Terse caveman voice: answer first, fluff gone, every technical fact kept. Use for /caveman, "caveman mode", "talk like caveman", "be brief", "less tokens". Stays on until "stop caveman" or "normal mode".
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Use when a coding agent (Claude Code, Cursor, Codex, Copilot CLI) burns through tokens or usage limits, sessions get slow or hit context limits, or the user asks to cut agent cost. Measures where the tokens go, then applies levers in order: session hygiene, always-loaded context (CLAUDE.md, skills, MCP), tool-output filtering, subagent delegation, output verbosity, and model/effort choice.
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This skill should be used when the user asks to "compress context", "summarize conversation history", "implement compaction", "reduce token usage", or mentions context compression, structured summarization, tokens-per-task optimization, or long-running agent sessions exceeding context limits.
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This skill should be used when the user asks to "diagnose context problems", "fix lost-in-middle issues", "debug agent failures", "understand context poisoning", or mentions context degradation, attention patterns, context clash, context confusion, or agent performance degradation. Provides patterns for recognizing and mitigating context failures.
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This skill should be used when the user asks to "understand context", "explain context windows", "design agent architecture", "debug context issues", "optimize context usage", or discusses context components, attention mechanics, progressive disclosure, or context budgeting. Provides foundational understanding of context engineering for AI agent systems.
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Use when the user asks for deep or academic research — a literature review, PRISMA systematic review or meta-analysis, fact-check, evidence synthesis, APA 7 research report, or Socratic guidance to sharpen a vague research question. Runs a 13-agent pipeline with full, quick, review, lit-review, fact-check, socratic, and systematic-review modes.
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Use when working with Entity Framework Core — modeling entities and relationships, migrations, LINQ query performance, change tracking, N+1 problems, and connection and transaction handling.
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This skill should be used when the user asks to "optimize context", "reduce token costs", "improve context efficiency", "implement KV-cache optimization", "partition context", or mentions context limits, observation masking, context budgeting, or extending effective context capacity.
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Runs an 11-step data cleaning and variable screening pipeline for pre-loan credit risk models: sample analysis, OOS split, missing-rate, IV, PSI, and correlation filters. Use when preparing raw credit data for scorecard or default-prediction modeling.
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Implements, adopts, and reviews iOS 26+ Liquid Glass in SwiftUI: glassEffect, GlassEffectContainer, correctness, performance, and design fit. Use when adding Liquid Glass to a SwiftUI app or refactoring existing UI to the new design language.
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Explains and designs Microsoft Fabric Lakehouse solutions: Delta tables, the SQL analytics endpoint, shortcuts, and AI functions. Use when building, optimizing, or documenting a Fabric Lakehouse.
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Use when turning GitHub issues or bug reports into reproducible coding-agent repair loops with sandboxed execution, focused tests, patch generation, and regression verification.
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PostgreSQL-specific code review assistant focusing on PostgreSQL best practices, anti-patterns, and unique quality standards. Covers JSONB operations, array usage, custom types, schema design, function optimization, and PostgreSQL-exclusive security features like Row Level Security (RLS). Use when reviewing SQL, schema, or functions written for PostgreSQL.
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Use when designing or auditing long-term memory for persistent AI agents. Stores immutable source evidence before canonical facts, links beliefs to provenance, applies novelty-aware ADD/UPDATE/NOOP write gating, keeps uncertain merges explicit, separates retrieval from answer generation, supports deletion, and evaluates memory failures layer by layer.
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Refactors and reviews SwiftUI views: consistent layout ordering, subview extraction, dependency injection, Observation, and MV versus view-model patterns. Use when SwiftUI views have grown large or inconsistent.
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Use before writing code on anything non-trivial: a new project or feature, ambiguous or incomplete requirements, a change spanning multiple modules, an architectural decision, or work over ~30 minutes. Runs a gated Specify → Plan → Tasks → Implement workflow with human review at each phase. Skip for single-line fixes.
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Writes and reviews dbt projects targeting Snowflake: models, schema.yml metadata, sources, tests, macros, and project structure. Use when building or reviewing dbt models, generating dbt YAML, or applying dbt best practices.
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Audits SwiftUI performance: rendering, scrolling, CPU and memory, unnecessary view updates, body re-evaluations, and layout thrashing, with Instruments guidance when code review is inconclusive. Use when a SwiftUI screen stutters, scrolls poorly, or uses too much CPU.
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Grounds every implementation decision in official documentation. Use when you want to verify an approach against the official docs before implementing it, or when you want authoritative, source-cited code free from outdated patterns. Use when building with any framework or library where correctness matters.
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Creates structured bug reports for defects found during Oracle-to-PostgreSQL migration. Use when documenting behavioral differences between Oracle and PostgreSQL as actionable bug reports with severity, root cause, and remediation steps.
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Discovers all projects in a .NET solution, classifies each for Oracle-to-PostgreSQL migration eligibility, and produces a persistent master migration plan. Use at the start of an Oracle-to-PostgreSQL migration of a multi-project .NET solution.
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Step-by-step guide for capturing key application requirements for NoSQL use-case and produce Azure Cosmos DB Data NoSQL Model design using best practices and common patterns, artifacts_produced: "cosmosdb_requirements.md" file and "cosmosdb_data_model.md" file. Use when designing a new Azure Cosmos DB for NoSQL data model or reviewing partition keys and document shapes.
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Generates ideas with named methods from creative practice (SCAMPER, lateral provocations, constraints, pataphysics, and more), routed to the user's situation. Use for open-ended generative or selective requests: "I want to make or start something", "I'm stuck", "inspire me", "make this weirder", "help me pick", or inventing a research question.
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Generate flat, minimal light/dark-aware SVG diagrams as standalone HTML files, using a unified educational visual language with 9 semantic color ramps, sentence-case typography, and automatic dark mode. Best suited for educational and non-software visuals — physics setups, chemistry mechanisms, math curves, physical objects (aircraft, turbines, smartphones, mechanical watches), anatomy, floor plans, cross-sections, narrative journeys (lifecycle of X, process of Y), hub-spoke system integrations (smart city, IoT), and exploded layer views. If a more specialized skill exists for the subject (dedicated software/cloud architecture, hand-drawn sketches, animated explainers, etc.), prefer that — otherwise this skill can also serve as a general-purpose SVG diagram fallback with a clean educational look. Ships with 15 example diagrams.
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