PointPillars for 3D object detection from LiDAR point clouds. Encodes point clouds into a pseudo-image via a pillar-based representation, then applies 2D detection — used in autonomous driving and robotics. Use when training, evaluating, exporting, pruning, retraining, or running inference for a TAO PointPillars model. Trigger phrases include "train PointPillars", "LiDAR 3D detection", "point-cloud object detection", "pillar-based 3D detector".

Optical Inspection for defect detection using Siamese networks. Compares image pairs to detect manufacturing defects, anomalies, or quality issues. Use when training, evaluating, exporting, or running inference for a TAO Optical Inspection model on AOI / quality-control data. Trigger phrases include "train optical inspection", "AOI defect detection", "Siamese defect classifier", "PCB / manufacturing inspection".

Grounding DINO for open-set object detection. Combines DINO-style detection with a BERT text encoder for language-guided detection — detects objects described by text prompts without a fixed class vocabulary. Use when training, evaluating, exporting, quantizing, or running inference for a TAO Grounding DINO model. Trigger phrases include "train Grounding DINO", "open-vocabulary detection", "text-prompted detector", "language-guided object detection".

The mandatory pre-launch gate and four-verb execution contract for every TAO workflow or action. Invoke BEFORE launching anything side-effecting — AutoML, train, evaluate, inference, export, TensorRT engine generation, or DEFT/application workflows — on any execution platform. Covers platform selection, credentials, image confirmation, dataset intake, preflight, the launch review, job records, monitoring, and failure/retry classification. Trigger phrases include "train this model", "run AutoML", "launch on SLURM/docker/k8s/brev/virtualenv", "evaluate my checkpoint", "start a TAO job".

Cosmos-Embed1 video-text embedding for text-to-video retrieval, video-to-video search, semantic deduplication, and fine-tuning. Use when the user asks to "fine-tune Cosmos-Embed1", "run cosmos-embed inference", "export Cosmos-Embed1", "embed videos", or "search videos with text".

Performs gap analysis on NVIDIA TAO VCN Classify (Visual Component Net) experiments by invoking the pinned TAO data-services container directly via `docker run … gap_analysis vcn_aoi …` — picks the optimal decision threshold, ranks per-sample weakness, and emits a top-K weakest parquet expanded per-lighting for downstream augmentation. Use when analyzing VCN classification failures, picking SDA augmentation targets, or auditing PASS/NO_PASS boundary cases.

Use this skill for OpenFold3, NVIDIA's BioNeMo NIM microservice for biomolecular structure prediction. Invoke whenever the user mentions OpenFold3 or needs protein, protein-ligand, protein-DNA/RNA, or multi-chain complex prediction with the hosted NVIDIA API or local Docker NIM. Covers endpoint choice, auth, request payloads, output artifacts, confidence scores, and local container setup.

GPU-accelerates scientific Python on NVIDIA hardware and verifies that the result is correct and faster. Use for CUDA/GPU optimization; CPU-bound NumPy, SciPy, pandas, scikit-learn, NetworkX, scikit-image, vector-search, image-processing, graph, simulation, or file-I/O workloads; CuPy, cuDF, cuML, cuGraph, cuVS, cuCIM, KvikIO, Warp, Newton, Numba-CUDA, or RAFT questions; and profiling, memory-transfer, kernel, or multi-GPU bottlenecks. Also use when large data-parallel Python code is slow and GPU acceleration is a plausible option, even if the user does not name CUDA.

Use when writing a pull request title or body, when about to run gh pr create, when about to git push on a branch that already has an open PR, or when the user says /pr-description, "write the PR description", or "update the PR title". Don't use for commit messages, changelogs, or review comments.

Delegate a single write task to Cursor Composer via cursor-companion.sh inside an isolated worktree, then Lead-review the diff and cherry-pick. Use when user invokes cursor:do, says delegate to cursor, have composer write it, refactor with cursor, hand a file edit to Composer. Do NOT load for: planning, code review only, read-only investigation, or multi-task team runs (use breezing --cursor or cursor:ask instead).

Automated vulnerability triage and dependency bumps for the git_integration worker (Python via uv + the two vendored Go modules). Fetches open Dependabot alerts scoped to git_integration manifests, correlates them with open Dependabot PRs, classifies each fix as safe or needs-human, validates safe fixes locally on the PR branches merged with latest main (uv sync / lint / pytest + full git-integration image build, which compiles both Go modules), updates safe PR branches server-side so they are one click from merge, then posts a "safe to merge" review summary to Slack and opens PRs for alerts Dependabot has no PR for. NEVER merges PRs, never dismisses alerts, never deploys without explicit user confirmation. Use when the user says "fix vulns", "bump deps", "dependabot triage", or asks about git_integration vulnerabilities.

How framework-owned tables in packages/core declare and change their schema with defineStore(). Use before adding a table, column, index, backfill or seed to a core store, before writing an ensure*Table() function, and when guard:release-schema-complete or SchemaNotMigratedError fires.

AI(ChatGPT·Claude·Gemini 등)가 쓴 한글 텍스트를 "사람이 쓴 글처럼" 윤문해주는 오케스트레이터 스킬. 번역투·영어 인용 과다·기계적 병렬·관용구·피동태 남용·접속사 남발·리듬 균일성·이모지/불릿 과다 등 10대 카테고리 85개 AI 티 패턴을 탐지·분류해 내용은 한 글자도 건드리지 않고 문체·리듬·표현만 자연스러운 한국어로 재작성한다. shim의 route_hint(light|standard|heavy)로 경로를 정해 잘 쓴 글은 1콜, 표준은 2콜, 중증·장문만 3+콜(진단→겨냥 윤문→finalize)로 처리한다. 트리거 — "AI 티 없애줘", "AI 같은 글 자연스럽게", "GPT/ChatGPT 문체", "AI 번역투 고쳐", "사람이 쓴 것처럼 윤문", "AI 윤문", "ChatGPT 티 제거", "한글 AI 탐지·윤문", "AI 글 사람처럼", "번역투 제거", "영어 인용 많은 글 윤문", "AI 글 티 안 나게", "휴머나이저", "humanize Korean", "AI detector bypass 한글". 후속 작업 — "특정 카테고리만 다시", "윤문 강도 조정", "장르 바꿔서", "이 문단만", "2차 윤문" 도 모두 이 스킬. 단순 맞춤법·오탈자 교정은 직접 처리, 번역은 번역 스킬, 내용 추가·삭제를 동반한 재작성은 별도 집필 스킬.

Review and optimize application data access that uses Entity Framework Core (EF Core). Use for EF Core performance and correctness reviews of services, repositories, imports, and background jobs: slow or memory-heavy queries, excessive round-trips, hidden SaveChanges, premature materialization, dynamic query construction, result-graph tracking, bulk writes, DbContext lifetime or overlapping operations, search semantics, paging, raw SQL, and model/index coverage. Use whether or not EF Core owns the schema. For EF Core, not Dapper or raw ADO.NET.

Build the site-facing parts of an EmDash CMS project on Astro, including schema and seeds, content queries, Portable Text rendering, menus, taxonomies, widgets, and deployment configuration. Use for EmDash site and theme work; use creating-plugins instead when the task is plugin authoring alone.

Build the site-facing parts of an EmDash CMS project on Astro, including schema and seeds, content queries, Portable Text rendering, menus, taxonomies, widgets, and deployment configuration. Use for EmDash site and theme work; use creating-plugins instead when the task is plugin authoring alone.

Build the site-facing parts of an EmDash CMS project on Astro, including schema and seeds, content queries, Portable Text rendering, menus, taxonomies, widgets, and deployment configuration. Use for EmDash site and theme work; use creating-plugins instead when the task is plugin authoring alone.

Build the site-facing parts of an EmDash CMS project on Astro, including schema and seeds, content queries, Portable Text rendering, menus, taxonomies, widgets, and deployment configuration. Use for EmDash site and theme work; use creating-plugins instead when the task is plugin authoring alone.

Create, review, or fix Agent Skills for GitHub Copilot. Use when asked to create, scaffold, improve, or repair a SKILL.md and its paired harness evaluation.

Beta desktop control through the connected A0 CLI host. Use for the user's host/local computer screenshots, screen inspection, menus, native app UI, OS-level clicking, scrolling, typing, or checking computer_use_remote status. Use instead of linux-desktop for host/local machine control. Do not use for ordinary browser navigation; host browser requests should use the browser tool.