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Found 1,836 Skills
Salesforce DevOps automation using sf CLI v2. TRIGGER when: user deploys metadata, creates/manages scratch orgs or sandboxes, sets up CI/CD pipelines, or troubleshoots deployment errors with sf project deploy. DO NOT TRIGGER when: writing Apex code (use generating-apex), building LWC components (use generating-lwc-components), creating metadata definitions (use generating-custom-object or generating-custom-field), or querying org data (use handling-sf-data).
Patterns for building applications that integrate the Krea API. Auth, polling discipline, error handling, validation, frontend integration (SvelteKit/React/Vue), and the 'prototype in chat, productize in app' workflow. Use when the user is writing code that calls the Krea API directly — building a generator UI, a content pipeline, a creative tool — not when they just want to generate one image. For interactive generation use the sibling krea-ai skill instead.
Configure specific Sentry features beyond basic SDK setup. Use when asked to monitor AI/LLM calls, set up OpenTelemetry pipelines, or create alerts and notifications.
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 cloud compliance—mapping SOC 2, ISO 27001, HIPAA, PCI DSS, FedRAMP, and data-residency requirements to cloud controls; collecting audit evidence from AWS, GCP, and Azure APIs; shared-responsibility narratives; CSPM/Config continuous monitoring; customer assurance questionnaires (CAIQ/SIG); and cloud-specific gap remediation before attestations. Use when scoping regulated workloads in cloud, preparing cloud control evidence for auditors, interpreting provider compliance artifacts (BAA, PCI AOC, FedRAMP packages), or proving residency and logging in multi-account estates—not for org-wide GRC programs and audit coordination without cloud evidence (compliance-specialist), non-cloud systems evidence automation (compliance-engineer), implementing security guardrails (cloud-security-engineer), legal DPAs or contract redlines (commercial-counsel), security strategy (cybersecurity), or CI pipeline gates only (devsecops).
Guides product infrastructure security—securing the runtime, data plane, and control plane that ships with the product: multi-tenant isolation, service-to-service auth, customer data boundaries, secure defaults in APIs and workers, abuse-resistant rate limits, product-scoped secrets and encryption, and security design reviews for product infra changes. Use when threat-modeling product features, designing tenant isolation, hardening service mesh or internal APIs, reviewing product IaC/modules for data leaks, defining secure baselines for microservices the product team owns, or partnering on incidents affecting customer workloads—not for corporate IdP/SIEM (information-security-engineer), CI pipeline gates only (devsecops), SOC operations (defensive-security-analyst), authorized pentest execution (offensive-security-analyst), general IDP golden paths (platform-engineer), company-wide GRC (cybersecurity), or applied AI solution architecture for LLM features (applied-ai-architect-commercial-enterprise).
Guides privacy research engineering for safeguards—PII and sensitive-data detection research, redaction and de-identification evals, memorization and extraction risk studies, privacy benchmarks and labeled corpora, logging/retention minimization for safety pipelines, and research memos on privacy–utility trade-offs for guardrail systems. Use when measuring PII detector quality, designing privacy eval suites for moderation stacks, studying training-data leakage or prompt logging risk, or recommending privacy mitigations for safeguard models—not for SOC 2/GDPR evidence automation (compliance-engineer), legal DPIA or AI policy (ai-risk-governance), harm/toxicity classifier R&D (ml-research-engineer-safeguards), production inference gateways (ml-infrastructure-engineer-safeguards), or general non-privacy research (ai-researcher).
Scores inbound HubSpot leads by engagement signals, company fit, and urgency markers to produce a "call these 5 today" list with talking points, drafts the follow-ups, and blocks Calendar time. Use when the user asks to prioritize leads, who to call first, or about their pipeline.
Generate video summary reports using the VSS video_search_frag extension with Long Video Summarization (LVS), Enterprise RAG knowledge retrieval, and human-in-the-loop parameter collection. Use when: user wants to generate a video summary, report, or analysis using the frag pipeline.
Diagnose and fix broken Goldsky Compose apps interactively. Triggers on: compose app in error state, crashlooping, not running, not processing tasks, cron not firing, HTTP trigger returning 500, onchain event listener missing events, wallet errors, gas sponsorship failures, 'No bundler provider available', manifest validation errors, bundling/esbuild failures, secret missing, 'You cannot use a smart wallet in local dev', 'Transaction Receipt failed with status'. Also use when the user mentions a Compose app name alongside a problem, even if they don't say 'compose' explicitly, if they're referring to `goldsky compose` commands (not `goldsky turbo` or `goldsky pipeline`). Runs `status`/`logs`/`secret list`/`wallet list` to identify root cause, and offers fixes. For building a new app from scratch, use /compose instead. For manifest field / CLI flag / API lookups without an active problem, use /compose-reference instead. Do NOT trigger on Turbo or Mirror pipeline problems.
End-to-end data engineering pipeline using Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization
End-to-end data engineering pipeline for Harvard Art Museums API with ETL, SQL analytics, and Streamlit visualization