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Found 2,408 Skills
Deterministic desk-reject preflight linter for LaTeX paper submissions. Use it when a researcher asks "is my paper ready to submit", or mentions preflight, desk reject, submission check, page limit, anonymization, double-blind, missing checklist, or format/template compliance for a conference (NeurIPS, ICML, CHI, SIGSPATIAL, SIGMOD, ICDE, CVPR, LNCS...). Lints the .tex source against a machine-readable venue profile for documentclass and style-file options, margin/template tampering (geometry, savetrees, spacing hacks), anonymization leaks (author block, acknowledgments, repo links, first-person self-citations, \thanks, pdfauthor), required sections (NeurIPS checklist, ICML impact statement, AI-use acknowledgement, COI, CCS concepts/keywords), abstract length, keywords format, and page-limit risk. Every finding is reported with file:line. Runs bundled stdlib-only Python scripts; advisory only, never submits anything.
Design, generate, review, and revise editable PowerPoint presentations through a rigorous brief-to-PNG workflow using the public pptx-designer Python library.
Use when importing FBX, USD, OBJ or glTF from Maya, ZBrush or Blender into Unreal Engine 5.8 in bulk or reimporting changed files, writing Editor Python tools, Interchange pipelines or import presets, enforcing naming conventions, assigning material instances by script, setting Nanite, collision and LODs per asset class, writing data validators or automation tests, fixing redirectors, or cooking and packaging from the command line (BuildCookRun, RunUAT, CI, Perforce). Also when a scripted import ignores its settings or names, a Python validator never runs, or a Mac build will not package.
Use when sculpting in Blender through Python: sculpt a head, face or bust, a stylized or cartoon character, a creature or a realistic portrait; block out from primitives; cut planes, a jaw or gonial angle, cheekbones; fix a jaw or face that looks like a ball or balloon; stylized hair clumps; eyelids; an expression or smirk; plan remesh or multires resolution; reproduce Clay Strips, Crease, Draw Sharp, Scrape, Grab or mask moves without a tablet; final clay renders; or when a sculpt operator crashes headless.
Configures and implements LiteRT (.tflite) on-device inference on Android in Kotlin using the CompiledModel API on CPU and GPU. Use this skill for any Android LiteRT or .tflite task, including Gradle setup, image classification or vision/audio/embedding helpers, TensorBuffer reuse, CPU memory optimization, Bitmap preprocessing, AndroidViewModel GPU-to-CPU fallback with warm-up on a single-thread dispatcher, LiteRT in Google Play services (play-services-litert), migrating from legacy org.tensorflow.lite.Interpreter, GpuDelegate, or play-services-tflite, and latency benchmarking. Don't use for running generative text LLMs with .litertlm files or for Python model conversion.
macOS launcher automation with Raycast extensions (TypeScript/React) and Alfred workflows (AppleScript/Python) for keyboard-driven productivity
CuTe Python DSL API reference and implementation patterns for NVIDIA GPU kernel programming. Provides execution model, core API table, key constraints, common patterns, and documentation index. Use when: (1) writing or modifying CuTe DSL kernel code, (2) looking up CuTe DSL API syntax, (3) implementing attention/GEMM/MLA patterns in CuTe DSL, (4) understanding CuTe DSL execution model and compilation pipeline, (5) checking what CuTe DSL can and cannot do.
AI demos and GPU compute with Gradio Spaces and Hugging Face Spaces ZeroGPU. Use when writing or reviewing code that uses `@spaces.GPU`, configuring `python_version` or `requirements.txt` for a ZeroGPU Space, or handling ZeroGPU-specific code constraints — pickle-based process isolation, `gr.State` semantics across the worker boundary, no `torch.compile` (use AoTI instead), CUDA wheel-only builds (no `nvcc` at build or runtime), large vs xlarge sizing, and dynamic duration callables. Make sure to use this skill whenever the user mentions ZeroGPU, `@spaces.GPU`, or the `spaces` Python package, or hits ZeroGPU-specific code errors like `PicklingError` across the worker boundary, `illegal duration`, or `flash-attn` wheel-build failures — even when the user does not explicitly ask for ZeroGPU coding guidance. Trigger on `import spaces` or `@spaces.GPU` in code.
Bring any source (a HuggingFace repo, a ComfyUI workflow, a Hub release, a custom container image, a GGUF repo, a Civitai asset, or a community template) to a proven Runpod Serverless endpoint: pick the engine for the modality and model format, size the GPU from the model's real metadata, provision, warm, prove it with one real job, and hand back the URL with what it bills. Deploys an existing source through the Runpod MCP tools; code-first Python deploys (the Flash dev loop) are out of scope. Use when the user wants a model or worker served on Runpod Serverless, whatever the modality: text generation, speech-to-text or text-to-speech, image or video generation, embeddings, document processing, or their own handler image.
Explains Google Cloud Application Integration concepts, region provisioning, IAM roles, run-as service accounts, interface choices (REST, Python discovery client, Terraform, ADK), and platform limits. Use when setting up Application Integration in a Google Cloud project, enabling required APIs, provisioning a region, configuring IAM or run-as service accounts, choosing between Application Integration and custom code, or checking quotas and timeouts. Do not use for building, publishing, or invoking integrations (use application-integration-authoring) or diagnosing failed or stuck runs and logs (use application-integration-troubleshooting).
Port code from C, C++, Python, Go, JavaScript, or TypeScript into idiomatic Rust that keeps the original behavior: ownership instead of manual memory, Result instead of error codes and exceptions, enums instead of flag ints and magic strings, and tests that prove the port matches. Use this whenever the user asks to port, translate, convert, migrate, or rewrite code in Rust ("rewrite this in Rust", "what would this look like in Rust", "move this module to Rust"), even if they don't say "port". Pairs with the rust-best-practices skill, which covers the target code's style.
Local-tooling companion to the bug-bounty orchestrator — carries the SAME complete bug-bounty workflow, but reach for THIS variant when you also need to resolve where tools, wordlists, and clones are installed on the local machine (jhaddix, SecLists, trufflehog, ffuf, dalfox, ghauri); for pure orchestration/routing use the bug-bounty skill. Workflow it covers — recon (subdomain enumeration, asset discovery, fingerprinting, HackerOne scope, source code audit), pre-hunt learning (disclosed reports, tech stack research, mind maps, threat modeling), vulnerability hunting (IDOR, SSRF, XSS, auth bypass, CSRF, race conditions, SQLi, XXE, file upload, business logic, GraphQL, HTTP smuggling, cache poisoning, OAuth, timing side-channels, OIDC, SSTI, subdomain takeover, cloud misconfig, ATO chains, agentic AI), LLM/AI security testing (chatbot IDOR, prompt injection, indirect injection, ASCII smuggling, exfil channels, RCE via code tools, system prompt extraction, ASI01-ASI10), A-to-B bug chaining (IDOR→auth bypass, SSRF→cloud metadata, XSS→ATO, open redirect→OAuth theft, S3→bundle→secret→OAuth), bypass tables (SSRF IP bypass, open redirect bypass, file upload bypass), language-specific grep (JS prototype pollution, Python pickle, PHP type juggling, Go template.HTML, Ruby YAML.load, Rust unwrap), and reporting (7-Question Gate, 4 validation gates, human-tone writing, templates by vuln class, CVSS 3.1, PoC generation, always-rejected list, conditional chain table, submission checklist). Use when you need the local install path of a tool / wordlist / clone for a hunt, or as the full-workflow variant when operating from this local toolkit; for general routing use the bug-bounty skill. 中文触发词：漏洞赏金、安全测试、渗透测试、漏洞挖掘、信息收集、子域名枚举、XSS测试、SQL注入、SSRF、安全审计、漏洞报告