Claude Code Skills · page 40

Use when a PinMe Cloudflare Worker needs R2 object storage, including secure file or image upload, streaming download, metadata lookup, deletion, listing, Range requests, or R2+D1 coordination. Guides AI to use PinMe's automatically injected env.R2 binding without R2 credentials or manual Wrangler configuration.

Use this skill when the user wants to share, publish, or upload a static result through PinMe, especially by generating a static HTML share page for a PinMe project link, deployed full-stack app, Codex conversation summary, report, file, demo, or any 分享/发布/上传分享页 request that should end with `pinme upload`.

Use when generating, modifying, or reviewing PinMe Worker (Cloudflare Worker TypeScript) code that accepts payments through UniwebPay — payment links, products/prices, checkout sessions, payment status reads, refunds, subscriptions, or handling UniwebPay webhooks with @uniwebpay/sdk in a PinMe project.

倪海厦（1954-2012）台湾中医师，经方派代表人物，汉唐中医创始人。核心心智模型：六经辨证、阳气论、经典至上、经方为主。决策启发式：先辨六经再选方、阳气不足先扶阳、经典原方最可靠。触发词：倪海厦、海厦视角、中医倪海厦、经方思维、倪海厦会怎么看、倪师。知识库覆盖：伤寒论条文补齐（太阳下篇+阳明篇，modules/13）+伤寒论129条全（modules/01）+金匮23篇+黄帝内经72篇+针灸教程+神农本草经374种+天纪+1257例全量结构化医案+243例分类叙事医案+梁冬对话+口述表达DNA+六经辨证诊断公式（8个公式+快速诊断流程图+脉舌速查+合病并病速查+真寒假热鉴别+七步走思维模式）。基于一手素材：梁冬对话录音稿、人纪班闭门课记录、医案集、神农本草经视频讲义。

Add or extend coding-agent support in cc-sdd by executing the SOP in docs/cc-sdd/sop-new-agent.md end-to-end. Use when introducing a new agent, adding a subagent-capable variant, or evaluating migration of an existing supported agent to skills-based templates.

Investigate implementation failures using root-cause-first debugging. Use when an implementer is blocked, verification fails, or repeated remediation does not converge.

Entry point for new work. Determines the best action path or work decomposition (update existing spec, create new spec, mixed decomposition, or no spec needed) and refines ideas through structured dialogue.

Implement approved tasks using TDD with subagent dispatch. Runs all pending tasks autonomously or selected tasks manually.

Review a task implementation against approved specs, task boundaries, and verification evidence. Use after an implementer finishes a task, after remediation, or before accepting a task as complete.

Create complete specs (requirements, design, tasks) for all features in roadmap.md using parallel sub-agent dispatch by dependency wave.

Create comprehensive technical design for a specification

Initialize a new specification with detailed project description

Quick spec generation with interactive or automatic mode

Generate comprehensive requirements for a specification

Show specification status and progress

Generate implementation tasks for a specification

Manage {{KIRO_DIR}}/steering/ as persistent project knowledge

Create custom steering documents for specialized project contexts

Interactive technical design quality review and validation

Analyze implementation gap between requirements and existing codebase

Validate feature-level integration after all tasks are implemented. Checks cross-task consistency, full test suite, and overall spec coverage.

Verify completion and success claims with fresh evidence. Use before claiming a task is complete, a fix works, tests pass, or a feature is ready for GO.

Design principles and creative direction for building HTML artifacts — pages, reports, plans, landing pages, demos, decks, and small tools. Use when creating or restyling any visual HTML deliverable and deciding its palette, type pairing, layout, theming, or overall register, or when the output must not look generically AI-generated.

Create or redesign self-contained single-file HTML artifacts with a visual direction shaped by the user's brief, project, and subject. Use when HTML is the deliverable for a report, explainer, landing page, presentation, tool, mixed artifact, or broad request. This is the collection's only implicit router. Route clear wireframe, prototype, mockup, plan, or diagram requests to the matching direct-invocation specialist when available. Do not use for ordinary application implementation when a standalone HTML file is not the deliverable.

Direct-invocation specialist for self-contained HTML diagrams whose layout, notation, and interaction clarify relationships, sequence, topology, state, hierarchy, or quantitative structure. Use when the user explicitly invokes html-diagram or the broad html skill routes a diagram request here. Do not activate independently from a general request.

Direct-invocation specialist for clear, self-contained HTML plans that preserve source material while improving hierarchy, sequence, ownership, dependencies, and reviewability. Use when the user explicitly invokes html-plan or the broad html skill routes a plan request here. Do not activate independently from a general request.

Direct-invocation specialist for polished, responsive, self-contained HTML mockups and interactive prototypes grounded in the user's conversation, product context, and design language. Use when the user explicitly invokes html-prototype or the broad html skill routes a mockup or prototype request here. Do not activate independently from a general request. Treat a mockup as a noninteractive fidelity mode within this skill, not as a separate skill.

Direct-invocation specialist for low-fidelity, self-contained HTML wireframes that test information hierarchy, content, navigation, task flow, and responsive structure before visual design. Use when the user explicitly invokes html-wireframe or the broad html skill routes a wireframe request here. Do not activate independently from a general request. Do not use for polished mockups or production-like interaction; use html-prototype for those.

Work with files and directories mounted in a Mirage virtual filesystem. Use when a task mentions Mirage, mounted cloud or database data, Mirage virtual paths, or asks to inspect, search, create, or edit data exposed through the Mirage tools.

Official NVIDIA-authored guidance for NVIDIA cuDF GPU DataFrames, pandas acceleration, dask-cuDF, ETL, joins, groupby, CSV/Parquet I/O, nullable semantics, and multi-GPU DataFrame workloads.

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Calibrate a new dataset from live RTSP camera streams via the AutoMagicCalib REST API. Use when the user provides RTSP URLs or asks to calibrate live cameras; VIOS records clips, AMC ingests them, then runs calibration.

Run end-to-end calibration on the shipped sample dataset (sdg_08_2_sample_data_010926.zip) against a running AMC microservice. Use when user says 'test sample dataset', 'run sample calibration', 'verify AMC install', or 'launch and test'.

Calibrate a new dataset from pre-recorded video files via the AutoMagicCalib REST API. Use when user has local MP4s and says 'calibrate my videos', 'run AMC on these videos', or similar. For RTSP/live streams, use amc-run-rtsp-calibration instead.

Launch AutoMagicCalib microservice and web UI from NGC release images via Docker Compose. Use when user says 'deploy auto calibration', 'launch auto calibration', 'launch AMC', 'start MS+UI', or 'set up auto-magic-calib'. Requires NGC API key.

CUDA-Q onboarding guide for installation, test programs, GPU simulation, QPU hardware, and quantum applications.

Modify, build, test, debug, and contribute to NVIDIA cuOpt (C++/CUDA, Python, server, CI). Use for solver internals, PRs, DCO, and code conventions.

Install cuOpt for Python, C, or server via pip, conda, or Docker; verify the install. For building cuOpt from source, see cuopt-developer.

Trace, complete, and interpret the Pareto frontier across competing objectives using repeated single-objective cuOpt solves (weighted-sum and ε-constraint).

LP, MILP, and QP (beta) with cuOpt — Python, C, and CLI. Use when the user is solving LP, MILP, or QP with any cuOpt interface.

LP, MILP, QP — concepts, problem-text parsing, and formulation patterns (parameters, constraints, decisions, objective). Concepts only; no API.

Vehicle routing (VRP, TSP, PDP) with cuOpt — Python API only. Use when the user is building or solving routing in Python.

cuOpt REST server — start server, endpoints, Python/curl client examples. Use when the user is deploying or calling the REST API.

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Install and verify cuPyNumeric for Python — requirements, commands, verification. Source builds are out of scope.

Pre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.

Load a sharded, on-disk dataset (sharded .npy, Parquet/Arrow, raw binary, sharded HDF5, custom layouts) into a distributed cuPyNumeric ndarray via a manual partition + leaf @task launch with CPU/OMP/GPU variants. Use when no single-call loader fits, including when per-shard row counts differ across files. Prefer cupynumeric.load or legate.io.hdf5.from_file when they apply.

DALI imperative dynamic mode (`nvidia.dali.experimental.dynamic`, ndd): use when working on ndd code or migrating pipelines; skip pipeline-only tasks.

Use when the user wants to create a dataset, generate synthetic data, or build a data generation pipeline.

NVIDIA DeepStream SDK development with Python pyservicemaker API. Use when building video analytics pipelines, GStreamer-based video processing, TensorRT inference integration, object detection/tracking, or Kafka/message broker integration.

Build DeepStream GStreamer pipelines interactively. Use when the user asks about pipelines for video/image inference, detection, tracking, or streaming — including natural phrases like 'pipeline to infer on image', 'run inference on video', 'detect objects in stream', 'save inference output', 'deepstream pipeline', 'gst-launch pipeline', 'process video with detection', 'build a pipeline', or any request involving GStreamer/DeepStream elements (nvinfer, nvstreammux, nvtracker, etc.).

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Profile a DeepStream pipeline with Nsight Systems and derive its configs from the measurement. Use when the user asks for an efficient, performant, or profiled pipeline — or to benchmark, tune, or measure FPS.

Run and operate the DeepStream Multi-View 3D Tracking reference app, also known as MV3DT. Use when the user asks to set up prerequisites, run shipped MV3DT samples, run Multi-View 3D Tracking on custom synchronized MP4 datasets, import camera calibration, delegate missing calibration to AutoMagicCalib, inspect OSD or BEV visualization, consume MV3DT Kafka metadata, or clean up MV3DT run state in the DeepStream MV3DT app directory.

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Used for extracting selected metadata from one DICOM file and flagging standard-tag PHI presence. Not for anonymization or clinical use.

Used for header-only preflight of one DICOM series folder before conversion or inference. Not for de-identification or clinical clearance.

Used for converting one CT DICOM series folder to a HU NIfTI volume with affine evidence. Not for multi-frame DICOM or clinical use.

Stage 2 of the Clinical ASR Flywheel. Use when curating clinical terms, tagging IPA, and synthesizing a NeMo manifest. NOT for scoring (use /digital-health-clinical-asr-eval).

Stage 3 of Clinical ASR Flywheel. Score a NeMo manifest, produce the five-section KER leaderboard (by-ipa_source diagnostic). Not for ASR auth (/riva-asr).

Stage 4 of the Clinical ASR Flywheel. Use when priority KER is above 0.3 to run stock NeMo SFT on Parakeet TDT v2 and offline cycle N+1 re-eval. NOT for generic word boosting (use /finetune-asr).

Stage 1 of Clinical ASR Flywheel. Use when bootstrapping a cycle: NVCF+MW disclosure, NVIDIA_API_KEY check, deps install, TTS+ASR smoke test.

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