the user that their integration is configured and data will appear shortly. All links use `DD_SITE` - construct them as `\u003CAPP_BASE>\u002F...`.\n\n- **Azure Integration tile**: `\u003CAPP_BASE>\u002Fintegrations\u002Fazure` - access the pre-built dashboard and verify the integration is active.\n- **Metrics Explorer**: `\u003CAPP_BASE>\u002Fmetric\u002Fexplorer?exp_metric=azure.vm.percentage_cpu` - confirm data is flowing.\n- Each Azure service (Virtual Machines, App Service, SQL Database, AKS, etc.) has its own dashboard that activates automatically when metrics for that service are detected.\n\n**Recommended Monitors** - suggest creating monitors for common Azure health signals at `\u003CAPP_BASE>\u002Fmonitors\u002Fcreate`:\n- Virtual Machine CPU exceeding a threshold\n- App Service HTTP error rate spikes\n- SQL Database DTU consumption approaching limits\n- AKS node pool availability\n\n**If resource collection was enabled:**\n- **Resource Catalog**: `\u003CAPP_BASE>\u002Finfrastructure\u002Fcatalog` - browse Virtual Machines, App Services, SQL Databases, AKS clusters, and more.\n- **Infrastructure Map**: `\u003CAPP_BASE>\u002Finfrastructure\u002Fmap` - visualize Azure infrastructure.\n\n**Explore more Datadog products:**\n- **Log Management**: `\u003CAPP_BASE>\u002Flogs` - stream Azure activity and resource logs for centralized search and alerting. Setup: https:\u002F\u002Fdocs.datadoghq.com\u002Fintegrations\u002Fazure\u002F#log-collection\n- **APM & Traces**: `\u003CAPP_BASE>\u002Fapm\u002Fgetting-started` - distributed tracing for applications on App Service, AKS, or Virtual Machines.\n- **Notebooks**: `\u003CAPP_BASE>\u002Fnotebook` - shareable investigations combining metrics, logs, and events.\n\n## Important Notes\n\n- **Concrete permissions the template needs** - \"can create app registrations\" is not enough, and a\n  Contributor or Application Developer will get through discovery and then fail with 403 at apply:\n  - **Application Administrator** or **Global Administrator** in Entra ID, because\n    `azuread_app_role_assignment` grants Microsoft Graph app roles (admin consent).\n  - `Microsoft.Authorization\u002FroleAssignments\u002Fwrite` at **every** selected subscription and management-group\n    scope - typically **User Access Administrator** or **Owner** there - for the Monitoring Reader\n    assignments.\n  Check these before Phase 2; if the user lacks them, they need their Entra administrator rather than a\n  retry.\n- The app registration secret expires after 1 year - remind the user they'll need to rotate it.\n- The Datadog API and app keys are never passed to Terraform as values: the `datadog` provider reads\n  `DD_API_KEY` and `DD_APP_KEY` from the environment, so there are no root variables, no `-var=` arguments,\n  and nothing for Terraform to record in state or a saved plan. Don't declare key variables, and don't\n  write the keys into a committed `.tfvars` file or any other persistent file. (The *client secret* this\n  template generates is a separate matter - it is a resource attribute and does land in state; see below.)\n- Never run `terraform apply` without showing the plan to the user first.\n- The `azurerm` provider requires at least one subscription ID even when using management groups.\n- Unlike the AWS role and GCP impersonation flows, this one issues a **client secret** that Datadog\n  stores, which is why it is created through a `time_rotating` resource rather than as a static value.\n- **The client secret is written to Terraform state.** `azuread_application_password.value` and\n  `datadog_integration_azure.client_secret` are resource attributes, so `sensitive = true` redacts them\n  from CLI output but not from state\n  ([HashiCorp docs](https:\u002F\u002Fdeveloper.hashicorp.com\u002Fterraform\u002Flanguage\u002Fmanage-sensitive-data)). 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Don't\n  offer it as a flag flip; see the note in `references\u002Fterraform.md` for what it would actually take.\n",{"data":40,"body":45},{"name":4,"description":6,"metadata":41},{"version":42,"author":8,"repository":29,"tags":43,"alwaysApply":44,"tools":24},"1.0.0","datadog,azure,integration,terraform,entra,cloud","false",{"type":46,"children":47},"root",[48,57,63,68,82,89,107,146,175,223,1138,1346,1455,1559,1668,1673,1697,1813,1840,1887,1893,1898,1903,1970,1975,1999,2004,2055,2063,2112,2120,2172,2177,2194,2252,2257,2289,2295,2302,2314,3089,3116,3121,4147,4152,4319,4325,4330,4412,4428,4498,4542,4552,4575,4581,7171,7177,7189,7195,7200,8314,8320,8337,8412,8428,8438,8457,8500,8517,8540,8548,8585,8593,8653,8659,8894],{"type":49,"tag":50,"props":51,"children":53},"element","h1",{"id":52},"datadog-azure-integration",[54],{"type":55,"value":56},"text","Datadog Azure Integration",{"type":49,"tag":58,"props":59,"children":60},"p",{},[61],{"type":55,"value":62},"You are helping a user set up the Datadog Azure integration using Terraform.",{"type":49,"tag":58,"props":64,"children":65},{},[66],{"type":55,"value":67},"The integration creates an Azure AD app registration with a service principal, assigns the Monitoring Reader\nrole to the user's subscriptions and\u002For management groups, grants Microsoft Graph API permissions for\nresource discovery, and registers the integration with Datadog.",{"type":49,"tag":58,"props":69,"children":70},{},[71,73,80],{"type":55,"value":72},"This is a hands-on setup: run the commands yourself as part of the conversation rather than handing the\nuser a list, keep them in the loop, and pause for confirmation before ",{"type":49,"tag":74,"props":75,"children":77},"code",{"className":76},[],[78],{"type":55,"value":79},"terraform apply",{"type":55,"value":81},".",{"type":49,"tag":83,"props":84,"children":86},"h2",{"id":85},"phase-0-preflight",[87],{"type":55,"value":88},"Phase 0: Preflight",{"type":49,"tag":58,"props":90,"children":91},{},[92,98,100,105],{"type":49,"tag":93,"props":94,"children":95},"strong",{},[96],{"type":55,"value":97},"Terraform or OpenTofu.",{"type":55,"value":99}," Every command in this skill is written as ",{"type":49,"tag":74,"props":101,"children":103},{"className":102},[],[104],{"type":55,"value":24},{"type":55,"value":106},", but OpenTofu is a\ndrop-in substitute - the providers and module sources used here resolve the same way on both registries.\nCheck which binary the user actually has before Phase 1:",{"type":49,"tag":108,"props":109,"children":114},"pre",{"className":110,"code":111,"language":112,"meta":113,"style":113},"language-bash shiki shiki-themes material-theme-lighter material-theme material-theme-palenight","command -v terraform tofu\n","bash","",[115],{"type":49,"tag":74,"props":116,"children":117},{"__ignoreMap":113},[118],{"type":49,"tag":119,"props":120,"children":123},"span",{"class":121,"line":122},"line",1,[124,130,136,141],{"type":49,"tag":119,"props":125,"children":127},{"style":126},"--shiki-light:#6182B8;--shiki-default:#82AAFF;--shiki-dark:#82AAFF",[128],{"type":55,"value":129},"command",{"type":49,"tag":119,"props":131,"children":133},{"style":132},"--shiki-light:#91B859;--shiki-default:#C3E88D;--shiki-dark:#C3E88D",[134],{"type":55,"value":135}," -v",{"type":49,"tag":119,"props":137,"children":138},{"style":132},[139],{"type":55,"value":140}," terraform",{"type":49,"tag":119,"props":142,"children":143},{"style":132},[144],{"type":55,"value":145}," tofu\n",{"type":49,"tag":58,"props":147,"children":148},{},[149,151,157,159,165,167,173],{"type":55,"value":150},"If only ",{"type":49,"tag":74,"props":152,"children":154},{"className":153},[],[155],{"type":55,"value":156},"tofu",{"type":55,"value":158}," is on the PATH, read every ",{"type":49,"tag":74,"props":160,"children":162},{"className":161},[],[163],{"type":55,"value":164},"terraform \u003Csubcommand>",{"type":55,"value":166}," below as ",{"type":49,"tag":74,"props":168,"children":170},{"className":169},[],[171],{"type":55,"value":172},"tofu \u003Csubcommand>",{"type":55,"value":174},". 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Only use if no Datadog Agent is deployed on the cluster yet.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8908,8909,8912,8915],{"name":20,"slug":21,"type":15},{"name":8910,"slug":8911,"type":15},"Deployment","deployment",{"name":8913,"slug":8914,"type":15},"Kubernetes","kubernetes",{"name":8916,"slug":8917,"type":15},"Observability","observability","2026-04-15T04:57:27.489805",{"slug":8920,"name":8920,"fn":8921,"description":8922,"org":8923,"tags":8924,"stars":28,"repoUrl":29,"updatedAt":8936},"agent-observability-auto-experiment","run iterative code improvements using Datadog data","Run an iterative code-improvement hill-climb against real Datadog LLM-Obs data, locally, with Claude Code as the agent. Establishes a baseline eval, makes one focused change, re-scores with the same harness, keeps the change if it improves the score in the goal's direction (labeling within-noise gains tentative), and repeats. Use when the user says \"run an auto experiment\", \"hill-climb this code\", \"iteratively improve X and measure the delta\", \"optimize this prompt\u002Ffile against my traces\", \"auto-optimize against LLM-Obs\", or wants the local equivalent of the auto_experiments worker. 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Use when user says \"bootstrap evaluators\", \"generate evaluators\", \"create evals from traces\", \"eval bootstrap\", \"write evaluators\", \"build eval suite\", \"publish evaluators\", or wants to generate BaseEvaluator\u002FLLMJudge code or online judge configs from production LLM trace data. 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Pure orchestration over the agent-observability sub-skills (`agent-observability-session-classify`, `agent-observability-trace-rca`, `agent-observability-eval-bootstrap`, `agent-observability-experiment-bootstrap`, `agent-observability-experiment-analyzer`). Use when user says \"run the eval pipeline\", \"go from traces to evals\", \"bootstrap evals end to end\", \"classify then RCA then bootstrap\", \"build an eval set from scratch\", \"onboard me to datasets and experiments\", \"walk me through experiments\", \"I have an ml_app, now what\", \"Agent Observability onboarding\", \"guided experiment setup\", \"from traces to experiments\", or wants a deterministic, narrated tour from production data through evaluators, datasets, and experiments. Stop early with `--stop-after \u003Cphase>` to short-circuit at evaluators or dataset, or resume mid-flow with `--start-at \u003Cphase>`.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8954,8957,8960,8961,8962],{"name":8955,"slug":8956,"type":15},"Agents","agents",{"name":8958,"slug":8959,"type":15},"Data Pipeline","data-pipeline",{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8916,"slug":8917,"type":15},"2026-08-22T03:24:05.862875",{"slug":8965,"name":8965,"fn":8966,"description":8967,"org":8968,"tags":8969,"stars":28,"repoUrl":29,"updatedAt":8977},"agent-observability-experiment-analyzer","analyze LLM experiment results","Analyze LLM experiment results. Handles single or comparative experiments, exploratory or Q&A modes. Use when user says \"analyze experiment\", \"compare experiments\", \"analyze against baseline\", or provides one or two experiment IDs for analysis.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8970,8973,8974,8975,8976],{"name":8971,"slug":8972,"type":15},"Analytics","analytics",{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},"2026-06-19T09:04:21.212498",{"slug":8979,"name":8979,"fn":8980,"description":8981,"org":8982,"tags":8983,"stars":28,"repoUrl":29,"updatedAt":8991},"agent-observability-experiment-bootstrap","bootstrap LLM observability experiments","Bootstrap a reproducible LLM Observability experiment through the Python ddtrace SDK or the Node dd-trace SDK. Use for experiment, dataset, evaluator, benchmark, regression, or LLM-as-a-judge scaffolding. The legacy Python invocation remains supported.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8984,8985,8986,8989,8990],{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8987,"slug":8988,"type":15},"Experiments","experiments",{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},"2026-08-22T03:56:54.562327",{"slug":8993,"name":8993,"fn":8994,"description":8995,"org":8996,"tags":8997,"stars":28,"repoUrl":29,"updatedAt":9005},"agent-observability-replay-trace","iterate on LLM traces with local code","Use when a developer wants to iterate on ONE specific Agent Observability \u002F LLM Obs trace whose output they didn't like — re-running that trace against their LOCAL code, seeing a concise diff of the old vs new output, and looping (change code → replay → diff) until satisfied. Invoked as \u002Fagent-observability-replay-trace \u003Ctrace-id> [changes to test]. Signals: \"replay this trace\"; \"iterate on a trace\"; \"this trace's output is wrong, fix it and re-run\"; \"re-run trace \u003Cid> with \u003Cchange>\"; pasting a trace id from the Agent Observability UI with a description of what to fix. It fetches the trace via the datadog-llmo MCP or the pup CLI, edits code, re-runs the app to emit a NEW trace, and diffs the two — no local server, no browser. For agents traced with ddtrace \u002F LLM Obs (Python first-class), with JSON-serializable entry input. Do NOT use for: scored Experiments or the browser \"Replay\" button (that's agent-observability-replay-experiment), building an experiment from a dataset\u002FCSV, writing evaluators, root-causing failed traces, or RUM\u002FHTTP session replay.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[8998,8999,9000,9001,9002],{"name":20,"slug":21,"type":15},{"name":8927,"slug":8928,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},{"name":9003,"slug":9004,"type":15},"Tracing","tracing","2026-08-06T05:38:08.57055",{"slug":9007,"name":9007,"fn":9008,"description":9009,"org":9010,"tags":9011,"stars":28,"repoUrl":29,"updatedAt":9017},"agent-observability-session-classify","evaluate user intent satisfaction in sessions","Classify whether user intent was satisfied in a Datadog Agent Observability trace or session. Three modes: (1) session_id — classify a single CMD+I assistant session with RUM; (2) trace_id — classify a single Agent Observability trace without RUM; (3) ml_app — sample and classify multiple sessions or traces from a given LLM app. Output is compact by default (verdict + one-sentence reason). Use when evaluating satisfaction, classifying sessions\u002Ftraces, labeling data, or generating signal for agent-observability-eval-pipeline or agent-observability-trace-rca.\n",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9012,9013,9014,9015,9016],{"name":8971,"slug":8972,"type":15},{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},"2026-06-19T09:04:26.341497",{"slug":9019,"name":9019,"fn":9020,"description":9021,"org":9022,"tags":9023,"stars":28,"repoUrl":29,"updatedAt":9029},"agent-observability-trace-rca","diagnose LLM application failures from traces","Root cause analysis on production LLM traces. Diagnoses why an LLM application is failing — works from eval judge verdicts, runtime errors, or structural anomalies depending on what signals are present. Walks the span tree from symptom to root cause. Use when user says \"what's wrong with my app\", \"why is my eval failing\", \"analyze errors\", \"root cause analysis\", \"diagnose failures\", or wants to understand production failure patterns.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9024,9025,9026,9027,9028],{"name":20,"slug":21,"type":15},{"name":8927,"slug":8928,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},"2026-06-19T09:04:24.316244",{"slug":9031,"name":9031,"fn":9032,"description":9033,"org":9034,"tags":9035,"stars":28,"repoUrl":29,"updatedAt":9039},"agent-skills","use Datadog observability skills","Datadog skills for AI agents. Essential monitoring, logging, tracing and observability.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9036,9037,9038],{"name":20,"slug":21,"type":15},{"name":17,"slug":18,"type":15},{"name":8916,"slug":8917,"type":15},"2026-08-29T09:21:41.362479",{"slug":9041,"name":9041,"fn":9042,"description":9043,"org":9044,"tags":9045,"stars":28,"repoUrl":29,"updatedAt":9059},"datadog-app","build and manage Datadog applications","Guides developers building Datadog Apps with TypeScript, React, the @datadog\u002Fapps scaffolder, and @datadog\u002Fvite-plugin. Use when a user wants to scaffold, run, debug, upgrade, build, upload, publish, upload without publishing (draft upload), add an upload-no-publish script, set up CI\u002FCD, use OAuth or API\u002Fapplication key auth, trigger\u002Fpoll Workflow Automation, choose DDSQL or Action Catalog for backend data access, or query app datastores with DDSQL, including backend function troubleshooting.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9046,9047,9050,9053,9056],{"name":20,"slug":21,"type":15},{"name":9048,"slug":9049,"type":15},"Frontend","frontend",{"name":9051,"slug":9052,"type":15},"React","react",{"name":9054,"slug":9055,"type":15},"TypeScript","typescript",{"name":9057,"slug":9058,"type":15},"Vite","vite","2026-06-18T08:01:32.562331",{"slug":1320,"name":1320,"fn":9061,"description":9062,"org":9063,"tags":9064,"stars":28,"repoUrl":29,"updatedAt":9070},"configure Datadog account authentication","Ensure the user has an authenticated Datadog account with a valid DD_API_KEY on the right region before any Datadog setup or instrumentation. Detects existing DD_API_KEY \u002F DD_APP_KEY \u002F DD_SITE, validates them against the Datadog API, and fixes the common wrong-region 403. If no usable key exists, signs the user in (OAuth) or creates a new account, then obtains and validates a key. Use this whenever a user needs a Datadog account or API key, hits a 403 \u002F wrong-region error, or is about to run any Datadog *-setup or instrumentation skill.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9065,9068,9069],{"name":9066,"slug":9067,"type":15},"Configuration","configuration",{"name":20,"slug":21,"type":15},{"name":8916,"slug":8917,"type":15},"2026-08-16T04:01:04.151248",43,{"items":9073,"total":9128},[9074,9081,9089,9096,9104,9112,9120],{"slug":8903,"name":8903,"fn":8904,"description":8905,"org":9075,"tags":9076,"stars":28,"repoUrl":29,"updatedAt":8918},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9077,9078,9079,9080],{"name":20,"slug":21,"type":15},{"name":8910,"slug":8911,"type":15},{"name":8913,"slug":8914,"type":15},{"name":8916,"slug":8917,"type":15},{"slug":8920,"name":8920,"fn":8921,"description":8922,"org":9082,"tags":9083,"stars":28,"repoUrl":29,"updatedAt":8936},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9084,9085,9086,9087,9088],{"name":20,"slug":21,"type":15},{"name":8927,"slug":8928,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},{"slug":8938,"name":8938,"fn":8939,"description":8940,"org":9090,"tags":9091,"stars":28,"repoUrl":29,"updatedAt":8947},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9092,9093,9094,9095],{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},{"slug":8949,"name":8949,"fn":8950,"description":8951,"org":9097,"tags":9098,"stars":28,"repoUrl":29,"updatedAt":8963},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9099,9100,9101,9102,9103],{"name":8955,"slug":8956,"type":15},{"name":8958,"slug":8959,"type":15},{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8916,"slug":8917,"type":15},{"slug":8965,"name":8965,"fn":8966,"description":8967,"org":9105,"tags":9106,"stars":28,"repoUrl":29,"updatedAt":8977},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9107,9108,9109,9110,9111],{"name":8971,"slug":8972,"type":15},{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},{"slug":8979,"name":8979,"fn":8980,"description":8981,"org":9113,"tags":9114,"stars":28,"repoUrl":29,"updatedAt":8991},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9115,9116,9117,9118,9119],{"name":20,"slug":21,"type":15},{"name":8930,"slug":8931,"type":15},{"name":8987,"slug":8988,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},{"slug":8993,"name":8993,"fn":8994,"description":8995,"org":9121,"tags":9122,"stars":28,"repoUrl":29,"updatedAt":9005},{"slug":8,"name":9,"logoUrl":10,"githubOrg":8},[9123,9124,9125,9126,9127],{"name":20,"slug":21,"type":15},{"name":8927,"slug":8928,"type":15},{"name":8933,"slug":8934,"type":15},{"name":8916,"slug":8917,"type":15},{"name":9003,"slug":9004,"type":15},41]