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Found 2,408 Skills
INVOKE THIS SKILL when setting up a new project or when asked about package versions, installation, or dependency management for LangChain, LangGraph, LangSmith, or Deep Agents. Covers required packages, minimum versions, environment requirements, versioning best practices, and common community tool packages for both Python and TypeScript.
Use this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
Registers functions and triggers on the iii engine across TypeScript, Python, and Rust. Use when creating workers, registering function handlers, binding triggers, or invoking functions across languages.
Server-side quantitative indicator runner via Longbridge Securities — execute Pine Script v6 syntax subset against historical K-line data on Longbridge servers without a local Python environment. Supports built-in indicators (MACD, RSI, Bollinger Bands, EMA, SMA, etc.) and custom calculation logic; results returned as JSON. Triggers: "量化指标", "Pine Script", "指标计算", "MACD计算", "RSI计算", "服务端指标", "指标脚本", "量化脚本", "技术指标运行", "量化指標", "指標計算", "MACD計算", "RSI計算", "服務端指標", "指標腳本", "quant indicator", "Pine Script", "indicator calculation", "run indicator", "server-side quant", "MACD script", "RSI calculation", "technical indicator runner", "quant run".
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.
Sui SDK landscape — which SDK to pick (TypeScript, Rust, or community-maintained Python/Go/Dart/Kotlin/Swift), how they map to each other, and how to install and wire each one up. Use when a user is starting a new Sui project in any language, migrating between languages, comparing APIs across SDKs, or asking "what SDK should I use for X?". For deep patterns inside a single SDK, route to that SDK's reference file.
Catalyst AppSail — persistent backend PaaS with managed runtimes (Node.js, Java, Python) and custom Docker containers. Trigger on 'AppSail', 'persistent server', 'Docker on Catalyst', 'X_ZOHO_CATALYST_LISTEN_PORT', or 'long-running process on Catalyst'. Do NOT use for stateless request/response handlers, event-driven functions, or scheduled jobs — use catalyst-functions instead.
Polishes LaTeX tables and figures to publication quality. Use when a researcher mentions table formatting, booktabs, figure layout, subfigures, captions, colorblind-safe colors or palettes, cref/cleveref consistency, overfull hbox, column widths, or asks to make tables and figures look professional or camera-ready for a venue (ACM, IEEE, NeurIPS, LNCS, CHI). Converts hline-and-vertical-rule tables to booktabs, sizes columns within the page budget, lays out subfigures with subcaption, applies caption conventions per venue family (table captions above, figure captions below, ACM Description alt-text), fixes ref/cref inconsistencies, and recommends colorblind-safe palettes (Okabe-Ito, Paul Tol) using bundled stdlib-Python linters and palette generators. Edits presentation only — never alters data values, units, or results.
Build integrations with the Factorial HR API using the official TypeScript (@factorialco/api-client) and Python (factorial-api-client) SDKs. Use when authenticating, calling endpoints, paginating list results, subscribing to webhooks, or typing webhook handler payloads against the Factorial API.
Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.
Drive external CLI coding agents (Claude Code, Codex, OpenCode) as first-class AG2 Agents over the Agent Client Protocol (ACP): point an Agent at a ClaudeCodeConfig / CodexConfig / OpenCodeConfig preset from ag2.acp and ask()/run() it like any agent, with its thinking, tool calls, plans and permission prompts externalized onto AG2's event stream. Use when orchestrating, observing, or gating CLI coding agents from Python — permission_policy ask/auto/deny HITL gating, fs_root confinement, turn timeouts, in-process testing via fake_acp_config. To expose an AG2 agent to other systems instead, see ag2-a2a or ag2-mcp; for approval plumbing see ag2-hitl.
Finds secrets, tokens, passwords, and API keys that can leak through generated serialization: Python dataclass repr and asdict, attrs, pydantic model_dump, NamedTuple, JavaScript JSON.stringify and util.inspect, structured logs, tracing spans, and error reports. Use when asked to "check for secret serialization", "credential in repr", "repr=False audit", "secret in spans", "token in logs", or when a change adds a credential field to a dataclass, model, or config object, or adds code that stringifies whole objects or every kwarg into telemetry.