Explorar skills

Adaptive summarization — output length scales with input length for comfortable reading. Triggers: "summarize", "summarize this", "tldr", "too long", "sum up", "key points", "give me summary", "eli5", "explain like I'm 5", "explain simply". NOT FOR: translation (just ask), content creation (use content-humanizer). Produces: summary at optimal compression ratio based on input length.

Adversarial red team — find security holes, logic bugs, wasted resources. Triggers: "critic", "red team", "attack this", "what's wrong", "find flaws", "challenge this". NOT FOR: code review before merge (use review), debugging (use systematic-debugging). Produces: prioritized list of flaws with severity and remediation steps.

Use when the user explicitly asks to research the scope of a requirement or proposed change against the current codebase — e.g. "research the scope of this requirement", "what would adding X touch in this codebase", "do scope research on this spec", "/scope-research". Surveys the codebase to surface concrete facts about which files/areas a change would touch and the relevant facts about each touch

Evaluate a shared AI skill or skill repository before installing or copying patterns.

Switch between small local skill profiles: all, coding, writing, or minimal.

Use when the user explicitly asks for an "AB", adversarial, two-sided, or "red-team" code review of code changes they have made — e.g. "AB review my diff", "do an AB review of my changes", "red-team this code change", "have two reviewers argue about my code". Dispatches two opposing sub-agents — one building the evidence-based case that the change is mergeable, one building the case that it must n

Use when about to present long or complex AI-generated markdown to the user — typically >150 lines, >5 H2 sections, or content like code reviews, implementation plans, specs, research reports. Also use when the user explicitly asks to convert a markdown source into a readable HTML report. Produces a single self-contained HTML report (TL;DR hero, sticky TOC with importance stars, semantic-color cal

Use when the user explicitly asks to reflect on, review, or learn from the current Claude Code session — e.g. "review this session", "reflect on this session", "why did that take so many tries", "what went wrong this session", or asks to turn this session's mistakes into project rules. Reads the session transcript, finds where Claude's output was rejected or required correction, distills the root

Structure and interpret a cohort analysis to understand retention, engagement, or behavior patterns over time. Use this skill when a team needs to understand how different user groups behave across their lifecycle.

DCB0129 compliance walkthrough for Digital Clinical Safety Officers and health IT manufacturers. Guides you through NHS England's mandatory Clinical Risk Management Standard for the manufacture of health IT systems — hazard identification, risk assessment, safety case, and DCSO responsibilities.

DCB0160 compliance walkthrough for Digital Clinical Safety Officers and NHS/health organisations deploying health IT systems. Guides through NHS England's mandatory Clinical Risk Management Standard for deployment — deployment safety case, integration hazards, local configuration risks, and staff training requirements.

ML/AI health IT safety assessment for Digital Clinical Safety Officers. Applies NHS Digital's supplementary guidance for Assurance of Machine Learning in Autonomous Systems (AMLAS healthcare) alongside DCB0129/DCB0160. Covers ML component identification, training data bias, model performance, explainability, failure modes, and MHRA regulatory considerations. Use when a health IT system includes AI