“logging”的搜索结果

Secure cloud environments — IAM, posture management, logging, and multi-account architecture across providers.

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Pino, Axiom, Sentry installation - one-time project setup for logging and error tracking with source maps upload

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Perform forensic analysis of the macOS Unified Logging System (ULS) to investigate system events, application crashes, kernel panics, and potential indicators of compromise (IoCs) such as persistence mechanisms or unauthorized access.

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Linux security mechanism bypass playbook. Use when facing restricted bash/rbash, read-only or noexec filesystems, AppArmor, SELinux, seccomp filters, or audit logging that must be evaded during post-exploitation.

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Cloud security posture assessment for AWS, Azure, and GCP. Tests IAM privilege escalation paths, public storage exposure, serverless attack surface, database exposure, logging gaps, container registry security, and cloud-specific attacks. Both authenticated (with cloud credentials) and unauthenticated (external) modes. Uses nuclei cloud templates, Prowler, ScoutSuite, manual IMDS/metadata probing, and deep AWS/Azure/GCP CLI enumeration. Produces: cloud architecture diagram, attack path map, findings per category, compliance mapping (SOC 2, PCI DSS 4.0, HIPAA, CIS), PoCs for confirmed exploits. Chains into /gh-export for issue filing.

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Triage the Gaia issue backlog and skill-batch intake queue. Use this skill when someone asks to: "triage issues", "clean up the issue tracker", "review the backlog", "close stale issues", "process the intake queue", "review skill-batches", "evaluate draft skill proposals", "approve or reject pending skills", "is this issue still valid?", "what's clogging the backlog?", "run a triage pass", or /gaia-triage. Covers two workstreams: (1) GitHub issue lifecycle — identifying resolved, stale, or need-more-info issues and acting on them via gh CLI; (2) skill-batch intake — evaluating draft proposals in registry-for-review/skill-batches/ and routing them toward promotion or rejection. This is the gatekeeping step before new skills enter the canonical registry; and (3) a cross-repo triage sweep — sizing, P0-P4 prioritization, umbrella/epic synthesis, and assignment across every gaia-research org repo, on the one headquarters tracker.

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Use when a task needs the judgment of a Construction Trades Supervisor (foreman) — allocating a crew's daily task assignments against a labor-unit budget, deciding whether today's scope requires a formally designated OSHA competent person, arbitrating a trade-sequencing conflict between crews needing the same physical space, tracking crew productivity variance before it shows up on the master schedule, or logging and acting on a near-miss.

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Use Terraform/IaC for Volcengine resources only when the user explicitly chooses Terraform/IaC, already has a Terraform workflow/state, or confirms they need plan/diff/drift/destroy safety for VKE, managed databases/cache/storage, load balancers, domains/certificates, logging/monitoring, or team-managed infrastructure.

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Diagnoses, remediates, and manages Google Cloud Identity and Access Management (IAM) access issues. Supports two distinct operational modes: (1) Requester Flow for developers encountering access denials (capturing error context, self-service PAM JIT activations, elevated developer self-remediation, or logging structured tickets), and (2) Resolver Flow for privileged administrators (authoritative Policy Troubleshooter analysis, deny policy exemptions, least-privilege role discovery, and PAM/IAM provisioning).

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Deploys a baseline landing zone foundation for a Google Cloud Organization, establishing security guardrails using Organization Policies, resource hierarchy folders and projects, billing association, and centralized logging and monitoring. Deploys Google Cloud's recommended security controls and architecture. Use when setting up a new Google Cloud Organization or establishing a secure, enterprise-grade landing zone foundation. Don't use for individual project onboarding (use google-cloud-recipe-onboarding or product-specific skills instead).

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Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (`audit_cluster.sh`), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny and Dataplane V2 logging), isolating high-risk pods inside GKE Sandbox (`gVisor`), enforcing Pod Security Standards (`restricted` labeling), and mounting Secret Manager secrets via CSI (`SecretProviderClass`). Use when auditing cluster security posture, isolating namespaces, applying pod security standards, setting up Workload Identity, or configuring network policies and secret volume mounts. Don't use for cluster-wide control plane security, RBAC hardening, Binary Authorization, Shielded Nodes, or enabling platform-level GKE add-ons (use gke-platform-security instead).

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Creates Cloud Storage (Google Cloud Storage, or GCS) buckets. Analyzes the workload (sensitive data, media hosting, ingestion, web hosting, archiving, backup, logging, analytics, AI/ML, or general-purpose), validates project-level security settings, and designs a secure-by-default, cost-effective configuration (location, storage class, uniform bucket-level access, public access prevention, soft delete, lifecycle) before creating it. Use whenever a user wants to create, make, set up, provision, or spin up a bucket, or needs object storage for an app, service, pipeline, or dataset — even a "simple" or "default" bucket, or when bucket creation is one step in a larger workflow. Outputs or executes the creation via gcloud, the JSON/REST API, Terraform, or SDK client libraries (C++, Java, Python, Go). Don't use for anything other than creating new buckets — for uploads, downloads, access changes, or reconfiguring existing buckets, use google-cloud-storage-basics.

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Convert genomic coordinates between assembly versions (GRCh37/hg19 to GRCh38/hg38, mm9 to mm10). Guides UCSC liftOver for BED files, CrossMap for VCF/bigWig, and handles unmapped regions with provenance logging.

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Spring Boot 4 observability with Actuator, Micrometer, and OpenTelemetry. Use when configuring health indicators, health checks, custom metrics, distributed tracing, production endpoint exposure, Kubernetes probes, Cloud Run probes, logging, or monitoring. Covers Actuator endpoint security, Micrometer Timer/Counter/Gauge patterns, OpenTelemetry span customization, liveness/readiness probe setup, and metric tag best practices.

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FrontendHire's job-search operating system (fh) — a strict pipeline tracker, deterministic weekly metrics, and a review ritual. Runs the search as a measured system where the philosophy is law. Use for logging applications/leads/referral asks, the weekly review, status/metrics, and the onboarding interview.

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Use when building Python 3.11+ applications requiring type safety, async programming, or robust error handling. Generates type-annotated Python code, configures mypy in strict mode, writes pytest test suites with fixtures and mocking, and validates code with black and ruff. Invoke for type hints, async/await patterns, dataclasses, dependency injection, logging configuration, and structured error handling.

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Prepares Go applications to work effectively with AI coding agents. Use when setting up a new Go project or retrofitting an existing one to ensure: structured JSON logging (slog/Zap/ZeroLog/Logrus), machine-readable command output, thorough golangci-lint configuration, non-interactive CLI design with --yes flags, structured error handling with meaningful exit codes, proper context.Context propagation, graceful shutdown, health check endpoints, and a standardized Makefile. Triggers when a user asks to make their Go app "agent-ready," "AI-friendly," wants to improve agent tooling/observability in a Go project, or needs to audit an existing Go project against agent-readiness best practices.

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Error handling review — silent failure detection, catch block analysis, error logging.

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Install, configure, and work with Turso DB — an in-process SQLite-compatible relational database engine written in Rust. Use when the user needs to (1) install Turso DB, (2) create or query databases with the tursodb CLI shell, (3) use Turso from JavaScript/Node.js via @tursodatabase/database, (4) work with vector search or embeddings in Turso, (5) set up full-text search with FTS indexes, (6) configure transactions including MVCC concurrent transactions, (7) enable encryption at rest, or (8) use Change Data Capture (CDC) for audit logging.

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Outbound SDR (Sales Development Representative) operating system that runs the full top-of-funnel motion across HubSpot (CRM, engagement, and source of truth) and Exa (web research + signals). Use this skill WHENEVER the user is doing outbound sales development, building target/prospect lists, defining or refining an ICP, enriching leads, researching accounts or people before outreach, writing cold emails or multi-touch sequences, qualifying or scoring leads, logging activity to CRM, handing meetings off to AEs, or reporting on pipeline. Trigger it even when the user only says things like "find me 50 leads", "research this account", "write a cold email to this person", "score these leads", "log this in HubSpot", or "build a sequence"; these are all SDR tasks this skill governs. Do not wait for the user to say "SDR" or "prospecting" explicitly.

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Set up application observability with Prometheus, Grafana, Datadog, or CloudWatch — instrument metrics endpoints, configure Golden Signal dashboards, define alert rules with burn-rate SLOs, and add structured logging

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Audit whether a product is ready for enterprise sales. Use when you need to assess SSO/SAML/SCIM support, RBAC maturity, multi-tenancy data isolation, audit logging coverage, public API quality, SLA operational readiness, SOC2/ISO27001 certification gaps, GDPR data residency controls.

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Audit ML experiment tracking infrastructure for reproducibility gaps, parameter logging completeness, metric capture, artifact management, and pipeline orchestration. Covers MLflow, Weights and Biases, DVC, Sacred, Neptune, Hydra configs, model registries, and produces a reproducibility scorecard (0-30) with actionable fixes for data science teams.

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FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, adapted from FLOW under CC BY 4.0). Use when user says "FLOW framework", "blog flow", "/blog flow", "evidence-led blogging", or "find optimize win", or asks for stage-specific blog prompts. Not for generic prompt writing.

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