powercat-overflow

Analyse chaque flux cloud Power Automate dans une solution Power Platform (.zip) par rapport aux directives de codage de Microsoft et produit un…

npx skills add https://github.com/microsoft/power-cat-skills --skill powercat-overflow

PowerCAT-Overflow

Review every Power Automate cloud flow in a Power Platform solution ZIP against Microsoft's coding guidelines, write a single solution-level findings JSON next to the uploaded solution, then open the hosted PowerCAT-Overflow viewer with both files loaded.

Hosted viewer: https://microsoft.github.io/power-cat-skills/PowerCAT-Overflow.html

Step 0 — Load the authoritative source list

Before doing any analysis, fetch the canonical source list with web_fetch (raw: true):

https://raw.githubusercontent.com/microsoft/power-cat-skills/refs/heads/main/Common/PowerCAT%20OverFlow/sources.md

Parse the Markdown link list and treat that set as the only anchor citations allowed in findings. If the fetch fails (network error, 404, empty body), stop and tell the user the source list is unreachable — do not proceed with a review. Cache the parsed list in memory for the rest of this run.

Step 1 — Locate the input solution

  1. Prefer a .zip path in <tagged_files>. Use the most recent match.
  2. If none, ask via m_ask_user: "Please attach the Power Platform solution .zip you'd like me to review."
  3. Reject anything that isn't a ZIP. The skill does not accept loose JSON files in this mode.

Remember the absolute path of the ZIP and its parent directory — both are needed later.

Step 2 — Unpack & enumerate flows

Unpack the solution to a temp working directory (Expand-Archive on Windows, unzip elsewhere).

A valid Power Platform solution ZIP contains:

  • solution.xml at the root
  • a Workflows/ folder with one or more <FriendlyName>-<GUID>.json files (and matching .xml sidecars you can ignore for analysis)

Read solution.xml and extract:

  • Display name → solution.name
  • Unique name (publisher-prefixed, the UniqueName element) → solution.uniqueName
  • Version (the Version element) → solution.version

For each *.json in Workflows/:

  • Compute the friendly name by stripping the trailing -<GUID>.json suffix (e.g. cf_AcknowledgeCaseId-1234abcd-...-....json → cf_AcknowledgeCaseId). This friendly name is the key under which the flow's findings will be stored.
  • Load the JSON, then unwrap properties.definition → definition if present, exactly as before.

If Workflows/ is missing or empty, stop and tell the user the ZIP isn't a flow-bearing solution.

Step 3 — Analyse each flow

For every flow, walk all actions recursively (actions, else.actions, cases.*.actions, default.actions) and assign each finding an impact of low, medium, or high. Where a finding clearly points at a specific action, capture its name in the action field (must match the action key exactly, case-sensitive; triggers count). Cite only URLs from the Step 0 source list — never invent or paraphrase URLs.

Complexity

  • Total action count over 50, or nesting depth over 4 → high.
  • Many top-level Initialize_Variable actions, or many sibling Scopes that could be split into child flows → medium/high.

Maintainability

  • Hardcoded URLs / tenants / phone numbers / email addresses → high.
  • Magic numbers driving business policy (license cost, approval thresholds, quotas, poll intervals) → high.
  • Inconsistent action naming or missing description / peek-code notes → medium.
  • Flow not authored inside a solution (no env-var references) → medium.
  • Duplicate flows (Copyof-…, Test, Test2, MayankTest, etc.) shipped alongside originals → high (also surface at solution level).

Security

  • Generated credentials/secrets in HTTP bodies without runtimeConfiguration.secureData.properties covering inputs and/or outputs → high.
  • A single $authentication parameter reused across distinct external vendors → high.
  • OData $filter or SQL fragments built by interpolating triggerBody() / user input → high.
  • Request trigger without auth posture (no Entra/SAS/IP allow-list) → high.
  • Sensitive PII (salary, SSN, DOB, phone) in HTTP body without secureInputs → high.
  • HTML/email bodies built via raw concat() of user input → medium.
  • Instrumentation keys / connection strings in request bodies → medium.

Performance

  • Reference to outputs('X') / body('X') where X belongs to a sibling Switch case or sibling parallel branch → high.
  • Compensation/rollback logic that doesn't cover every resource the flow created → high.
  • Reference to fields not declared in the trigger schema → high.
  • HTTP action with no explicit retryPolicy → medium (group similar ones per flow).
  • Wait actions with long fixed delays, or Until loops with > 30 iterations / > 1 h total → medium.
  • Foreach without explicit runtimeConfiguration.concurrency → medium.
  • Catch / final scopes that only inspect a subset of upstream scopes → medium.
  • Explicit, sensible concurrency on Foreach loops → low (positive callout).

For each flow, produce 1–4 category objects (Complexity, Maintainability, Security, Performance in that order). Each category's impact = the highest impact among its items. 3–10 items per category is ideal; don't pad.

For every finding item, always include a fix field — a single actionable sentence (≤ 30 words, start with a verb) telling the developer exactly what to change. Examples:

  • "Enable secure inputs on HTTP action 'Send_Request' via Settings → Secure Inputs."
  • "Replace the hardcoded URL in 'Initialize_Variable_Endpoint' with an environment variable."
  • "Add concurrency (recommend: 10) to the Apply-to-each in its Settings panel."

Step 4 — Build the solution-level roll-up

Collect across all flows:

  • Per-category roll-up (1–4 entries): for each category that has at least one finding anywhere in the solution, compute the highest impact seen, write a 1–2 sentence summary, and set flowsAffected = how many flows had at least one item in that category.
  • topRisks: 3–8 cross-flow high-priority items (mostly impact: high, a few medium if they affect the executive verdict). Each risk has label, desc, impact, optional category, optional fix (same single-sentence remediation convention as per-flow items), and — when applicable — flow (friendly name) and action (so the viewer can deep-link).
  • stats: flowCount, totalActions (sum of action counts across flows), highImpactFlows (flows where any category rolled up to high), flowsReviewed (= flowCount unless one failed to parse).
  • summary: a narrative paragraph (markdown OK) giving the overall verdict, the top 1–3 priorities, and any patterns (duplicate flows, naming drift, security posture).

Step 5 — Write the Solution Findings file

Schema (authoritative): https://raw.githubusercontent.com/microsoft/power-cat-skills/refs/heads/main/Common/PowerCAT%20OverFlow/solution.findings.schema.json Example: https://raw.githubusercontent.com/microsoft/power-cat-skills/refs/heads/main/Common/PowerCAT%20OverFlow/solution.findings.sample.json

Shape:

{
  "solution": {
    "name": "...",
    "uniqueName": "...",
    "version": "...",
    "summary": "...",
    "categories": [ { "category": "...", "impact": "...", "summary": "...", "flowsAffected": 0 } ],
    "topRisks":   [ { "label": "...", "desc": "...", "fix": "...", "impact": "...", "category": "...", "flow": "...", "action": "..." } ],
    "stats":      { "flowCount": 0, "totalActions": 0, "highImpactFlows": 0, "flowsReviewed": 0 }
  },
  "flows": {
    "<FriendlyFlowName>": [
      { "category": "Complexity",      "impact": "low|medium|high", "items": [ { "label": "...", "desc": "...", "fix": "...", "impact": "...", "action": "..." } ] },
      { "category": "Maintainability", "impact": "...", "items": [ ... ] },
      { "category": "Security",        "impact": "...", "items": [ ... ] },
      { "category": "Performance",     "impact": "...", "items": [ ... ] }
    ]
  }
}

Output filename: <OriginalSolutionZipBaseName>.findings.json Output location: the same folder as the uploaded .zip.

Validate the output against the JSON Schema before saving (at minimum: required keys present, category enum values, impact enum values, additionalProperties=false respected). If validation fails, fix and re-validate before continuing.

Step 6 — Launch the hosted viewer and load both files

Open the hosted PowerCAT-Overflow viewer and upload the two files using the Playwright browser tools:

  1. playwright-browser_navigate → https://microsoft.github.io/power-cat-skills/PowerCAT-Overflow.html
  2. playwright-browser_snapshot to discover the two file inputs (one for the solution .zip, one for the findings .json). The labels in the UI clearly distinguish them.
  3. playwright-browser_file_upload once per input, passing the absolute paths in the right order. If the page exposes a single chooser that opens twice, call the upload tool twice with the appropriate path each time.
  4. playwright-browser_snapshot again to confirm both files are accepted (look for the rendered solution overview / flow list).

If Playwright is unavailable, fall back to Start-Process "https://microsoft.github.io/power-cat-skills/PowerCAT-Overflow.html" and tell the user the two exact file paths to upload manually.

Step 7 — Hand over

Write a short chat message (≤150 words) containing:

  1. A 4-row score table (Complexity / Maintainability / Security / Performance — solution roll-up impact).
  2. The top 3 risks from solution.topRisks (one line each, with the flow name when present).
  3. The absolute path of the .findings.json file you wrote.
  4. The viewer URL.
  5. One-line note that source citations were loaded fresh from upstream sources.md.

End with: "Handing over — explore the solution in the open viewer tab."

Hard rules

  • Never invent action names. Only use names that appear in the flow's actions / triggers keys.
  • Never paste secrets, tokens, or full PII payloads back into chat.
  • Never write the user's flow JSON or solution ZIP to any external destination; all artefacts stay local.
  • Never cite a guideline URL that isn't in the Step 0 source list.
  • The output JSON must validate against the published solution.findings.schema.json.
  • Friendly flow names in flows keys must match the rule: ZIP filename minus the trailing -<GUID>.json.

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