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Found 364 Skills
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 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).
Code style and quality rules for Megatron Bridge — ruff configuration, naming conventions, type hints, mypy rules, docstrings, copyright headers, logging, and the code review checklist.
Explains middleware concepts, patterns, and implementations. Covers server middleware, edge middleware, request/response pipelines, and common use cases like auth, logging, and CORS. Use when implementing middleware or understanding request processing pipelines.
Implement request logging, tracing, and observability. Use for debugging, monitoring, and production observability.
Self-report agent issues by logging user corrections for later review, then resume with the correct skill. Use when a user says "don’t do that", "stop doing X", "always do Y", or requests self-correction.
Set up comprehensive observability for Databricks with metrics, traces, and alerts. Use when implementing monitoring for Databricks jobs, setting up dashboards, or configuring alerting for pipeline health. Trigger with phrases like "databricks monitoring", "databricks metrics", "databricks observability", "monitor databricks", "databricks alerts", "databricks logging".
Use when adding logging to services, setting up monitoring, creating alerts, debugging production issues, designing SLIs/SLOs, or implementing structured logging (Pino, Winston), metrics (Prometheus, DataDog, CloudWatch), or distributed tracing (OpenTelemetry).
Parse raw text from an Instagram or TikTok Story insights screenshot and format it into a clean, spreadsheet-ready row with labeled fields. This skill should be used when parsing Story metrics from a screenshot, formatting Story insights for a spreadsheet, extracting metrics from a pasted Story screenshot, cleaning up Story analytics data, converting Story insights text into structured data, turning a Story performance screenshot into a row for the tracker, logging Story metrics into a spreadsheet, normalizing Story screenshot data, pulling numbers from a Story insights paste, organizing Story metrics from creator screenshots, processing a batch of Story screenshots into rows, building a Story metrics tracker from screenshots, or entering Story data from a screenshot into a sheet. For normalizing metrics from multiple sources into a unified table, see metrics-normalization-formatter. For calculating engagement rates and comparing to benchmarks, see engagement-rate-calculator-benchmarker.
This skill should be used when the user asks to "harden code", "security hardening", "improve security posture", "add security headers", "tighten security", "defensive coding suggestions", or "proactive security improvements". Also triggers when the user asks about CSP, CORS hardening, rate limiting, input validation improvements, security logging, or defense-in-depth measures.
Logging best practices for applications and services including structured logging, log levels, and log management strategies
Full Sentry SDK setup for Elixir. Use when asked to "add Sentry to Elixir", "install sentry for Elixir", or configure error monitoring, tracing, logging, or crons for Elixir, Phoenix, or Plug applications. Supports Phoenix, Plug, LiveView, Oban, and Quantum.