assign-offline-profile

Utilisez lorsque l'utilisateur doit lier des utilisateurs ou des équipes à un profil hors ligne mobile afin qu'ils reçoivent effectivement la synchronisation hors ligne sur leurs appareils. Sans cela, le profil…

npx skills add https://github.com/microsoft/power-platform-skills --skill assign-offline-profile

Shared instructions: shared-instructions.md — read first.

References:

Assign Offline Profile

Bind one or more users and/or teams to an existing Mobile Offline Profile. Without this step, the profile exists in Dataverse but is unbound — no one's app actually uses it for offline sync.

Per the maker portal's UX (the "Assign profile to user" dialog under env settings), this is a separate operation from profile creation. Many users hit "I created the profile but offline still doesn't work" — the missing piece is membership.

Workflow

  1. Verify project + locate profile → 2. Pick users/teams → 3. Discover existing memberships → 4. Confirm diff (single gate) → 5. POST memberships → 6. Verify → 7. Summary

Step 1 — Verify project + locate profile

test -f power.config.json
node "${PLUGIN_ROOT}/scripts/resolve-environment.js" "$(node -e \"console.log(require('./power.config.json').environmentId)\")"

Profile ID resolution (in order):

SourceUsed when
$ARGUMENTS contains --profile-id <guid>Explicit override
$ARGUMENTS contains --profile-name <name>Resolve via GET /mobileofflineprofiles?$filter=name eq '<name>'&$select=mobileofflineprofileid
offline-profile.json in cwdRead top-level profileId field
OtherwiseGET /mobileofflineprofiles and present AskUserQuestion with the list (max 4 options)

STOP if no profile can be resolved. Print: Run /setup-offline-profile first, or pass --profile-id.

power.config.json is intentionally NOT consulted here. That file is owned by npx power-apps init. The profile ID lives in offline-profile.json only.

Step 2 — Pick users/teams

$ARGUMENTS parsing:

FlagEffect
--user <upn> (repeatable)Add specific user(s) by UPN (user@domain.com)
--team <name> (repeatable)Add specific team(s) by name
--meAdd the current Dataverse user from WhoAmI / systemusers(<UserId>) — useful for solo dev demos
--all-app-usersAdd every user with System User role in the current env (broad; intended for prod rollout — confirm at gate)
--unassign-user <upn> / --unassign-team <name>Remove an existing membership rather than add

If no flags passed, present AskUserQuestion:

Question: "Who should receive this offline profile?"

Options (max 4):

  • Just me (the current user) — equivalent to --me
  • Pick specific users by UPN — you reply with comma-separated emails in the next message
  • Pick a team — list env's teams and pick one
  • All users with System User role — equivalent to --all-app-users; broad scope, confirm at gate

For pick-users flow: after the choice, print:

"Reply with comma-separated UPNs (e.g. rm1@contoso.com, rm2@contoso.com)"

Then read the next user message and parse.

Step 3 — Discover existing memberships

Telemetry checkpoint: discover_offline_profile_memberships

For idempotency:

# Existing user memberships for this profile
node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET \
  "usermobileofflineprofilememberships?\$filter=_mobileofflineprofileid_value eq <profileId>&\$select=usermobileofflineprofilemembershipid,_systemuserid_value&\$expand=systemuserid_systemuser(\$select=domainname)"

# Existing team memberships for this profile
node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET \
  "teammobileofflineprofilememberships?\$filter=_mobileofflineprofileid_value eq <profileId>&\$select=teammobileofflineprofilemembershipid,_teamid_value&\$expand=teamid_team(\$select=name)"

Build the set of already-bound UPNs and team names.

For each candidate user/team from Step 2, look up their systemuserid / teamid (skip if already in already-bound):

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET \
  "systemusers?\$filter=domainname eq '<upn>'&\$select=systemuserid,fullname,domainname&\$top=1"

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> GET \
  "teams?\$filter=name eq '<team-name>' and teamtype eq 0&\$select=teamid,name&\$top=1"

(teamtype eq 0 excludes Access Teams and Owner Teams — only Manage Teams get profile assignments.)

Construct three lists:

  • to_add — resolved IDs to POST
  • to_remove — resolved IDs to DELETE (from --unassign-* flags)
  • not_found — UPNs/team-names that didn't resolve (warn)
  • already_bound — skipped no-ops

Step 4 — Confirm diff (single gate)

Telemetry checkpoint: confirm_offline_profile_assignment_diff

AskUserQuestion:

Question header: Confirm membership changes

Question body:

Profile: <name> (<profileId>)

Will ADD:
  - User: rahul@contoso.com (Rahul Bansal)
  - User: charanma@... (Charan Mahankali)
  - Team: Field Service RMs (12 members)

Will REMOVE:
  (none)

Already bound (skipping):
  - User: admin@... (no-op)

Could not resolve:
  - someone@external.com — not in this env's system users

Proceed?

Options:

  • Proceed
  • Cancel

Step 5 — POST memberships

Telemetry checkpoint: assign_offline_profile_memberships

For each in to_add, POST sequentially (parallel POSTs occasionally return 429):

User membership:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST \
  "usermobileofflineprofilememberships" \
  --body '{
    "MobileOfflineProfileId@odata.bind": "/mobileofflineprofiles(<profileId>)",
    "SystemUserId@odata.bind": "/systemusers(<systemuserid>)"
  }' \
  --include-headers

Expected 204 with OData-EntityId → capture membership GUID.

Team membership:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> POST \
  "teammobileofflineprofilememberships" \
  --body '{
    "MobileOfflineProfileId@odata.bind": "/mobileofflineprofiles(<profileId>)",
    "TeamId@odata.bind": "/teams(<teamid>)"
  }' \
  --include-headers

For each in to_remove, DELETE:

node "${PLUGIN_ROOT}/scripts/dataverse-request.js" <envUrl> DELETE \
  "usermobileofflineprofilememberships(<membershipid>)"

⚠️ Duplicate handling: POSTing a membership that already exists returns 409 Conflict. The dataverse-request.js wrapper's looksLikeDuplicate rescue treats this as silent success (the Step 3 dedup should catch most cases first). Re-runs are safe.

Step 6 — Verify

Telemetry checkpoint: verify_offline_profile_memberships

Re-query memberships from Step 3 and assert the diff applied:

  • Every to_add now appears in the GET response
  • Every to_remove no longer appears

If the verification disagrees, return BLOCKED: membership writes did not commit and print the discrepancy.

Step 7 — Summary

Print:

✓ Membership updates applied.

  Profile      : <name>
  Total members: <N users + M teams>
  Added        : <list>
  Removed      : <list>
  Skipped      : <list> (already bound)

Users will receive the profile on their next mobile app sign-in. Existing
sessions need to sign out + sign in to trigger the profile pull.

Update memory-bank.md ## Offline profile block:

membership:
  users: [rahul@..., charanma@...]
  teams: [Field Service RMs]
  lastAssignedAt: 2026-05-19T...

Status code (final line)

  • DONE — every requested add/remove applied; verify confirmed
  • DONE_WITH_CONCERNS: <list> — some UPNs/teams could not be resolved, or --all-app-users matched 0 users (env may not have the role granted yet)
  • NEEDS_CONTEXT: <missing> — couldn't determine profileId (no offline-profile.json, no --profile flags, no profiles in env)
  • BLOCKED: <reason> — auth failure, profile not found in env, or verification disagreement

Failure recovery

Memberships are individually committed (no transaction). If Step 5 fails mid-loop:

  • Partially-added memberships remain (visible in env)
  • Re-running with the same arguments is idempotent (Step 3 dedup catches what's already bound)
  • Use --unassign-* to undo specific bindings if needed

Plus de skills de microsoft

oss-growth
microsoft
Persona de growth hacker OSS
agent-framework-azure-ai-py
microsoft
Créez des agents Azure AI Foundry à l’aide du SDK Python Microsoft Agent Framework (agent-framework-azure-ai). À utiliser lors de la création d’agents persistants avec AzureAIAgentsProvider, de l’utilisation d’outils hébergés (interpréteur de code, recherche de fichiers, recherche web), de l’intégration de serveurs MCP, de la gestion de fils de conversation ou de l’implémentation de réponses en streaming. Couvre les outils de fonction, les sorties structurées et les agents multi-outils.
development
airunway-aks-setup
microsoft
Configurez AI Runway sur AKS — du cluster nu au modèle en cours d'exécution. Couvre la vérification du cluster, l'installation du contrôleur, l'évaluation GPU, la configuration du fournisseur et le premier déploiement. QUAND : « configurer AI Runway », « intégrer un cluster AKS », « installer AI Runway », « configuration airunway », « déployer un modèle sur AKS », « inférence GPU sur AKS », « configuration KAITO sur AKS », « exécuter LLM sur AKS », « vLLM sur AKS », « configurer le service de modèles sur AKS », « contrôleur AI Runway ».
devops
appinsights-instrumentation
microsoft
Conseils pour instrumenter les applications web avec Azure Application Insights. Fournit des modèles de télémétrie, la configuration du SDK et des références de configuration. QUAND : comment instrumenter une application, SDK App Insights, modèles de télémétrie, qu'est-ce qu'App Insights, conseils sur Application Insights, exemples d'instrumentation, bonnes pratiques APM.
devops
applicationinsights-web-ts
microsoft
Instrumentez les applications navigateur/web avec le SDK JavaScript Application Insights (@microsoft/applicationinsights-web). Utilisez-le pour la surveillance des utilisateurs réels (RUM) — vues de page, clics, dépendances AJAX/fetch, exceptions, événements personnalisés et traces d’agents GenAI côté navigateur corrélées aux traces OpenTelemetry backend. Couvre le script de chargement du SDK et la configuration npm, les extensions de framework (React, React Native, Angular), Click Analytics, les initialiseurs de télémétrie et les conventions sémantiques OTel GenAI pour les spans d’agents/outils/modèles émises depuis le navigateur.
devops
azure-ai-anomalydetector-java
microsoft
Créez des applications de détection d'anomalies avec le SDK Azure AI Anomaly Detector pour Java. Utilisez-le lors de l'implémentation de la détection d'anomalies univariées/multivariées, de l'analyse de séries temporelles ou de la surveillance basée sur l'IA.
development
azure-ai-language-conversations-py
microsoft
Implémentez la compréhension du langage conversationnel (CLU) à l’aide du SDK Python azure-ai-language-conversations. Utilisez-le lorsque vous travaillez avec ConversationAnalysisClient pour analyser l’intention et les entités d’une conversation, créer des fonctionnalités de NLP ou intégrer la compréhension du langage dans des applications.
development
azure-ai-ml-py
microsoft
SDK v2 d’Azure Machine Learning pour Python. Utiliser pour les espaces de travail ML, les tâches, les modèles, les jeux de données, le calcul et les pipelines. Déclencheurs : « azure-ai-ml », « MLClient », « espace de travail », « registre de modèles », « tâches d’entraînement », « jeux de données ».
development