Agent Skills

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name: nlp-alignment

description: Best practices for LLM alignment techniques including RLHF, DPO, and instruction tuning. Use when working on alignment or safety.

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name: nlp-pretraining

description: Best practices for language model pretraining and fine-tuning. Use when generating or reviewing NLP training code.

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name: quantum-qiskit

description: Reference qiskit 2.x patterns for variational quantum machine learning. Covers data-encoding feature maps, variational quantum classifier (VQC) training, variational quantum eigensolver (VQE) for chemistry, matrix-product-state circuits, and noise model integration. Use when writing Python code that imports `qiskit`, `qiskit_aer`, `qiskit_algorithms`, `qiskit_machine_learning`, or `qiskit_nature`.

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name: rl-policy-optimization

description: Best practices for reinforcement learning policy optimization. Use when working on RL agents, PPO, SAC, or reward design.

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name: scientific-visualization

description: Publication-ready scientific figure design with matplotlib and seaborn. Use when creating journal submission figures with proper formatting, accessibility, and statistical annotations.

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name: scientific-writing

description: Academic manuscript writing with IMRAD structure, citation formatting, and reporting guidelines. Use when drafting or revising research papers.

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name: statistical-experimental-evaluation

description: Design and run statistical experiments that test the formal problem, proposed methods, theoretical predictions, baselines, and ablations.

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name: statistical-method-design

description: Design statistical methods, baselines, diagnostics, variants, and ablations that directly address a formal problem formulation.

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name: statistical-problem-formulation

description: Formulate statistical research problems with formal notation, target parameters, assumptions, hypotheses, evaluation criteria, and theory targets.

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name: statistical-reporting

description: Statistical test selection, assumption checking, and APA-formatted reporting. Use when analyzing experimental results or writing results sections.

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name: statistical-theory-analysis

description: Analyze theoretical properties of statistical methods under the formal formulation: identifiability, bias, variance, consistency, asymptotics, coverage, error bounds, robustness, and limitations.

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name: stat-research-orchestrator

description: Orchestrate a statistical research pipeline centered on formal problem formulation, method proposal, theoretical analysis, experimental evaluation, comparison, and final result synthesis.

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name: stat-result-validator

description: Validate statistical research outputs for formulation quality, method-to- problem alignment, theory presence, experimental evidence, fair comparison, artifact completeness, and final-claim consistency.

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name: systematic-review

description: Structured methodology for comprehensive literature review following PRISMA guidelines. Use during literature search and screening stages.

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name: add-app-icon

description: Use when adding a bundled app icon to Dozzle for a container image that shows no logo, or when an image resolves to the wrong icon

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name: run-dev-instance

description: Use when Dozzle needs to be running in a browser to test a change by hand (clicking the UI, screenshots, driving it with Chrome or Playwright). Starts an instance on a free port derived from this worktree, so several worktrees can each have one at the same time, and never disturbs an instance someone else started.

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name: update-int-snapshots

description: Use when Playwright visual snapshots in e2e/visual.spec.ts-snapshots/ need to be regenerated after a UI change (logo, theme, layout, anything that affects rendered pixels) and the integration suite needs to be rerun via make int

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name: install-memu

description: Install or uninstall memU for whatever agent you are — identify your host, print its packaged guide, and follow it to wire (or unwire) both seams (record and inject). Use when the user asks to install, set up, integrate, remove, or uninstall memU.

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name: prompt-master

description: Generates optimized prompts for AI tools. Activates only when the user explicitly asks to write, fix, improve, or adapt a prompt for a specific AI tool (LLM, Cursor, Midjourney, image AI, video AI, coding agents, etc.). Does not activate for general conversation, coding tasks, document writing, or other non-prompt-engineering work.

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name: bloc-cubit

description: Use when working with Flutter Bloc/Cubit state management. Covers when to choose Bloc vs Cubit, how to use bloc and flutter_bloc together, lifecycle, testing, and safe defaults.

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name: document-extraction

description: Use this skill for intelligent document processing and content extraction using LandingAI's Agentic Document Extraction (ADE). Trigger when users need to (1) Parse documents (PDFs, images, spreadsheets, presentations) into structured Markdown with layout understanding, (2) Extract specific structured data from documents using schemas (invoice fields, form data, table data, etc.), (3) Classify and separate multi-document batches by type (invoices vs receipts, statements vs forms, etc.), (4) Process large documents asynchronously (up to 1GB/1000 pages), (5) Get visual grounding (bounding boxes, page numbers) for extracted content — use when users mention bounding boxes, word locations, grounding, highlighting extracted content, or showing where data appears in a document. Use this skill when the task involves understanding document content for a set of documents. In particular this skill can help you write code that run on sets of documents. This will increase speed, and reduce the cost of loading the documents on the Agent context window because you can use a single script to extract the information needed.

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name: document-workflows

description: Use this skill for building end-to-end document processing workflows and pipelines using LandingAI ADE. Trigger when users need to: (1) Process batches of documents in parallel or async, (2) Build classify-then-extract pipelines for mixed document types, (3) Prepare parsed documents for RAG systems with chunking and vector DB ingestion, (4) Load extraction results into databases like Snowflake or export to CSV/DataFrames, (5) Visualize extraction results: draw bounding box overlays on pages, crop chunk images, or highlight/annotate specific words or phrases found in documents, (6) Build Streamlit or web UIs for document processing, (7) Find and highlight specific terms within document sections using word-level grounding (e.g. highlight "L2S" in the Introduction, redact PII, annotate extracted values on the original page). This skill complements the document-extraction skill which covers ADE SDK basics. Use document-extraction to write code that executes parse/extract/split operations with more precision and less cost than adding the document image to the prompt and asking the LLM to find the relevant info. Use document-workflows when composing those operations into pipelines, or when you need visualization, annotation, or word-level grounding on parsed documents.

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name: electronics-sourcing

description: Guide for AI agents to source electronic components using parts-mcp — tool sequencing, decision patterns, and multi-step workflows

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name: get-api-docs

description: Use this skill to get documentation for third-party APIs, SDKs or libraries before writing code that uses them to ensure you have the latest, most accurate documentation. This is a better way to find documentation than doing web search. This includes when a user asks for tasks like "use the OpenAI API", "call the Stripe API", "use the Anthropic SDK", "query Pinecone", or any other time the user asks you to write code against an external service and you need current API reference. Fetch the docs with chub before answering, rather than relying on your pre-trained knowledge, which may be outdated because of recent changes to these APIs. Be sure to use this skill when the user asks for the latest docs, latest API behavior, or explicitly mentions chub or Context Hub. Ensure `chub` is available, run `chub --help`, then follow the instructions there.