Research

OpenAI-powered legal research with UK legal API integration (legislation.gov.uk, caselaw.nationalarchives.gov.uk). Implements RAG pipeline for context-aware legal queries. Use when implementing AI chat features, legal document retrieval, or case law searches.

Conduct systematic research and opportunity investigation for ACP protocol integration, collaboration, and enhancement opportunities. Use when the user wants to research external projects, protocols, or tools for potential collaboration; investigate gap-filling opportunities for IDE integrations; assess compatibility between ACP and external protocols; or identify opportunities for ACP adoption. Guides through discovery, analysis, validation, and RFC generation with mandatory gates and source grounding. Outputs include comprehensive summary documents, gap analyses, and formal RFC proposals.

Use when the user wants to create, edit, list, or manage Claude Code rules in .claude/rules/. Provides guided elicitation to configure path-specific rules with proper YAML frontmatter and glob patterns. Can list all available rules from user-level and project directories.

Create production-grade, accessible Jekyll themes for researchers conducting "research in public." Generates complete lab notebook-style themes with Tufte-inspired sidenotes, KaTeX math rendering, and WCAG 2.1 AA compliance. Use when building Jekyll themes for scientific journals, experiment logs, field notes, or research documentation sites. Supports collections for organizing experiments and field notes, responsive sidenote rendering (sidebar on desktop, inline on mobile), and full-width layout options.

This skill should be used whenever users request personal assistance tasks such as schedule management, task tracking, reminder setting, habit monitoring, productivity advice, time management, or any query requiring personalized responses based on user preferences and context. On first use, collects comprehensive user information including schedule, working habits, preferences, goals, and routines. Maintains an intelligent database that automatically organizes and prioritizes information, keeping relevant data and discarding outdated context.

Comprehensive research, analysis, and content extraction system. Multi-source parallel research using available researcher agents. Deep content analysis with extended thinking. Intelligent retrieval for difficult sites. Fabric pattern selection for 242+ specialized prompts. USE WHEN user says 'do research', 'extract wisdom', 'analyze content', 'find information about', or requests web/content research.

Recognize when synthesis has reached saturation through observable signals. Use when: (1) asked when to stop exploring or gathering data, (2) new examples reinforce existing patterns without new dimensions, (3) themes identified early hold across additional observations, (4) predictions about next observations prove accurate.

Lock tasks to user-provided sources (PDF/txt/URL) by creating a per-task context container, ingesting and chunking only those sources, ranking chunks for relevance, and enforcing negative constraints against out-of-scope content. Use when strict source grounding is required.

Build production-grade CLI tools with Bun. Reference implementation covering argument parsing patterns (--flag value, --flag=value, --flag), dual markdown/JSON output, error handling, subcommands, and testing. Use when building CLIs, designing argument parsing, implementing command structures, reviewing CLI quality, or learning Bun CLI best practices.

Comprehensive SEO content strategy including keyword research, content cluster architecture, technical SEO audit, on-page optimization guidelines, 90-day content calendar, link building strategy, and success metrics for sustainable organic growth

Create relationships between issues via comment references

Create separate documentation that preserves discovered intent from historical investigation so future readers benefit from your research. Use when: (1) completing code archaeology investigation and needing to ensure discoveries benefit future developers facing the same confusion, (2) recurring questions from team members signal that institutional knowledge needs capture in ADRs or documentation files, (3) team member departure would take critical undocumented context about design decisions with them, (4) creating onboarding documentation where critical context exists only in git history or senior engineers' memories

Create epic-specific documentation skill with external reference docs. MUST BE USED for caching external docs. Fetches URLs, caches full content, uses documentation-engineer to generate cohesive summaries, and creates auto-activating skill. Use when starting work on epic that requires external documentation context (API docs, tool guides, reference materials), or when user mentions "cache docs", "external docs", "API documentation", URLs for docs, documentation needs, reference materials, knowledge caching, or epic context documentation.

Deep code intelligence via Narsil MCP providing 76 tools for security scanning (OWASP, CWE, taint analysis), call graph analysis (CFG, DFG, callers/callees), structural queries (symbols, definitions, references), and supply chain security (SBOM, license compliance). Accessed via Code Mode for token efficiency.

Master API documentation with REST references, OpenAPI specs, code examples, and developer-friendly API guides.

This skill should be used whenever users need help planning trips, creating travel itineraries, managing travel budgets, or seeking destination advice. On first use, collects comprehensive travel preferences including budget level, travel style, interests, and dietary restrictions. Generates detailed travel plans with day-by-day itineraries, budget breakdowns, packing checklists, cultural do's and don'ts, and region-specific schedules. Maintains database of preferences and past trips for personalized recommendations.

Build validated specifications from discussion documents through collaborative refinement. Third phase of research-discussion-specification-plan-implement-review workflow. Use when: (1) User asks to create/build a specification from discussions, (2) User wants to validate and refine discussion content before planning, (3) Converting discussion documents into standalone specifications, (4) User says 'specify this' or 'create a spec' after discussions, (5) Need to filter hallucinations and enrich gaps before formal planning. Creates specifications in docs/workflow/specification/{topic}.md that technical-planning uses to build implementation plans.

Use when you need information you don't have, expertise outside your comfort zone, or fresh eyes on code - dispatches agents to research, advise, or review. NOT for implementation delegation (see subagent-driven-development).

Systematic data validation, error detection, cross-source reconciliation, and query correctness checking for analytical work. Use when validating Snowflake queries, catching calculation errors, reconciling metrics across different data sources, checking for null values, ensuring date range validity, detecting statistical anomalies, validating metric calculations (median vs mean, rate normalization), checking aggregation grain (per-record vs per-entity), validating contribution analysis for non-additive metrics, or validating consistency across analysis sections. Essential when reviewing analysis before publication, debugging unexpected results, or ensuring data quality in reports. Triggers include "validate this query", "check for errors", "why don't these numbers match", "should I use median or mean", "why don't contributions sum to 100%", "reconcile these metrics", "verify data quality", or any request to catch potential issues in data or calculations.

Discover structural and causal relationships between seemingly unrelated observations to reveal how things influence each other. Use when: (1) asked to find relationships between separate ideas or map how observations connect, (2) multiple observations seem unrelated but might share causal links or common mechanisms, (3) building system models that show interaction patterns and feedback loops, (4) seeking solutions by recognizing analogous mechanisms from different contexts.