Research

Use when verifying claims before decisions, fact-checking statements against sources, conducting due diligence on vendor/competitor assertions, evaluating conflicting evidence, triangulating source credibility, assessing research validity for literature reviews, investigating misinformation, rating evidence strength (primary vs secondary), identifying knowledge gaps, or when user mentions "fact-check", "verify this", "is this true", "evaluate sources", "conflicting evidence", or "due diligence".

Use when validating product assumptions before building, discovering unmet user needs, understanding customer problems and workflows, testing concepts or positioning, researching target markets, identifying jobs-to-be-done and hiring triggers, uncovering pain points and workarounds, or when users mention user research, customer interviews, surveys, discovery interviews, validation studies, or voice of customer.

Use when building Cmd+K command palettes in React - covers keyboard navigation with arrow keys, keeping selected items in view with scrollIntoView, filtering with shortcut matching, and preventing infinite re-renders from reference instability

Systematic blueprint for reading and annotating academic papers with searchable notes, explicit constraints, and quality gates.

Build plans anchored in research evidence, constraints, and validation gates.

Use when content must be translated between audiences with different expertise, context, or goals while preserving accuracy but adapting presentation. Invoke when technical content needs business framing (engineering decisions → executive summary), strategic vision needs tactical translation (board presentation → team OKRs), expert knowledge needs simplification (academic paper → blog post, medical diagnosis → patient explanation), formal content needs casual tone (annual report → social media post), long-form needs summarization (50-page doc → 1-page brief), internal content needs external framing (roadmap → public updates, bug tracking → known issues), cross-cultural adaptation (US idioms → international clarity, Gen Z → Boomer messaging), medium shifts (written report → presentation script, detailed spec → action checklist), or when user mentions "explain to", "reframe for", "translate this for [audience]", "make this more [accessible/formal/technical]", "adapt for [executives/engineers/customers]", "simplify without losing accuracy", or "same content, different audience". Apply to technical communication (code → business value), organizational translation (strategy → execution), education (expert → novice), customer communication (internal → external), cross-cultural messaging, and anywhere same core message needs different presentation for different stakeholders while maintaining correctness.

Use when writing or reviewing career documents including research statements, teaching statements, diversity statements, CVs, or biosketches. Invoke when user mentions research statement, teaching philosophy, diversity statement, biosketch, academic CV, faculty application, or needs help with career narrative, positioning, or professional documents for academic advancement.

Use when formulating clinical research questions (PICOT framework), evaluating health evidence quality (study design hierarchy, bias assessment, GRADE), prioritizing patient-important outcomes, conducting systematic reviews or meta-analyses, creating evidence summaries for guidelines, assessing regulatory evidence, or when user mentions clinical trials, evidence-based medicine, health research methodology, systematic reviews, research protocols, or study quality assessment.

Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes - four-phase framework (root cause investigation, pattern analysis, hypothesis testing, implementation) that ensures understanding before attempting solutions

Use when reviewing any scientific document for logical clarity, argument soundness, and scientific rigor. Invoke when user mentions check clarity, review logic, scientific soundness, hypothesis-data alignment, claims vs evidence, or needs a cross-cutting scientific logic review independent of document type.

Research and compile the latest AI news from across the industry. Use this skill when asked to find AI news, get AI updates, research what's happening in AI, check for AI announcements, or gather intelligence on AI companies. Triggers include requests for "AI news", "latest AI developments", "what's new in AI", "AI industry updates", or news about specific AI companies (OpenAI, Anthropic, Google, Microsoft, Meta, Amazon, Nvidia, xAI, Mistral, Cohere, Apple, Salesforce).

시장 기회, 경쟁, 트렌드를 평가할 때 포괄적인 시장 조사 분석을 수행합니다.

Reference for project architecture. Use when you need to understand the codebase structure, module organization, request/response flow, or key types.

Use the aissist CLI tool for personal goal tracking, todo management, daily history logging, context-specific notes, guided reflections, and AI-powered semantic recall. Activate when users mention goals, tasks, todos, progress tracking, journaling, work history, personal assistant, meal planning, fitness tracking, or want to search their past activities and reflections.

Use when setting product North Star metrics, decomposing high-level business metrics into actionable sub-metrics and leading indicators, mapping strategy to measurable outcomes, identifying which metrics to move through experimentation, understanding causal relationships between metrics (leading vs lagging), prioritizing metric improvement opportunities, or when user mentions metric tree, metric decomposition, North Star metric, leading indicators, KPI breakdown, metric drivers, or how metrics connect.

Use when facing decisions with multiple legitimate perspectives and inherent tensions. Invoke when stakeholders have competing priorities (growth vs. sustainability, speed vs. quality, innovation vs. risk), need to pressure-test ideas from different angles before committing, exploring tradeoffs between incompatible values, synthesizing conflicting expert opinions into coherent strategy, or surfacing assumptions that single-viewpoint analysis would miss.

Plan LLM fine-tuning and evaluation experiments. Use when the user wants to design a new experiment, plan training runs, or create an experiment_summary.yaml file.

Verify that results, builds, and experiments can be reproduced consistently with documented steps and deterministic inputs.

Use when optimizing multi-factor systems with limited experimental budget, screening many variables to find the vital few, discovering interactions between parameters, mapping response surfaces for peak performance, validating robustness to noise factors, or when users mention factorial designs, A/B/n testing, parameter tuning, process optimization, or experimental efficiency.

Explore open-source repositories, analyze implementation details of libraries/frameworks, and research official documentation. Use when you need to understand how features are implemented in popular open-source projects.