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Investigate why a specific skill’s tests failed and surface error details.
This is an open-source prompt-only skill with no declared secrets or remote endpoints, so the overall risk is low. Caution is still warranted because its documentation explicitly targets a specific Azure Storage account and instructs use of external tools/commands to enumerate and download test result data, so data access and potential egress should be tightly scoped.
The material states no keys or environment variables are required, and the skill itself does not ask the user to provide secrets. Although the README references Azure Storage and az commands, it does not explicitly request exposing or returning credentials, and no concrete credential-abuse red flags are shown.
The README explicitly names Azure Storage account `strdashboarddevveobvk` and container `integration-reports`, and instructs using MCP/az to download blob contents; this implies connections to a declared cloud endpoint and possible transfer of test-result data. This is normal for the stated function, and there is no evidence of exfiltration to unknown or unrelated endpoints.
The system classifies it as prompt-only, so the skill itself does not contain auditable local execution logic; however, the documentation instructs use of commands such as `az storage blob download` and `echo $env:TEMP` and references local file links, indicating potential execution of external tools/commands in the usage flow. This is a normal tool capability, with no evidence of unusual system privilege requests.
Its stated purpose is to read integration test result files from a specific storage account and download them to a local temp directory for inspection; accessible data includes blob names, metadata, and test-result contents. This access is aligned with the declared function, but it may include internal error details, so it should be constrained to the specified container/path and avoid overbroad reads.
The source is a Microsoft GitHub repository and is marked open-source, which provides auditability; 222 stars offer some positive community-adoption signal. The missing license declaration and unknown maintenance status add minor uncertainty, but not enough to constitute a high-risk supply-chain red flag.
Copy the install command and let the AI configure it · recommended for beginners
Please install the "analyze-skill-issues" skill from askskill: 1. Download https://raw.githubusercontent.com/microsoft/GitHub-Copilot-for-Azure/main/.github/skills/analyze-skill-issues/SKILL.md 2. Save it as ~/.claude/skills/analyze-skill-issues/SKILL.md 3. Reload skills and tell me it's ready
Analyze why the skill "email-summarizer" is failing tests. Read the integration test results and summarize the main errors, failed cases, and likely causes.
A summary of failed tests for that skill, including error highlights, key log clues, and likely failure causes.
Investigate test errors for the skill "invoice-parser". List the error message for each failed test, related files, and recurring failure patterns.
Structured failure details that help quickly determine whether the issue is configuration, dependency, or logic related.
Why have the tests for the skill "pdf-translator" been failing recently? Use the stored test result files to identify the root cause and suggest fixes.
A root cause analysis of the failures, plus actionable fix directions or next troubleshooting steps.
Queries the strdashboarddevveobvk Azure Storage account that stores all integration test results and retrieves error details for a given skill.
| Property | Value |
|---|---|
| Storage account | strdashboarddevveobvk |
| Container | integration-reports |
| Blob path pattern | {date}/{run_id}/{skill_name}/[{test_name}/]<file> — see Blob Path Layout |
| Blob discovery | mcp_azure_mcp_storage_blob_get — list blobs to find the right paths |
| Blob content download | az storage blob download ... --file "$env:TEMP\<filename>" — local path reported in summary |
| Best for | Diagnosing why a skill's integration tests are failing |
TOOL USAGE — MANDATORY: Use
mcp_azure_mcp_storage_blob_getto enumerate and identify the correct blob paths. Then useaz storage blob downloadto download and read the content of those blobs. These tools serve different purposes and are both always required — they are not alternatives.
Resolve the user's skill name to its canonical directory name using the Skill Name Mapping.
MCP tools are used exclusively for blob discovery — finding which blob paths exist. All calls require account: "strdashboarddevveobvk" and container: "integration-reports". Use az storage blob download (see Phase 2) to read blob content.
| Tool | Purpose | Key parameters |
|---|---|---|
mcp_azure_mcp_storage_blob_get | List blob names and metadata (does not return file content) | account, container; omit blob to list all |
mcp_azure_mcp_storage_blob_container_get | Verify the container exists | account, container |
Every response MUST include all of the following. Do not omit any item:
vscode://file/ link⚠️ MANDATORY: The Referenced Files section is not optional. Always resolve
$env:TEMPto the actual path (runecho $env:TEMP) and include a clickable link for every file downloaded. If this section is missing from your response, the output is incomplete.
TOOL RESPONSIBILITIES:
mcp_azure_mcp_storage_blob_getis used exclusively for blob discovery (listing available paths).az storage blob downloadis used exclusively for reading blob content. Both are always required.
Resolve the skill name using the Skill Name Mapping.
List all blobs in the container to discover available date/run paths:
mcp_azure_mcp_storage_blob_get({ account: "strdashboarddevveobvk", container: "integration-reports" })
This returns a flat list of all blob names. Filter for entries that:
yyyy-mm-dd within the last 7 days/{skill_name}/ in the pathtoken-usage.json or agent-metadata.jsonGroup the matching blobs by date (descending) and by run ID (the second path segment).
Limit scope: If the user specified a date in their prompt, use only that date. Otherwise, take the 3 most recent dates from the filtered results and discard the rest. Do not process more than 3 dates.
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Analyze and compress Markdown to reduce tokens and improve AI efficiency.
Write, review, and standardize Agent Skills to match agentskills.io requirements.
Discover Azure OpenAI capacity, quotas, and best regions for deployment.
Create, validate, and run eval.yaml suites for agent skill evaluation.
Query and analyze logs, telemetry, and time-series data in Azure Data Explorer.
Assess AKS Automatic readiness and generate fixes for migration blockers.
Investigate failing GitHub integration tests and suggest likely fixes
Review skill PRs with severity-rated feedback on quality and repository conventions.
Create, refine, and evaluate AI skills for better performance and triggering accuracy.
Turn a repeated workflow into a reusable skill file from the current session.
Iteratively audit and fix skill frontmatter compliance, scoring, and token issues.
Find reusable skills across local and online sources before creating one.