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Found 350 Skills
Systematic debugging methodology with root cause analysis. Phases: investigate, hypothesize, validate, verify. Capabilities: backward call stack tracing, multi-layer validation, verification protocols, symptom analysis, regression prevention. Actions: debug, investigate, trace, analyze, validate, verify bugs. Keywords: debugging, root cause, bug fix, stack trace, error investigation, test failure, exception handling, breakpoint, logging, reproduce, isolate, regression, call stack, symptom vs cause, hypothesis testing, validation, verification protocol. Use when: encountering bugs, analyzing test failures, tracing unexpected behavior, investigating performance issues, preventing regressions, validating fixes before completion claims.
Persistent shared memory for AI agents backed by PostgreSQL (fts + pg_trgm, optional pgvector). Includes compaction logging and maintenance scripts.
Structured JSON logging with correlation IDs, request context propagation across async boundaries, performance timing decorators, and worker metrics collection.
Operating system for logging market/competitive signals with severity, confidence, and routing.
Monitoring and observability patterns for Prometheus metrics, Grafana dashboards, Langfuse LLM tracing, and drift detection. Use when adding logging, metrics, distributed tracing, LLM cost tracking, or quality drift monitoring.
Dynamic tier system for right-sizing n8n workflow hardening. Use this skill on ANY n8n workflow request to determine appropriate validation, logging, and error handling levels. Adapts to user needs — from quick prototypes to mission-critical production systems.
OWASP Top 10 CI/CD Security Risks - prevention, detection, and remediation for pipeline security. Use when securing or reviewing CI/CD - flow control, IAM, dependency chain, poisoned pipeline execution, PBAC, credential hygiene, system config, third-party services, artifact integrity, logging and visibility.
Automatically generate complete Python project deliverables from natural language requirements through collaboration among four virtual roles: autonomous learning, PM, architect, and senior programmer. Supports feature expansion, project refactoring, and skill invocation. Also supports web search, knowledge integration, version control, Python 3.11+ features, UV package management, loguru logging, and project size adaptation (folder/single file). It provides support for database design and implementation (SQLite, PostgreSQL, MongoDB, vector databases, graph databases), data layer abstraction (Repository pattern), and database switching. Suitable for scenarios such as software requirement clarification, rapid prototyping, project initialization, feature expansion, and code refactoring.
Provides comprehensive patterns for deploying Next.js applications to production. Use when configuring Docker containers, setting up GitHub Actions CI/CD pipelines, managing environment variables, implementing preview deployments, or setting up monitoring and logging for Next.js applications. Covers standalone output, multi-stage Docker builds, health checks, OpenTelemetry instrumentation, and production best practices.
Persistent shared memory for AI agents backed by PostgreSQL (fts + pg_trgm, optional pgvector). Includes compaction logging and maintenance scripts.
Technical safeguards and architectural patterns for building HIPAA-compliant software on AWS. Use when building healthcare SaaS, handling PHI (Protected Health Information), designing patient data systems, implementing healthcare APIs, setting up HIPAA-eligible AWS infrastructure, reviewing code for PHI exposure, designing audit logging, or when the user mentions patients, medical records, EHR/EMR, health data, HL7, FHIR, or covered entities. Essential for founders and developers building in healthcare or digital health space.
Make application behavior visible to coding agents by exposing structured logs and telemetry. Use when asked to "add telemetry", "make logs accessible to agents", "add observability", "debug with logs", or when an agent needs to understand runtime behavior but has no way to query logs. Also use when debugging is difficult because there are no structured logs, when agent docs (CLAUDE.md, AGENTS.md) lack instructions for querying application logs, or when setting up logging infrastructure for a new or existing web application.