Automatically optimize Supabase PostgreSQL queries by analyzing execution plans, adding indexes, and improving RLS policies. Triggers when user mentions slow queries, performance issues, or query optimization.
WHEN: SQL query review, query optimization, index usage, N+1 detection, performance analysis WHAT: Query plan analysis + Index recommendations + N+1 detection + Join optimization + Performance tuning WHEN NOT: Schema design → schema-reviewer, ORM code → orm-reviewer
Review database operations, migrations, and data persistence. Analyzes query safety, migration rollback, transaction boundaries, and data integrity. Use when reviewing migrations, models, repositories, or database queries.
Use this skill to read ChatTwo chat logs as a debug console. Covers querying the SQLite database, finding script runs, and extracting debug evidence for bug analysis.
Structured performance profiling and optimization skill for identifying and eliminating bottlenecks. Auto-triggers on slowness keywords.
Explains code in simple terms with visual diagrams. Use when the user asks to explain code, understand how something works, or wants a code walkthrough.
REQUIRED Phase 2 of /dev workflow after dev-brainstorm. This skill should be used when the user asks to 'explore the codebase', 'map architecture', 'find similar features', 'discover test infrastructure', 'trace execution paths', 'identify code patterns', or needs to understand WHERE code lives and HOW it works before implementation. Launches parallel explore agents and returns prioritized key files list.
Debug and analyze {{PROJECT_NAME}} LangGraph agent traces. Use when investigating agent behavior patterns, finding failures, analyzing latency, or understanding why Orchestrator/Analyst responses went wrong. Covers trace queries by agent tags, pattern analysis across runs, and common debugging scenarios.
Token-efficient codebase exploration using RepoPrompt CLI. Use when user says "use rp to..." or "use repoprompt to..." followed by explore, find, understand, search, or similar actions.
Deep code and system analysis using the Quantum Cognitive OS. Use when analyzing code, architectures, or complex systems that benefit from multi-dimensional reasoning.
Comprehensive research and analysis using Claude (subagents), Gemini CLI, and Codex CLI. Multi-perspective research with cross-verification, iterative refinement, and 100% citation coverage. Use for security analysis, architecture research, code quality assessment, performance analysis, or any research requiring rigorous verification and multiple AI perspectives.
Comment quality review. Detects LLM generation traces, implementation history, conversational tone, HOW vs WHY patterns, and redundant comments.
Read and analyze Inspect AI evaluation log files using the Python API. Extract samples, messages, events, and metrics from .eval files.
Structured code debugging through hypothesis formation and falsification planning. Use when diagnosing bugs, unexpected behaviour, or system failures where the root cause is unclear. Produces a hypothesis document for execution by another agent rather than performing the investigation directly. Triggers on requests to debug issues, diagnose problems, investigate failures, or create debugging plans.
Use when diagnosing agent failures, debugging lost-in-middle issues, understanding context poisoning, or asking about "context degradation", "lost in middle", "context poisoning", "attention patterns", "context clash", "agent performance drops"
Comprehensive truth scoring, code quality verification, and automatic rollback system with 0.95 accuracy threshold for ensuring high-quality agent outputs and codebase reliability.
Comprehensive Claude Code conversation analysis skill for deep-diving into CC session logs. Use when analyzing exported Claude Code conversations to understand: project patterns, error rates, command failures, security risks, session duration, tool usage, and workflow efficiency. Triggers: "analyze conversation", "CC analysis", "conversation analysis", "session review", "Claude Code logs", "analyze my sessions", "review CC usage", "conversation insights", "what went wrong in my session", "session forensics", "CC forensics"
Senior Next.js 15 expert with 15+ years experience. Generate production-ready code with strict TypeScript typing, professional JSDoc, solid architecture patterns, complete test coverage, security best practices, and performance optimization. Use for any code generation, features, refactoring, debugging, or architecture decisions in this SaaS project.
Analyzes research queries to determine if new strategies are needed, optimizes existing strategies using Langfuse traces, and generates strategy templates
This skill should be used when the user asks to "query Langfuse traces", "show sessions", "check LLM costs", "analyse token usage", "view observations", "get scores", "create score", "add score to trace", "query metrics", or mentions Langfuse, traces, or LLM observability. Also triggers on requests to analyse API latency, debug LLM calls, or investigate model performance. Use for prompt management tasks like "list prompts", "get prompt", "create prompt", "update prompt labels", or "deploy prompt to production". Use for dataset management tasks like "list datasets", "create dataset", "add dataset item", "view dataset runs", or "manage evaluation datasets".
Observability patterns for metrics, logs, and traces. Use when implementing monitoring, setting up Prometheus/Grafana, configuring logging pipelines, implementing distributed tracing, or designing alerting systems.
Comprehensive debugging strategies, tools, and techniques for efficiently identifying and fixing bugs across different environments.
