Research

Run end-to-end competitor research and monitoring through the Hyper MCP — pick the set, scrape every public surface (site, blog, pricing, organic social, search rank, mentions, demand) via Firecrawl + HyperSEO + the Apify scrapers, diff against last run, and synthesize a brief. Use when the user wants to research competitors, build a battle card or comparison page, run a weekly digest, or asks "what's [competitor] doing?" or "how do we compare?".

User provides a theme for research, or research is needed before generation.

Research a target note, idea, project, or topic. Preserve external sources as traceable source records by default; write summaries only when the user asks for synthesis, assessment, or a concise research brief. Any source used in a summary keeps a complete immutable copy in `raw/processed/YYYY-MM-DD/`. Notes tagged `external` are treated as clipped/imported external sources and are eligible for synthesis while preserving the complete source record. Portent reference: `../references/portent-kn...

You are a recruiting intelligence researcher. Your job is to deep-research a company so a recruiter walks into outreach fully prepared — not winging it.

Use after /shep-kit:new-feature to analyze technical approach, evaluate libraries, document decisions. Triggers include "research", "technical analysis", "evaluate options", "which library", or explicit /shep-kit:research invocation. Part of the Shep autonomous SDLC platform — https://shep.bot

Academic paper search via Google Scholar using Serper API

Researches a topic by breaking it into subtopics, gathering factual information with reasoning, and producing a structured summary with key findings and open questions. Use when the user asks to research, investigate, look up, summarize a topic, or says 'what is known about...' or 'learn about...'

Orchestrated multi-source research using a persistent agent team. Use for thorough investigation of complex topics requiring diverse sources, cross-referencing, synthesis, and iterative refinement with user feedback. Decomposes topics into subtopics, spawns a team of specialist researchers that stay with the team through synthesis and revision, and produces a unified report with ACM citations.

Use when conducting user research, creating personas or journey maps, planning usability testing, synthesising user feedback, evaluating information architecture, analysing user behaviour, or any task that requires understanding what users need, think, or feel.

Use this skill to review Jiang Lens concept docs, episode reads, glossary drafts, canon proposals, or source-grounded public pages with adversarial reader/world-model and grounding/provenance passes before handoff or promotion.

Analyze competitor websites for UX patterns, strengths, and weaknesses. Navigates external URLs, documents UX patterns, evaluates against Nielsen heuristics, and extracts actionable learnings. Trigger keywords: competitor analysis, competitor UX, competitive analysis, benchmark, compare competitors.

Convert raw discovery notes into structured insights using atomic research methods adapted for data products. Use when synthesizing findings, reviewing evidence, summarizing research, writing problem briefs, or when someone asks "what did we learn?" or "what does the evidence say?"

Research assistant — gathers information, performs competitive analysis, and generates structured research reports. Use when the user asks to research a topic, compare options, analyze competitors, investigate a question, compile findings, or produce a market or technical report.

Synthesize user interviews, survey results, feedback, and support tickets into structured insights. Identifies themes, pain points, and opportunities. Generates research reports ready for stakeholders.

Generate end-to-end PM case studies from real AI product launches, pivots, and decisions. Analyzes what happened, why, what the PM likely decided, trade-offs made, and lessons learned.

Structured teardown of AI products (ChatGPT, Claude, Gemini, Perplexity, Copilot, etc.). Analyzes product decisions, UX patterns, technical architecture, business model, and competitive positioning.

Framework for navigating AI safety, ethics, and capability trade-off discussions. Covers responsible scaling, content policy, bias, privacy, dual-use, and alignment.

Research a domain before building products. Tests Claude's baseline knowledge, identifies gaps, scans the marketplace, runs deep research, and recommends the optimal product setup. Use when: 'scout', 'research domain', 'what should I build for', 'analyze domain', or before /create for any non-trivial product.

Extract and structure domain knowledge into domain-map.md for product creation. Asks targeted questions, maps expertise for /create and /fill. Use when the creator says 'map', 'map my knowledge', 'extract expertise', or before complex products.

Search the marketplace, analyze competition, discover gaps, and find inspiration. Use when: 'explore', 'search marketplace', 'what exists for', 'competitors', 'market gap', 'trending', or before browsing the marketplace. For domain research with build recommendations, use /scout instead.

Multi-source deep research — search, synthesize, and deliver cited reports. Use when the user wants thorough research on any topic with evidence and citations.

Deep learning compile framework — transforms raw information into actionable judgment. Use when the user wants to deeply understand a topic, not just capture it.

Conduct deep, multi-source research on technologies, companies, markets, and topics — synthesizing findings into structured reports with citations and actionable insights

Have a focused discussion about a topic and reach a conclusion