{"slug":"dotnet-technology-selection","name":"technology-selection","description":"Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).","long_description":"---\nname: technology-selection\ndescription: \"Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).\"\nlicense: MIT\n---\n\n# .NET AI and Machine Learning\n\nPick the right technology first, then deliver **only what the task asks for**. If the task asks for\na plan, comparison, or architecture (or says \"do not write code\"), produce that — do not scaffold,\nbuild, or run code unprompted.\n\n## Step 1: Classify the task (decision tree)\n\nState which branch applies and why, then choose that technology.\n\n| Task type | Technology | Why |\n|-----------|-----------|-----|\n| Structured/tabular: classification, regression, clustering, anomaly detection, recommendation | **ML.NET** (`Microsoft.ML`) | Deterministic (fixed seed), no cloud dependency, purpose-built |\n| NL understanding, generation, summarization, reasoning (single prompt → response, no tools) | **LLM via Microsoft.Extensions.AI** (`IChatClient`) | Language capability, no orchestration needed |\n| Agentic: multi-step tool/function calling, agent loops, multi-agent | **Microsoft Agent Framework** (`Microsoft.Agents.AI`) on **Microsoft.Extensions.AI** | Needs orchestration, tool dispatch, iteration control `IChatClient` lacks |\n| GitHub Copilot extensions / custom dev-workflow agents | **GitHub Copilot SDK** (`GitHub.Copilot.SDK`) | Integrates with the Copilot agent runtime |\n| Run a pre-trained/custom model in production | **ONNX Runtime** (`Microsoft.ML.OnnxRuntime`) | Hardware-accelerated, format-agnostic inference |\n| Local/offline LLM inference | **OllamaSharp** ([Ollama models](https://ollama.com/search)) | Privacy-sensitive, air-gapped, cost-constrained |\n| Semantic search, RAG, embedding storage | **Microsoft.Extensions.VectorData.Abstractions** (MEVD) + a provider (Azure AI Search, Milvus, MongoDB, pgvector, Pinecone, Qdrant, Redis, SQL) | Provider-agnostic vector search |\n| Ingest, chunk, load documents into a vector store | **Microsoft.Extensions.AI.DataIngestion** (preview) + MEVD | Parses, chunks, embeds, upserts |\n| Both structured predictions AND NL reasoning | **Hybrid**: ML.NET scoring + LLM reasoning layer | ML.NET is reproducible; LLM adds explanation |\n\n**Critical rule:** Do NOT use an LLM for tasks ML.NET handles well (tabular classification,\nregression, clustering) — LLMs are slower, costlier, and non-deterministic for these.\n\n## Step 1b: Pick the library layer\n\n| Layer | Library | Use when |\n|-------|---------|----------|\n| **Abstraction** | `Microsoft.Extensions.AI` (MEAI) | Always the foundation. Use `IChatClient` directly for prompt-response and simple, bounded function invocation. |\n| **Provider SDK** | `Azure.AI.OpenAI` / `OpenAI` / `Azure.AI.Inference` / `OllamaSharp` | Concrete provider behind MEAI via `AddChatClient`. |\n| **Orchestration** | `Microsoft.Agents.AI` (prerelease) | Multi-step tool use, durable agent loops, and multi-agent workflows. |\n| **Copilot** | `GitHub.Copilot.SDK` | Building Copilot-platform extensions only. |\n\nRules: start with MEAI; put the provider behind it via `AddChatClient` (don't call the provider in\nbusiness logic); use `Microsoft.Agents.AI` for multi-step or durable agent workflows rather than\nhand-rolling an agent loop; never mix a raw `HttpClient`-to-OpenAI call with MEAI in the same\nworkflow. Do **not** use Accord.NET (archived). For new projects, prefer MEAI and Agent Framework\nunless existing Semantic Kernel features or investments are a requirement. Register AI/ML services\nvia DI; load secrets from user-secrets / env / Key Vault — never hardcode keys.\n\n## Step 2: Cover the branch essentials, then decide depth\n\nEvery answer — plan or implementation — must address the guardrails for the selected branch:\n\n- **ML.NET** — `new MLContext(seed: …)` (reproducible); `TrainTestSplit` + evaluate on the held-out\n  set; report real metrics (MicroAccuracy/MacroAccuracy/LogLoss, AUC/F1, or RMSE/R²); serve with\n  `PredictionEnginePool<TIn,TOut>` (never a singleton `PredictionEngine`).\n- **LLM (MEAI)** — depend on `IChatClient` registered via `AddChatClient` (provider behind it);\n  set `Temperature` and `MaxOutputTokens` in `ChatOptions`; add retry/timeout\n  (`RetryingChatClient`/Polly); pin a dated model; load keys from user-secrets / env / Key Vault —\n  **never hardcode an `sk-…` key**; validate non-deterministic output against a schema with a\n  fallback.\n- **Agentic (Agent Framework)** — orchestrate with `Microsoft.Agents.AI` on `IChatClient` (never a\n  hand-rolled loop); set `MaximumIterations` and a token/cost ceiling; define each tool with a clear\n  schema (`AIFunctionFactory.Create`); log each step (never raw sensitive content).\n- **RAG / embeddings** — semantic **chunking** (not fixed-size); `IEmbeddingGenerator` and **cache\n  the embeddings** (don't re-embed per query); store/query with\n  `Microsoft.Extensions.VectorData.Abstractions` (MEVD) + the provider the user asked for (e.g.\n  pgvector); filter by a **minimum similarity score**; keep **source attribution** for each answer.\n  Honor the UI/storage the user specified; use only real, existing NuGet packages.\n\n**Then choose depth:**\n\n- **Plan / comparison / architecture only** (or \"do not write code\"): answer from this file alone\n  using the essentials above. **Do NOT open a reference** — the branch essentials here are\n  sufficient for a selection or plan. For RAG plans, cover chat, ingestion/chunking, embeddings,\n  vector storage, source attribution, and the requested UI/storage.\n- **Writing implementation code**: read the matching reference(s) for packages and implementation\n  guidance (read only the selected branch; for Hybrid, read both Classic ML.NET and LLM):\n  - Classic ML.NET → [`references/classic-ml.md`](references/classic-ml.md)\n  - LLM integration (MEAI) → [`references/llm.md`](references/llm.md)\n  - Agentic (Agent Framework) → [`references/agentic.md`](references/agentic.md)\n  - RAG / embeddings / ingestion → [`references/rag.md`](references/rag.md)\n  - GitHub Copilot extensions → [`references/copilot.md`](references/copilot.md)\n  - ONNX Runtime inference → [`references/onnx.md`](references/onnx.md)\n  - Local/offline LLM with Ollama → [`references/ollama.md`](references/ollama.md)\n\n## Validation\n\n- [ ] Selection follows the decision tree — no LLM for tasks ML.NET handles\n- [ ] Only what was asked is produced (plan-only requests get a plan, not code)\n- [ ] AI/ML services registered via DI; config via `IOptions<T>`; keys from secure sources\n- [ ] Branch guardrails (Step 2 essentials, plus the reference when implementing) are satisfied\n- [ ] After implementing, build and run existing tests\n\n## Anti-Patterns to Reject\n\n| Anti-pattern | Redirect |\n|-------------|----------|\n| LLM for tabular classification | Use **ML.NET** — faster, cheaper, deterministic |\n| LLM calls without retry/timeout | Add `RetryingChatClient` or Polly retry |\n| API keys in committed `appsettings.json` | user-secrets / env / Key Vault |\n| Accord.NET, or defaulting to Semantic Kernel without a requirement | ML.NET; prefer MEAI + `Microsoft.Agents.AI` for new work |\n| Hand-rolled multi-step tool loops with `IChatClient` | `Microsoft.Agents.AI` (`MaximumIterations`, tool dispatch) |\n| Agent Framework for a single prompt→response | `IChatClient` directly |\n| Raw `HttpClient`/OpenAI SDK in business logic alongside MEAI | one abstraction layer; depend on `IChatClient` |\n| `PredictionEngine` singleton in ASP.NET Core | `PredictionEnginePool<TIn,TOut>` (not thread-safe) |\n| RAG without chunking or relevance filtering | semantic chunking + minimum similarity score |\n| Building custom neural nets in .NET from scratch | pre-trained via ONNX Runtime or an LLM API |\n","tagline":"Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. 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No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"],"backward_compatible":{"trust_score_v4":{"version":"trust-score-v4","score":75,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout."}}},"trust_score_v4":{"version":"trust-score-v4","score":75,"tier":"strong","label":"Strong shortlist","summary":"Good trust signals with a few areas worth checking before rollout.","recommendedAction":"Test in a sandbox workflow and compare its install path with close alternatives.","dimensions":[{"id":"github_adoption","label":"GitHub adoption","score":94,"weight":0.13,"status":"pass","detail":"5.3K GitHub stars"},{"id":"repo_activity","label":"Stars/forks activity","score":88,"weight":0.08,"status":"pass","detail":"5.3K stars, 403 forks; issue activity unavailable in current metadata"},{"id":"maintenance","label":"Recent maintenance","score":100,"weight":0.14,"status":"pass","detail":"3d since push"},{"id":"license","label":"License clarity","score":86,"weight":0.09,"status":"pass","detail":"MIT"},{"id":"documentation","label":"README/SKILL.md completeness","score":76,"weight":0.14,"status":"info","detail":"Public metadata needs stronger README/SKILL.md context"},{"id":"dependency_risk","label":"Dependency/runtime risk","score":56,"weight":0.12,"status":"warn","detail":"credential or environment access, network or browser surface"},{"id":"installability","label":"Install availability","score":92,"weight":0.1,"status":"pass","detail":"npx skills add dotnet/skills --skill technology-selection"},{"id":"install_safety","label":"Install command safety","score":92,"weight":0.1,"status":"pass","detail":"standard package or runtime install path"},{"id":"permission_surface","label":"Permission surface","score":34,"weight":0.07,"status":"fail","detail":"secrets or environment access, filesystem or document access"},{"id":"repository","label":"Repository evidence","score":86,"weight":0.04,"status":"pass","detail":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection"},{"id":"review_status","label":"Review status","score":66,"weight":0.05,"status":"info","detail":"AI review data available"},{"id":"agent_outcomes","label":"Agent Proven outcomes","score":54,"weight":0.13,"status":"info","detail":"No agent outcome data yet"}],"checks":[{"status":"pass","label":"GitHub adoption","detail":"5.3K GitHub stars"},{"status":"pass","label":"Stars/forks activity","detail":"5.3K stars, 403 forks; issue activity unavailable in current metadata"},{"status":"pass","label":"Recent maintenance","detail":"3d since push"},{"status":"pass","label":"License clarity","detail":"MIT"},{"status":"info","label":"README/SKILL.md completeness","detail":"Public metadata needs stronger README/SKILL.md context"},{"status":"warn","label":"Dependency/runtime risk","detail":"credential or environment access, network or browser surface"},{"status":"pass","label":"Install availability","detail":"npx skills add dotnet/skills --skill technology-selection"},{"status":"pass","label":"Install command safety","detail":"standard package or runtime install path"},{"status":"fail","label":"Permission surface","detail":"secrets or environment access, filesystem or document access"},{"status":"pass","label":"Repository evidence","detail":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection"},{"status":"info","label":"Review status","detail":"AI review data available"},{"status":"info","label":"Agent Proven outcomes","detail":"No agent outcome data yet"},{"status":"warn","label":"Ownership","detail":"No approved owner claim yet"},{"status":"info","label":"OpenAgentSkill usage","detail":"No local usage activity yet"},{"status":"info","label":"Agent outcomes","detail":"No agent outcome data yet"}],"strengths":["AI review approved","Install path is available","Repository evidence is available","Recently maintained repository","Large GitHub adoption signal","Install command has no obvious high-risk pattern"],"warnings":["The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"],"evidence":{"stars":"5.3K GitHub stars","repoActivity":"5.3K stars, 403 forks","lastPushed":"3d since push","license":"MIT","repository":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection","install":"npx skills add dotnet/skills --skill technology-selection","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"installReadiness":{"ready":true,"command":"npx skills add dotnet/skills --skill technology-selection","policy":"human_review_before_install","label":"Human review before install","notes":["Install path is available","Repository evidence is available","License is declared","No Agent Proven outcome evidence yet","3d since push"]},"agentCompatibility":["Codex","Claude Code","Cursor","OpenAgentSkill CLI"],"riskSummary":{"level":"medium","label":"Review before production","notes":["The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. 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No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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Useful for discovery, but not for autonomous installation.","README/SKILL.md completeness: Public metadata needs stronger README/SKILL.md context","High-risk permission hints: Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"],"validation_plan":["Inspect repository, README/SKILL.md, license, and recent commits before production use.","Install in an isolated workspace or sandbox with no production secrets available.","Run the smallest representative task and record files touched, commands run, network access, and outputs.","Compare the selected skill against at least one alternative when the eval status is review or failed.","Promote only after the agent reports a successful verification result and unresolved warnings are accepted."],"checks":[{"id":"task_fit","label":"Task fit","status":"pass","score":94,"required_for_auto_install":true,"detail":"Task wording matches this skill metadata.","evidence":["Evaluate technology-selection before installing it in an agent workflow","research","RAG and knowledge workflows; Claude Code teams; teams that value GitHub adoption signals"]},{"id":"install_path","label":"Install path","status":"pass","score":92,"required_for_auto_install":true,"detail":"Install handoff is available.","evidence":["npx skills add dotnet/skills --skill technology-selection"]},{"id":"install_safety","label":"Install command safety","status":"pass","score":92,"required_for_auto_install":true,"detail":"standard package or runtime install path","evidence":["npx skills add dotnet/skills --skill technology-selection"]},{"id":"trust_score","label":"Trust score","status":"warn","score":75,"required_for_auto_install":true,"detail":"Good trust signals with a few areas worth checking before rollout.","evidence":["Strong shortlist","5.3K GitHub stars","MIT"]},{"id":"audit_score","label":"Audit score","status":"warn","score":83,"required_for_auto_install":true,"detail":"Needs review","evidence":["Dependency or permission surface needs review"]},{"id":"agent_safety_gate","label":"Agent safety gate","status":"warn","score":51,"required_for_auto_install":true,"detail":"Sparse or mixed signals. 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DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).","category":"research","url":"https://www.openagentskill.com/skills/dotnet-technology-selection","repository":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection","github_repo":"dotnet/skills"},"suited_tasks":["RAG and knowledge workflows","Claude Code teams","teams that value GitHub adoption signals","Chunk documents","Create embeddings","Retrieve and cite relevant passages","Inspect repository metadata","Compare code changes"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","OpenAI Agents","CLI"],"install":{"command":"npx skills add dotnet/skills --skill technology-selection","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add dotnet-technology-selection"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"technology-selection\" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"technology-selection\" as a Claude Code skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"technology-selection\" from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. 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No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. 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No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":85,"label":"Excellent"},"supply":{"track":"Research and knowledge work","scenario":"RAG and knowledge","maintenance":"3d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","No OpenAgentSkill engagement data yet","High-risk permission hints: Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Quality score needs review"],"agent_contract":{"task_input":"Use technology-selection in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 75/100 Strong shortlist","Audit: 83/100 Needs review","Safety: 51/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"dotnet-technology-selection (technology-selection)","install_command":"npx skills add dotnet/skills --skill technology-selection","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"dotnet-technology-selection","task":"Use technology-selection in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/dotnet-technology-selection","api":"https://www.openagentskill.com/api/agent/skills/dotnet-technology-selection","audit":"https://www.openagentskill.com/skills/dotnet-technology-selection/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=dotnet-technology-selection&task=Use%20technology-selection%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20technology-selection%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20technology-selection%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/dotnet-technology-selection/install","manifest":"https://www.openagentskill.com/api/registry/manifest/dotnet-technology-selection"}},"machine_metadata":{"version":"openagentskill-agent-metadata-v2","skill":{"slug":"dotnet-technology-selection","name":"technology-selection","description":"Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference).","category":"research","url":"https://www.openagentskill.com/skills/dotnet-technology-selection","repository":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection","github_repo":"dotnet/skills"},"suited_tasks":["RAG and knowledge workflows","Claude Code teams","teams that value GitHub adoption signals","Chunk documents","Create embeddings","Retrieve and cite relevant passages","Inspect repository metadata","Compare code changes"],"suited_agents":["Codex","Claude Code","Cursor","OpenAgentSkill CLI","OpenAI Agents","CLI"],"install":{"command":"npx skills add dotnet/skills --skill technology-selection","ready":true,"targets":[{"id":"openagentskill-cli","label":"CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add dotnet-technology-selection"},{"id":"codex","label":"Codex","kind":"agent-prompt","value":"Install the \"technology-selection\" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"claude-code","label":"Claude Code","kind":"agent-prompt","value":"Add \"technology-selection\" as a Claude Code skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."},{"id":"cursor","label":"Cursor","kind":"agent-prompt","value":"Turn \"technology-selection\" from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes."}],"handoff_url":"https://www.openagentskill.com/api/skills/dotnet-technology-selection/install","manifest_url":"https://www.openagentskill.com/api/registry/manifest/dotnet-technology-selection"},"trust":{"score":75,"label":"Strong shortlist","version":"trust-score-v4","install_policy":"human_review_before_install","evidence":{"stars":"5.3K GitHub stars","repoActivity":"5.3K stars, 403 forks","lastPushed":"3d since push","license":"MIT","repository":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection","install":"npx skills add dotnet/skills --skill technology-selection","installSafety":"standard package or runtime install path","permissionSurface":"secrets or environment access, filesystem or document access","documentation":"Usable metadata, review docs","agentOutcomes":"No agent outcome data yet"},"outcome_evidence":{"total":0,"successes":0,"failures":0,"not_relevant":0,"success_rate":null,"recent_success_rate":null,"recent_failure_rate":null,"install_attempts":0,"install_success_rate":null,"risk_blocked":0,"setup_required":0,"avg_output_quality":null,"production_outcomes":0,"last_outcome_at":null,"label":"No agent outcome data yet"},"auto_install":{"allowed":false,"sandbox_required":true,"reason":"Human review or sandbox validation is required before automatic installation."},"best_for":["research","agent-skill"],"known_risks":["The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"agent_proven":{"version":"agent-proven-v1","score":0,"tier":"unproven","label":"Needs first agent run","summary":"No agent outcome reports yet. Use Resolve, run one narrow sandbox task, then report the result.","metrics":{"totalOutcomes":0,"successfulOutcomes":0,"failedOutcomes":0,"installAttempts":0,"installSuccessRate":null,"successRate":null,"recentSuccessRate":null,"recentFailureRate":null,"riskBlocked":0,"setupRequired":0,"notRelevant":0,"avgOutputQuality":null,"avgTimeToUsefulMs":null,"productionOutcomes":0,"humanReviewRequired":0,"uniqueAgents":0,"lastOutcomeAt":null},"signals":[],"penalties":["No real agent outcome evidence yet"]},"audit":{"score":83,"risk_level":"needs_review","risk_label":"Needs review","warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"safety_gate":{"tier":"experimental","label":"Experimental","auto_install_policy":"review","auto_install_allowed":false,"human_review_required":true,"blocked":false,"recommended_action":"Test manually in an isolated workspace and compare against safer alternatives."},"quality":{"score":85,"label":"Excellent"},"supply":{"track":"Research and knowledge work","scenario":"RAG and knowledge","maintenance":"3d since push","risk":"Needs review"},"alternative_skills":[],"do_not_use_when":["teams that need a vendor-supported SLA","production agents without a repository review","The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","No OpenAgentSkill engagement data yet","High-risk permission hints: Secrets or environment access","Dependency or permission surface needs review","Permission surface may require sandboxing","Quality score needs review"],"agent_contract":{"task_input":"Use technology-selection in an agent workflow","recommended_action":"Test manually in an isolated workspace and compare against safer alternatives.","install_policy":"review","minimum_review_before_use":["Trust: 75/100 Strong shortlist","Audit: 83/100 Needs review","Safety: 51/100 Avoid automatic install","Review repository, license, install command, and permission surface before production use."],"expected_agent_output":{"selected_skill":"dotnet-technology-selection (technology-selection)","install_command":"npx skills add dotnet/skills --skill technology-selection","risk_summary":"Needs review; Experimental; Review before production","verification_result":"Report the smallest successful task, files touched, warnings, and any missing setup."}},"outcome_feedback":{"endpoint":"https://www.openagentskill.com/api/agent/outcome","method":"POST","requires_resolve_event_id":true,"event_id_source":"Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.","expected_outcomes":["success","failed","not_relevant","blocked_by_risk","setup_required"],"payload_template":{"event_id":"<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>","skill_slug":"dotnet-technology-selection","task":"Use technology-selection in an agent workflow","agent":"codex","outcome":"success","install_used":true,"risk_blocked":false,"setup_required":false,"task_success":true,"output_quality":4,"error_type":null,"human_review_required":false,"workspace":"sandbox","time_to_useful_ms":120000,"notes":"Report the smallest successful task, setup friction, files touched, and risk notes."}},"endpoints":{"web":"https://www.openagentskill.com/skills/dotnet-technology-selection","api":"https://www.openagentskill.com/api/agent/skills/dotnet-technology-selection","audit":"https://www.openagentskill.com/skills/dotnet-technology-selection/audit","eval":"https://www.openagentskill.com/api/agent/evals?slug=dotnet-technology-selection&task=Use%20technology-selection%20in%20an%20agent%20workflow&max_risk=medium","resolve":"https://www.openagentskill.com/api/agent/resolve?task=Use%20technology-selection%20in%20an%20agent%20workflow&agent=codex&max_risk=medium","receipt":"https://www.openagentskill.com/api/agent/receipt?task=Use%20technology-selection%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text","install":"https://www.openagentskill.com/api/skills/dotnet-technology-selection/install","manifest":"https://www.openagentskill.com/api/registry/manifest/dotnet-technology-selection"}},"supply_profile":{"track":{"slug":"research","label":"Research and knowledge work","shortLabel":"Research","description":"Deep research, source comparison, literature review, RAG, knowledge search, and reports."},"scenario":{"label":"RAG and knowledge","description":"I need my agent to build a RAG workflow over documents and retrieve reliable context.","useCases":[{"slug":"rag-knowledge","title":"RAG and knowledge"},{"slug":"github-automation","title":"GitHub automation"},{"slug":"research-agents","title":"Research agents"}]},"applicableAgents":["Claude Code","OpenAI Agents","CLI","Codex","Cursor"],"install":{"ready":true,"command":"npx skills add dotnet/skills --skill technology-selection","primaryTarget":"CLI","targetCount":4},"githubQuality":{"stars":5320,"starsLabel":"5.3K","forks":403,"license":"MIT","qualityScore":85,"trustScore":75,"auditScore":83},"maintenance":{"status":"fresh","label":"3d since push","daysSincePush":3,"lastPushedAt":"2026-09-02T12:13:15+00:00"},"risk":{"level":"needs_review","label":"Needs review","requiresReview":true,"notes":["Dependency or permission surface needs review","Permission surface may require sandboxing","The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access"]},"coverageTags":["Research","RAG and knowledge","agent-skill"]},"audit":{"audit_score":83,"risk_level":"needs_review","risk_label":"Needs review","quality_score":85,"trust_score":75,"maintenance_score":100,"security_score":74,"install_score":92,"warnings":["Dependency or permission surface needs review","Permission surface may require sandboxing","The SKILL.md excerpt is truncated in the provided documentation, but the full repository likely contains complete content. No critical issues found.","Quality score needs review","Permission surface needs review: secrets or environment access, filesystem or document access","Dependency/runtime risk: credential or environment access, network or browser surface","Permission surface: secrets or environment access, filesystem or document access"]},"quality_signals":{"model":"v2","star_score":26.08,"usage_score":0,"review_score":5.7,"metadata_score":3,"freshness_score":15},"platforms":["Claude Code","OpenAI Agents"],"use_cases":[{"slug":"rag-knowledge","title":"RAG and knowledge","url":"https://www.openagentskill.com/use-cases/rag-knowledge"},{"slug":"github-automation","title":"GitHub automation","url":"https://www.openagentskill.com/use-cases/github-automation"},{"slug":"research-agents","title":"Research agents","url":"https://www.openagentskill.com/use-cases/research-agents"},{"slug":"workflow-automation","title":"Workflow automation","url":"https://www.openagentskill.com/use-cases/workflow-automation"}],"stacks":[{"slug":"rag-knowledge-base","title":"RAG knowledge base","url":"https://www.openagentskill.com/collections/rag-knowledge-base"},{"slug":"research-report-agent","title":"Research report agent","url":"https://www.openagentskill.com/collections/research-report-agent"},{"slug":"content-growth-agent","title":"Content growth agent","url":"https://www.openagentskill.com/collections/content-growth-agent"}],"install":"npx skills add dotnet/skills --skill technology-selection","install_targets":[{"id":"openagentskill-cli","label":"CLI","title":"OpenAgentSkill CLI","kind":"command","value":"npx --yes https://github.com/Leon-Drq/openagentskill/releases/download/cli-v0.3.0/openagentskill-0.3.0.tgz add dotnet-technology-selection","description":"Resolve policy, run the source installer safely, and report a verified install receipt.","copyLabel":"Copy command"},{"id":"codex","label":"Codex","title":"Codex install prompt","kind":"agent-prompt","value":"Install the \"technology-selection\" agent skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection. Read its SKILL.md or equivalent instructions first, install only the files needed for this workspace, and summarize any required setup before using it. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"codex\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Give Codex a repo-aware install prompt when the skill is not available through a local CLI.","copyLabel":"Copy prompt"},{"id":"claude-code","label":"Claude Code","title":"Claude Code skill prompt","kind":"agent-prompt","value":"Add \"technology-selection\" as a Claude Code skill from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection. Inspect the skill instructions, place the reusable skill files in the appropriate local skills location for this project, and report the activation steps. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"claude-code\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this prompt to ask Claude Code to add the skill and explain the local activation steps.","copyLabel":"Copy prompt"},{"id":"cursor","label":"Cursor","title":"Cursor rule prompt","kind":"agent-prompt","value":"Turn \"technology-selection\" from https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection into a reusable Cursor project rule or agent instruction. Preserve the core workflow, adapt paths to this repo, and keep the rule scoped to tasks where it is relevant. Skill purpose: Guides technology selection and implementation of AI and ML features in .NET 8+ applications using ML.NET, Microsoft.Extensions.AI (MEAI), Microsoft Agent Framework (MAF), GitHub Copilot SDK, ONNX Runtime, and OllamaSharp. Covers the full spectrum from classic ML through modern LLM orchestration to local inference. Use when adding classification, regression, clustering, anomaly detection, recommendation, LLM integration (text generation, summarization, reasoning), RAG pipelines with vector search, agentic workflows with tool calling, Copilot extensions, or custom model inference via ONNX Runtime to a .NET project. DO NOT USE FOR projects targeting .NET Framework (requires .NET 8+), the task is pure data engineering or ETL with no ML/AI component, or the project needs a custom deep learning training loop (use Python with PyTorch/TensorFlow, then export to ONNX for .NET inference). After the install attempt, report the result to https://www.openagentskill.com/api/agent/outcome with POST JSON {\"event_id\":\"install_<unique-id>\",\"skill_slug\":\"dotnet-technology-selection\",\"task\":\"Install technology-selection\",\"agent\":\"cursor\",\"outcome\":\"success\",\"install_used\":true}. Replace event_id with a unique value and outcome with success or failed. Report success only after the skill is installed and a minimal verification passes.","description":"Use this when installing as Cursor project rules or reusable agent instructions.","copyLabel":"Copy prompt"}],"repository":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection","github_repo":"dotnet/skills","version":"1.0.0","license":"MIT","urls":{"web":"https://www.openagentskill.com/skills/dotnet-technology-selection","repository":"https://github.com/dotnet/skills/tree/main/plugins/dotnet-ai/skills/technology-selection","api":"/api/agent/skills/dotnet-technology-selection","install_api":"/api/skills/dotnet-technology-selection/install"},"meta":{"created_at":"2026-09-02T12:26:03.421703+00:00","updated_at":"2026-09-02T12:26:03.568408+00:00","agent_friendly":true}}