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Found 377 Skills
Configures Depot-managed GitHub Actions runners as a drop-in replacement for GitHub-hosted runners. Use when setting up or migrating GitHub Actions workflows to use Depot runners, choosing runner sizes (CPU/RAM), configuring runs-on labels, setting up ARM or Windows or macOS runners, troubleshooting GitHub Actions runner issues, configuring egress filtering, using Depot Cache with GitHub Actions, or running Dagger/Dependabot on Depot runners. Also use when the user mentions depot-ubuntu, depot-windows, depot-macos runner labels, or asks about faster/cheaper GitHub Actions runners.
Run DuckDuckGo web searches from the terminal using `ddgr` with JSON output. Use when the task calls for lightweight web search, quick result triage, or filtering by region/time/site.
Vercel Firewall and security expert guidance. Use when configuring DDoS protection, WAF rules, rate limiting, bot filtering, IP allow/block lists, OWASP rulesets, Attack Challenge Mode, or any security configuration on the Vercel platform.
Implement Syncfusion React TreeView component for hierarchical data display with node selection, drag-drop reordering, inline editing, and custom templating. Use this when building organizational charts, file systems, navigation trees, or any multi-level hierarchical interface. Covers selection modes, checkboxes, filtering, sorting, keyboard navigation, accessibility, and performance optimization with stateless templates.
PHP Web source code CRLF/response splitting audit tool. Identifies user input that enters HTTP response headers, analyzes filtering and encoding of newlines/control characters, and outputs severity ratings, PoCs and fix suggestions (omission is prohibited).
Use this skill whenever the user wants to work with survey data using the `survy` Python library. Triggers include: loading or reading survey CSV/Excel/JSON/SPSS files, handling multiselect (multi-choice) questions, computing frequency tables or crosstabs, exporting survey data to SPSS (.sav) or other formats, updating variable labels or value indices, transforming survey data between wide/compact formats, filtering respondents, replacing values, adding/dropping/sorting variables, or any task involving survy's API (read_csv, read_excel, read_json, read_polars, read_spss, crosstab, survey["Q1"], to_spss, to_csv, to_excel, to_json, etc.). Also trigger when the user says things like "analyze my survey", "process questionnaire data", "build a survey analysis script", or "help me with survy". Always read this skill before writing any survy code — it contains the correct API, patterns, and gotchas.
Command-line interface for AdGuard Home - Network-wide ad blocking and DNS management via AdGuard Home REST API. Designed for AI agents and power users who need to manage filtering, DNS rewrites, clients, DHCP, and query logs without a GUI.
Resolves experiment references from natural language to concrete experiment IDs. Handles name lookups, fuzzy descriptions ('the signup experiment', 'my latest experiment'), status filtering, and disambiguation when multiple experiments match. TRIGGER when: user refers to an experiment by name, description, or relative reference ('latest', 'most recent', 'the one I created yesterday') and you don't already have the experiment ID. DO NOT TRIGGER when: user provides an experiment ID directly, or you already resolved the experiment earlier in the conversation.
Implements Syncfusion ASP.NET Core Grid component for feature-rich data tables and grids. Use this when working with data display, sorting, filtering, grouping, aggregates, editing, or exporting. This skill covers grid configuration, CRUD operations, virtual scrolling or infinite scrolling, hierarchy grids, state persistence, and advanced data management features for data-intensive applications.
Search the web using Google via AceDataCloud API. Use when searching for web pages, images, news, maps, local places, or videos. Supports localization, time filtering, and pagination. Returns structured results with titles, snippets, URLs, and rich data.
Managed vector database for production AI applications. Fully managed, auto-scaling, with hybrid search (dense + sparse), metadata filtering, and namespaces. Low latency (<100ms p95). Use for production RAG, recommendation systems, or semantic search at scale. Best for serverless, managed infrastructure.
GPU-accelerated data curation for LLM training. Supports text/image/video/audio. Features fuzzy deduplication (16× faster), quality filtering (30+ heuristics), semantic deduplication, PII redaction, NSFW detection. Scales across GPUs with RAPIDS. Use for preparing high-quality training datasets, cleaning web data, or deduplicating large corpora.