Convert raw notes, error logs, voice dictation, or screenshots into crisp GitHub-flavored markdown issue reports. Use when the user pastes bug info, error messages, or informal descriptions and wants a structured GitHub issue. Supports images/GIFs for visual evidence.
中文简介正在准备中。当前显示原文说明。
查看安装方法安装前可以查看交给 AI 代理的指令内容。
Transform messy input (error logs, voice notes, screenshots) into clean, actionable GitHub issues.
## Summary
[One-line description of the issue]
## Environment
- **Product/Service**:
- **Region/Version**:
- **Browser/OS**: (if relevant)
## Reproduction Steps
1. [Step]
2. [Step]
3. [Step]
## Expected Behavior
[What should happen]
## Actual Behavior
[What actually happens]
## Error Details
[Error message/code if applicable]
## Visual Evidence
[Reference to attached screenshots/GIFs]
## Impact
[Severity: Critical/High/Medium/Low + brief explanation]
## Additional Context
[Any other relevant details]
Create issues as markdown files in /issues/ directory at the repo root. Use naming convention: YYYY-MM-DD-short-description.md
Be crisp: No fluff. Every word should add value.
Extract structure from chaos: Voice dictation and raw notes often contain the facts buried in casual language. Pull them out.
Infer missing context: If user mentions "same project" or "the dashboard", use context from conversation or memory to fill in specifics.
Placeholder sensitive data: Use [PROJECT_NAME], [USER_ID], etc. for anything that might be sensitive.
Match severity to impact:
Image/GIF handling: Reference attachments inline. Format: 
Input (voice dictation):
so I was trying to deploy the agent and it just failed silently no error nothing the workflow ran but then poof gone from the list had to refresh and try again three times
Output:
## Summary
Agent deployment fails silently - no error displayed, agent disappears from list
## Environment
- **Product/Service**: Azure AI Foundry
- **Region/Version**: westus2
## Reproduction Steps
1. Navigate to agent deployment
2. Configure and deploy agent
3. Observe workflow completes
4. Check agent list
## Expected Behavior
Agent appears in list with deployment status, errors shown if deployment fails
## Actual Behavior
Agent disappears from list. No error message. Requires page refresh and retry.
## Impact
**High** - Blocks agent deployment workflow, no feedback on failure cause
## Additional Context
Required 3 retry attempts before successful deployment
Input (error paste):
Error: PERMISSION_DENIED when publishing to Teams channel. Code: 403. Was working yesterday.
Output:
## Summary
403 PERMISSION_DENIED error when publishing to Teams channel
## Environment
- **Product/Service**: Copilot Studio → Teams integration
- **Region/Version**: [REGION]
## Reproduction Steps
1. Configure agent for Teams channel
2. Attempt to publish
## Expected Behavior
Agent publishes successfully to Teams channel
## Actual Behavior
Returns `PERMISSION_DENIED` with code 403
## Error Details
Error: PERMISSION_DENIED Code: 403
## Impact
**High** - Blocks Teams integration, regression from previous working state
## Additional Context
Was working yesterday - possible permission/config change or service regression
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Build Azure AI Foundry agents using the Microsoft Agent Framework Python SDK (agent-framework-azure-ai). Use when creating persistent agents with AzureAIAgentsProvider, using hosted tools (code interpreter, file search, web search), integrating MCP servers, managing conversation threads, or implementing streaming responses. Covers function tools, structured outputs, and multi-tool agents.
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Set up AI Runway on AKS — from bare cluster to running model. Covers cluster verification, controller install, GPU assessment, provider setup, and first deployment. WHEN: "setup AI Runway", "onboard AKS cluster", "install AI Runway", "airunway setup", "deploy model to AKS", "GPU inference on AKS", "KAITO setup on AKS", "run LLM on AKS", "vLLM on AKS", "set up model serving on AKS", "AI Runway controller".
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Diagnose Day-2 AKS GPU and KAITO incidents using profile-aware, read-only evidence. WHEN: 'Insufficient nvidia.com/gpu', GPU pod Pending, model-load OOM, DCGM/VRAM, KAITO Workspace not ready, or GPU autoscaling. DO NOT USE FOR: setup (airunway-aks-setup), non-GPU incidents (aks-troubleshooting), standalone VM quota (azure-quotas), or generic cost (cost-analysis or cost-optimization from the optional azure-cost plugin).
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Lookup documented AKS fixes only when the prompt includes an exact catalog signature and all of its qualifiers: VMCannotFitEphemeralOSDisk; NodePoolMcVersionIncompatible; 'NodeImageVersion is not accepted'; AKS SkuNotAvailable with size, location, and zone; ZonalAllocationFailed with insufficient zone capacity; OverconstrainedAllocationRequest with listed constraints; nested AKS vmssCSE/CSE VMExtensionError_OutboundConnFail, VMExtensionError_K8SAPIServerConnFail, or VMExtensionError_K8SAPIServerDNSLookupFail; or AllocationFailed with the full cataloged internal-error or insufficient-regional-capacity message. Never use for quota errors, code-only or bare wrappers, generic symptoms, incomplete signatures, or failures outside AKS; use aks-troubleshooting or azure-diagnostics.
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Collects bounded packet captures from AKS nodes and Azure network configuration for wire-level evidence. WHEN: "capture packets on an AKS node", "take a pcap", "run tcpdump on AKS", "prove where packets drop". Use for explicit packet-capture intent after read-only diagnostics, not general AKS connectivity or ingress troubleshooting.
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Debug live Azure Kubernetes Service (AKS) incidents with a read-only, evidence-first investigation. WHEN: pod crashes or Pending, CrashLoopBackOff, OOMKilled, ImagePullBackOff, node NotReady, DNS or ingress failure, connectivity timeout, network policy, SNAT exhaustion, node-pool scaling blocked by QuotaExceeded or InsufficientVCPUQuota, upgrade stuck, spot or zone disruption, a bare VMExtensionProvisioningError or AllocationFailed wrapper, an uncataloged capacity symptom, or 'investigate my AKS cluster'. DO NOT USE FOR: packet capture (use aks-network-capture); GPU or model-serving issues (use aks-gpu-inference); cluster creation or provisioning (use azure-kubernetes); cost (use cost-analysis from the optional azure-cost plugin); pod rightsizing (use azure-kubernetes); a fully qualified documented AKS signature with every required nested qualifier (use aks-known-issues); standalone failures on non-AKS Azure resources (use azure-diagnostics). Unqualified errors and open-ended incidents stay here only for AKS.
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