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Found 1,066 Skills
ML supply chain security scanner. Scans model files, scores risk (0-100), maps to 5 global compliance frameworks (ISM-2072, EU AI Act, OWASP LLM, MITRE ATLAS, NIST AI RMF), and provides remediation steps. Zero-config, auto-installs scanners. Use when the user asks to scan a model, check if a model is safe, audit ML security posture, check compliance, inspect pickle/safetensors/pytorch files, or mentions model supply chain security. Also trigger on ISM-2072, EU AI Act, OWASP LLM06, model risk score, "is this model safe", "scan my models", "check compliance".
CrewAI architecture decisions and project scaffolding. Use when starting a new crewAI project, choosing between LLM.call() vs Agent.kickoff() vs Crew.kickoff() vs Flow, scaffolding with 'crewai create flow', setting up YAML config (agents.yaml, tasks.yaml), wiring @CrewBase crew.py, writing Flow main.py with @start/@listen, or using {variable} interpolation.
Live SEO data via DataForSEO MCP server. SERP analysis (Google, Bing, Yahoo, YouTube), keyword research (volume, difficulty, intent, trends), backlink profiles, on-page analysis (Lighthouse, content parsing), competitor analysis, content analysis, business listings, AI visibility (ChatGPT scraper, LLM mention tracking), and domain analytics. Requires DataForSEO extension installed. Use when user says "dataforseo", "live SERP", "keyword volume", "backlink data", "competitor data", "AI visibility check", "LLM mentions", or "real search data".
Firecrawl produces cleaner markdown than WebFetch, handles JavaScript-heavy pages, and avoids content truncation. This skill should be used when fetching URLs, scraping web pages, converting URLs to markdown, extracting web content, searching the web, crawling sites, mapping URLs, LLM-powered extraction, autonomous data gathering with the Agent API, or fetching AI-generated documentation for GitHub repos via DeepWiki. Provides complete coverage of Firecrawl v2.8.0 API endpoints including parallel agents, spark-1-fast model, and sitemap-only crawling.
Add Opik tracing to an existing codebase. Detects language (Python/TypeScript), identifies LLM frameworks, adds appropriate decorators and integrations, marks entrypoints, and wires up environment config. Use for "instrument my code", "add opik tracing", "add observability", or "trace my agent".
Corrects outdated LLM knowledge about the Vercel platform and introduces new products. Injected at session start.
Privacy-first planning-only statement export for banks, cards, brokerages, and payment platforms. The LLM builds a manual download checklist with official URLs, suggested date ranges, export formats, and staging directory, but does not use browser tools or browser automation. Use when account files are not on disk yet and the user wants more privacy. CLEAR step: C (Capture)
Social listening and brand monitoring strategy — monitoring, Boolean queries, sentiment, competitive intel, crisis detection, AI visibility monitoring, LLM brand mentions. Platform comparison (Meltwater, Brandwatch, Talkwalker, Brand24, Sprout Social, Mention, Hootsuite, BrandJet, Influencity), monitoring setup (keywords, sources, alerts), sentiment analysis, competitive benchmarking (share of voice), crisis detection (real-time alerts, escalation), consumer insights, and reporting. Use when you don't know what people are saying about your brand, competitors are getting mentioned more than you, negative sentiment is spiking and you need to understand why, you're missing PR crises until it's too late, you can't tell if your brand shows up in AI/LLM answers, or you need to pick the right social listening tool. Do NOT use for platform-specific config (use /sales-meltwater), influencer discovery (use /sales-influencer-marketing), social media publishing/scheduling, or SEO keyword research (use /sales-semrush).
Generate professional company tear sheets using S&P Capital IQ data via the Kensho LLM-ready API MCP server. Use this skill whenever the user asks for a tear sheet, company one-pager, company profile, fact sheet, company snapshot, or company overview document — especially when they mention a specific company name or ticker. Also trigger when users ask for equity research summaries, M&A company profiles, corporate development target profiles, sales/BD meeting prep documents, or any concise single-company financial summary. This skill supports four audience types: equity research, investment banking/M&A, corporate development, and sales/business development. If the user doesn't specify an audience, ask. Works for both public and private companies.
Trigger when the user wants to create a new dashboard, set up monitoring for a service or infrastructure component, or import a pre-built dashboard template. Includes requests like "create a dashboard for PostgreSQL", "monitor my Redis cluster", "set up observability for my k8s cluster", "I need a dashboard for tracking LLM costs".
Master local LLM inference, model selection, VRAM optimization, and local deployment using Ollama, llama.cpp, vLLM, and LM Studio. Expert in quantization formats (GGUF, EXL2) and local AI privacy.
Investigates distributed application performance using PostHog APM (OpenTelemetry span) data via MCP. Use when the user asks about service traces, slow HTTP/database spans, error spans, trace IDs, or span attributes — not LLM analytics traces or product logs. Uses posthog:query-apm-spans, posthog:apm-trace-get, posthog:apm-services-list, posthog:apm-attributes-list, and posthog:apm-attribute-values-list.