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Found 1,653 Skills
Scan project dependencies for CVEs, outdated packages, and license compliance across npm, pip, cargo, go, maven, and other ecosystems. Use for vulnerability scanning, SBOM generation, supply chain analysis, and automated dependency updates.
This skill should be used when the user wants to refactor TypeScript code to functional patterns or write new code following functional doctrine. Common triggers include "make this functional", "remove the class", "use Result instead of throw", "stop mutating this", and "refactor to factory function". Bakes in factory functions over classes, Result<T,E> over exceptions, immutable state via spread/map/filter, and pure functions composed in pipelines. Skip when the user wants general TS hygiene (use ts-best-practices), the class wraps a stateful SDK (PrismaClient, Octokit, WebSocket), or a framework requires a class.
Use this skill any time a spreadsheet file is the primary input or output. This means any task where the user wants to: open, read, edit, or fix an existing .xlsx, .xlsm, .csv, or .tsv file (e.g., adding columns, computing formulas, formatting, charting, cleaning messy data); create a new spreadsheet from scratch or from other data sources; or convert between tabular file formats. Trigger especially when the user references a spreadsheet file by name or path — even casually (like "the xlsx in my downloads") — and wants something done to it or produced from it. Also trigger for cleaning or restructuring messy tabular data files (malformed rows, misplaced headers, junk data) into proper spreadsheets. The deliverable must be a spreadsheet file. Do NOT trigger when the primary deliverable is a Word document, HTML report, standalone Python script, database pipeline, or Google Sheets API integration, even if tabular data is involved.
Used when you need to chain multiple skills to complete pipeline tasks such as compilation + flashing + monitoring or compilation + flashing + debugging.
Expert knowledge of Apache Airflow for building, scheduling, and monitoring data pipelines and workflows
AI agent skill for CompressO — a free, open-source, offline desktop tool for batch video and image compression built with Tauri + React. Use when the user needs to compress, trim, convert, or embed subtitles into video/image files locally without any network dependency. Covers installation (Homebrew, DMG, MSI, AppImage, DEB), build from source (Rust + Node.js + pnpm), and guidance on FFmpeg/pngquant/jpegoptim/gifski pipelines. Triggers on: compresso, compress video, compress image, batch compression, ffmpeg compression, tauri desktop compression, offline video compress.
Connect SaaS data (HubSpot, Stripe, Salesforce, GitHub, Slack, etc.) to Wren Engine for SQL analysis. Guides the user through the full flow: install dlt, pick a SaaS source, set up credentials, run the data pipeline into DuckDB, then auto-generate a Wren semantic project from the loaded data. Use this skill whenever the user mentions: connecting SaaS data, importing data from an API, dlt pipelines, loading HubSpot/Stripe/Salesforce/GitHub/Slack data, querying SaaS data with SQL, or setting up a new data source from a REST API. Also trigger when the user already has a dlt-produced DuckDB file and wants to create a Wren project from it.
Guides defensive security analysis—alert triage, log and SIEM investigation, threat hunting, detection engineering basics, MITRE ATT&CK mapping, incident scoping, containment recommendations, and DFIR evidence handling for SOC and blue-team analysts. Use when investigating security alerts, writing detection rules, tuning false positives, analyzing EDR/network/auth logs, building timelines of suspicious activity, recommending containment steps, or documenting findings for incident command—not for enterprise security strategy (cybersecurity), CI/CD pipeline hardening (devsecops), offensive pentest execution (authorize red team separately), or LLM adversarial testing (ai-redteam), or designing on-call rotations and postmortem programs (incident-management-engineer).
Check if the development environment is configured properly; if not, first confirm the office location (Chongqing/Beijing), identify the current operating system, system tools, package managers, and eliteforge-* skill environment variable declarations, report them categorized as missing_required, missing_conditional, optional_unset, then attempt to automatically install essential commands and complete configurations such as hosts, Git global settings, Git HTTPS, npm/pip private sources, and pipx packages. Use this skill when the user mentions "check environment configuration", "prepare development environment", "missing commands/hosts/private sources/Git configurations/package management tools/skill environment variables". Trigger threshold: Only use this skill when the user explicitly states that the current project complies with the "璀璨工坊规范" (Bright Workshop Specification) or "eliteforge specification".
Creative-mode PPT pipeline. One full-page 16:9 PNG per slide. LLM / VLM calls go through sn-ppt-standard/lib/model_client.py (shared thin client). Text-to-image (the actual png rendering) goes through sn-image-base/scripts/sn_agent_runner.py. Expects task_pack.json + info_pack.json already written by sn-ppt-entry.
Generates a new image that imitates the style of a reference image while updating content based on user intent. Uses a three-stage pipeline: image annotation (long caption), caption rewriting, and image generation. Use when user asks to "imitate style", "保持这个风格重画", "按这张图风格生成", or "style transfer with new content".
Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.