kimtth

Use when: review an academic paper with a structured review form and multiple reviewer personas.

Use when: design a clear, consistent, and evolvable API contract before implementing it.

Use when: record a significant technical decision, its context, and consequences as an ADR.

Use when: implement or review authentication flows, sessions, tokens, and password handling securely.

Use when: design or review access control so users can only do what they are permitted to do.

Use when: write or review robust shell scripts that fail fast and handle paths and input safely.

Use when: explore options or decompose an ambiguous problem before committing to one approach.

Use when: add caching with the right layer, key design, invalidation, and consistency trade-offs.

Plan, implement, review, and troubleshoot Microsoft Fabric ALM and CI/CD workflows using Git integration, deployment pipelines, Variable Libraries, Fabric REST APIs, fabric-cicd, GitHub Actions, or Azure DevOps. Use when the user asks about source control,…

Design, configure, evaluate, and govern Microsoft Fabric Data Agents for natural-language Q&A over Lakehouse, Warehouse, Power BI semantic model, KQL database, mirrored database, ontology, or Microsoft Graph data. Use when the user asks for Fabric data agent,…

Create alerts, notifications, and automated actions on Fabric data and events via Fabric REST API and `az rest` CLI. Use when the user wants to: (1) create, update, or delete an alert or notification flow, (2) send a Teams message, send an email, or run a…

Inspect existing alerts, notifications, and automated actions in Fabric via read-only REST API calls using `az rest` CLI. Use when the user wants to: (1) list existing alerts in a workspace, (2) inspect how an alert or notification is configured, (3) read and…

Port Databricks notebooks and jobs to Microsoft Fabric. Provides an exhaustive dbutils to notebookutils substitution table: fs operations (mount removal via OneLake Shortcuts), secret scope to Key Vault URL conversion, notebook run and exit, widget…

Create, update, delete, and manage Fabric Dataflows Gen2 artifacts with Power Query M mashup definitions via CLI (az rest / curl). Uses az rest and curl against the Fabric REST API to author definitions containing base64-encoded mashup.pq, queryMetadata.json,…

Monitor, inspect, and discover Fabric Dataflows Gen2 via read-only CLI operations (az rest / curl). List dataflows across workspaces, decode base64 definitions to inspect Power Query M queries and queryMetadata.json, discover typed parameters with defaults,…

Assess, plan, and execute dataflow Gen1 → Gen2.1 CI/CD save-as operations via CLI (az rest / curl) against both Power BI REST and Fabric REST APIs. Scan workspaces or entire tenants for Gen1 dataflows, evaluate save-as readiness with seven risk signals…

Create editable, production-ready PPTX decks with narrative planning, explicit layout specs, asset guidance, and quality checks.

Prepare the narrative, sources, and design context for an editable PPTX deck.

Validate and repair editable PPTX artifacts, including layout, package, accessibility, and visual checks.

Reference PPTX analysis: extraction, style analysis, deck diagnostics, and safe read-only OOXML package inspection.

Author or repair coordinate-explicit JSON specifications for editable PPTX decks.

Plan and place supporting visual assets in editable PPTX decks without flattening slide content.

Workflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR: Adding new resources to the knowledge base. DO NOT USE FOR: Editing existing entries or…

Classification guidelines for entries in temp_entries.md. Each entry has a title containing the markdown file name and section name. USE FOR: Classifying new entries in temp_entries.md into *.md section files. DO NOT USE FOR: Adding entries to temp_entries.md…

Workflow for updating the popular LLM applications pool (section/x_llm_apps.md) using fetch_llm_apps.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked from…

Workflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding…

Guidelines for updating citation counts for papers in the section files using the `update_citation_counts.py` tool. USE FOR: Updating citation counts for papers listed in the section files to keep information current. DO NOT USE FOR: 1) Adding new papers to…

Workflow and tools for adding new entries from temp.md to the section files. Includes legend format, section reference, code tools, and common pitfalls. USE FOR: Adding new resources to the knowledge base. DO NOT USE FOR: Editing existing entries or…

Workflow for updating the LLM landscape paper pool (section/x_llm_papers.md) using fetch_llm_papers.py. Covers full re-fetch, resume from checkpoint, and adding new topics. USE FOR: Refreshing citation counts, expanding topic coverage. DO NOT USE FOR: Adding…

Workflow for updating the popular LLM applications pool (section/x_llm_apps.md) using get_app_list_by_github_star.py. Covers full refresh, alternate exports, topic tuning, and common pitfalls. USE FOR: Refreshing the ranked GitHub applications list linked…

Classification guidelines for entries in temp_entries.md. Each entry have its own title with the markdown file name and section name in temp_entries.md. USE FOR: Classifying new entries in temp_entries.md into *.md in the section files. DO NOT USE FOR: 1)…

Guidelines for updating citation counts for papers in the section files using the `update_citation_counts.py` tool. USE FOR: Updating citation counts for papers listed in the section files to keep information current. DO NOT USE FOR: 1) Adding new papers to…

Generate synthetic and simulated datasets for evaluation and fine-tuning using Azure AI Foundry simulators. Create non-adversarial task data, adversarial safety data, and conversation datasets without manual data collection.

Train or fine-tune LLMs on Azure ML managed compute with TRL trainers. Uses direct trainer loops (SFT, DPO, RL) without relying on serverless APIs or Hugging Face infrastructure.

Evaluate generative AI applications and models locally or in the cloud using Azure AI Evaluation SDK. Measure quality, safety, and performance with built-in and custom evaluators.