Skill UI

Guideline for front-end JavaScript reverse engineering using js-reverse MCP tools and jshookmcp. Covers locating signature and encrypted parameters, tracing request initiators, runtime sampling via hooks, local Node environment reconstruction, and evidence-first output for reproducing signing logic.

This skill programmatically searches and fetches biomedical literature from PubMed and PMC using NCBI E-utilities (ESearch, EFetch, ESummary). It allows users to gather large corpora of abstracts, full texts, and metadata related to specific conditions, drugs, or genes. Ideal for building evidence bases, performing biomedical Named Entity Recognition (NER), or summarizing scientific findings.

This mode defines an autonomous, senior-level AI agent capable of resolving complex tasks from start to finish with minimal human supervision. It operates by strictly verifying every claim against real evidence (live web search, codebase, available tools). The agent prioritizes action over questioning, only interrupting the user for irreversible actions, genuine dead-ends, or critical ambiguities, ensuring maximum efficiency in hands-on engineering and operational workflows.

This skill transforms diverse raw source materials—such as articles, transcripts, and notes—into a highly structured summary suitable for a presentation or talk. It automatically detects the source type (Real-world Evidence or Conceptual Thesis), extracts the narrative arc, identifies key metrics, outlines main themes, and critically highlights potential gaps, providing a complete foundation for content creation.

This skill performs deep 'git archaeology' by cross-referencing commit history, changelogs, and initial summaries. It extracts verified metrics like commit velocity, contributor rankings, and release timelines, generating multiple detailed reports. Use this when building a REX talk and needing factual, data-backed evidence from a source git repository.

A comprehensive skill designed to transform contested questions or complex decisions into a structured, auditable evidence map. It systematically tracks all supporting, contradicting, qualifying, and missing evidence, ensuring that reasoning relationships and source regions are preserved for rigorous review.

A rigorous framework to prevent confident fabrication (hallucination) by treating competence as an evidence boundary. It guides users to classify claims—Grounded, Partial, or Ungrounded—based on verifiable sources. It mandates assessing the cost of wrongness before answering, fetching, or abstaining, ensuring that assertions are always backed by sufficient evidence.

This advanced methodology builds an evidence-linked causal chain from a localized fault to an actionable systemic root. It moves beyond simple proximate causes by requiring counterfactual testing, verifying multiple independent pathways, and prescribing countermeasures that eliminate enabling conditions, ensuring true recurrence prevention.

A rigorous methodology for systematic problem diagnosis and consequential decision-making. It operates in two modes: Problem Analysis (for root cause identification using IS/IS-NOT contrast) and Decision Analysis (for prioritizing options by screening MUSTs, weighting WANTs, and assessing adverse consequences). Ideal for complex engineering and business scenarios requiring structured evidence and risk assessment.

A rigorous framework for making evidence-based estimates and forecasts when precise data is unavailable. It guides users to anchor predictions on base rates, articulate uncertainty using confidence intervals, and systematically update beliefs using likelihood ratios (prior to posterior) as new evidence arrives. Ideal for risk sizing, project timelines, and strategic decision-making.

This framework, known as Via Negativa, guides iterative improvement by prioritizing removal and simplification over adding new features. It mandates that before introducing any new complexity, dependency, or layer, one must exhaust all possibilities of eliminating redundant, harmful, or nonessential elements. It emphasizes evidence-based decision-making and risk assessment, ensuring that new additions are truly necessary after thorough subtraction passes.

This framework provides a rigorous pre-execution review mechanism for proposed actions by AI agents or systems. It functions as a critical safety boundary, auditing the entire decision path—from initial request to proposed tool execution—without authorizing or executing anything. It verifies action conformity, evidence integrity, temporal validity, and reviewer independence to ensure system compliance and security.