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Found 56 Skills
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".
Project conventions for Laravel projects, including application code, project-supporting PHP, Inertia Blade shells, React Email templates, schema-backed models, and Pest test routing. Use when creating or changing routes, config, localization, bootstrap/public entrypoints, seeders, root tooling PHP, models, migrations, relationships, casts, factories, resources, actions, console commands, middleware, listeners, policies, providers, support classes, resources/views Blade shells, resources/react-email mail templates/exports, or tests under Unit, Integration, Feature, or Architecture, plus Browser only when a real browser suite exists. Encodes strict typed PHP, unguarded Eloquent models, public NanoID route keys, indexed *_id columns without database foreign key constraints, migrations without down() methods, controller/API feature test ordering, and system-logic model integration tests.
This skill should be used when working with Strudel.cc, a live-coding music environment. Use when creating musical patterns, drum sequences, melodies, basslines, or generative compositions. The user will always want to run Strudel code in the browser, either by copy-pasting or by providing a clickable URL with the code encoded in base64.
Regulatory variant interpretation -- GWAS association lookup, eQTL analysis, chromatin state annotation, regulatory element overlap, and trait ontology resolution. Connects GWAS Catalog, GTEx, ENCODE, RegulomeDB, OpenTargets, OLS ontology, and Ensembl regulatory features. Use when users ask about non-coding variants, GWAS hits, eQTLs, regulatory elements, enhancer/promoter variants, or trait-associated SNPs.
Use this skill to generate a package.xml (and optionally destructiveChanges.xml, destructiveChangesPre.xml, or destructiveChangesPost.xml) from a local source directory, an explicit component list, or org introspection. Trigger when the user says "generate a package.xml from this folder", "create a manifest for these classes", "I need a deploy manifest", "build package.xml for the contacts changes", or "create both package.xml and destructiveChanges.xml for these deletions". Encodes which metadata types accept a wildcard member and which must be enumerated, avoiding the common "Wildcards are not supported for this metadata type" deploy failure. DO NOT TRIGGER for executing a deploy (use platform-metadata-deploy), performing the deletion in destructiveChanges.xml (use platform-destructive-deploy), or retrieving metadata (use platform-metadata-retrieve).
Vision-language pre-training framework bridging frozen image encoders and LLMs. Use when you need image captioning, visual question answering, image-text retrieval, or multimodal chat with state-of-the-art zero-shot performance.
Classical cipher analysis playbook. Use when encountering substitution ciphers, Vigenere, transposition, XOR, or encoded text in CTF challenges that requires frequency analysis, Kasiski examination, or known-plaintext cryptanalysis.
Professional-grade contract review skill that adds comment-based issue annotations without changing original text. Enforces a four-layer review (entity verification, basic, business, legal), writes structured comments (issue type, risk reason, revision suggestion) with risk level encoded via reviewer name, and generates a contract summary, consolidated opinion, and Mermaid business flowchart (with rendered image). Output language must follow the contract’s language.
How to write benchmarks in Motoko using bench‑helper. Covers project setup (mops.toml), bench file layout in bench/*.bench.mo, the Bench.Schema rows/cols model, and safe patterns for encode/decode, hashing, crypto, and allocation benches.
Use when the user has one or more video clips and wants to add post-production on top — AI-generated cover as first frame, HTML/CSS captions synced to SRT, kinetic illustration overlays at hook moments, chapter chips, end-card CTA, or any other timed motion graphics. Most often used as the downstream of `/wjs-segmenting-video` — pick up where that skill stopped (raw cropped clip + per-clip SRT) and produce the upload-ready MP4. Backed by HyperFrames so everything compiles to ONE final encode — no cascade of re-encodes. Triggers — "加封面", "加字幕", "加动画", "加 CTA", "做后期", "post-production", "title card", "kinetic captions", "end card".
Implement Swift Codable models for JSON and property-list encoding and decoding with JSONDecoder, JSONEncoder, CodingKeys, and custom init(from:) or encode(to:). Use when parsing API responses, remapping keys, flattening nested JSON, handling date or data decoding strategies, decoding heterogeneous arrays, or integrating Codable with URLSession, SwiftData, or UserDefaults.
Migrate vision/detection/segmentation small models to Ascend NPU, covering the full workflow: model structure analysis, migration verification, performance profiling, and optimization. Based on torch_npu and msprof Use this skill when the user wants to: (1) migrate encoder-only models like ResNet, YOLO, UNet to Ascend NPU, (2) analyze model structure for migration feasibility, (3) verify model inference on NPU, (4) identify performance bottlenecks and get optimization suggestions Trigger: user mentions "migrate", "migration", "Ascend", "NPU", "YOLO", "ResNet", "encoder-only", "detection", "segmentation", "adaptation", "adapt", "迁移", "昇腾迁移", "小模型", "适配", "昇腾适配", "GPU迁移", "NPU适配", "适配NPU", "适配昇腾"