Generates llms.txt and llms-full.txt files for LLM-friendly project documentation following the llms.txt specification. Use when the user wants to create LLM-readable summaries, llms.txt files, or make their wiki accessible to language models.
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설치 방법 보기설치하기 전에 에이전트에게 전달되는 지시 내용을 확인할 수 있습니다.
Generate llms.txt and llms-full.txt files that provide LLM-friendly access to wiki documentation, following the llms.txt specification.
llms.txt or mentions the llms.txt standardllms-full.txt or context-expanded documentationBefore generating, resolve the source repository context:
git remote get-url originREPO_URLgit rev-parse --abbrev-ref HEADThe file follows the llms.txt specification:
# {Project Name}
> {Dense one-paragraph summary — what it does, who it's for, key technologies}
{Important context paragraphs — constraints, architectural philosophy, non-obvious things}
## {Section Name}
- [{Page Title}]({relative-path-to-md}): {One-sentence description of what the reader will learn}
## Optional
- [{Page Title}]({relative-path-to-md}): {Description — these can be skipped for shorter context}
[Title](url): Description entries| ❌ Bad | ✅ Good |
|---|---|
| "Architecture overview" | "System architecture showing how Orleans grains communicate via message passing with at-least-once delivery" |
| "Getting started guide" | "Prerequisites, local dev setup with Docker Compose, and first API call walkthrough" |
| "The API reference" | "REST endpoints with auth requirements, rate limits, and request/response schemas" |
Same structure as llms.txt but with full content inlined:
# {Project Name}
> {Same summary}
{Same context}
## {Section Name}
<doc title="{Page Title}" path="{relative-path}">
{Full markdown content — frontmatter stripped, citations and diagrams preserved}
</doc>
--- blocks) from each page```mermaid fences intact[file:line](URL) links stay as-is<!-- Sources: --> comments — these provide diagram provenanceThis skill works best when wiki pages already exist (via /deep-wiki:generate or /deep-wiki:page). If no wiki exists yet:
/deep-wiki:generate firstllms.txt from README + source code scan (without wiki page links)Generate three files:
| File | Purpose | Discoverability |
|---|---|---|
./llms.txt | Root discovery file | Standard path per llms.txt spec. GitHub MCP get_file_contents and search_code find this first. |
wiki/llms.txt | Wiki-relative links | For VitePress deployment and wiki-internal navigation. |
wiki/llms-full.txt | Full inlined content | Comprehensive reference for agents needing all docs in one file. |
The root ./llms.txt links into wiki/ (e.g., [Guide](./wiki/onboarding/contributor-guide.md)). The wiki/llms.txt uses wiki-relative paths (e.g., [Guide](./onboarding/contributor-guide.md)).
If a root llms.txt already exists and was NOT generated by deep-wiki, do NOT overwrite it.
Before finalizing:
llms.txt actually exist<doc> blocks in llms-full.txt have real content (not empty)llms.txt is concise (1-5 KB)llms-full.txt contains all wiki pages아직 리뷰가 없습니다. 사용해 본 소감을 남겨 주세요.
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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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
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.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.