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概要と使いどころ
Brain-augmented web research. Sends brain context about a topic to Perplexity, which searches the web with citations and returns what is NEW vs what the brain already knows. Use for entity enrichment, current-state checks, deal monitoring, and freshness deltas. NOT for simple URL fetches (use web_fetch) or brain-only queries (use gbrain query).
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Verify a research claim or academic citation by tracing it through publication → methodology → raw data → independent replication. Routes through perplexity-research for the actual web lookup, then formats results as a citation-checked brain page. Use when a book/article/conversation cites a study and you want to confirm the claim is real, replicated, and accurately characterized.
日本語の概要は準備中です。原文の説明を表示しています。
Structured data research: search sources, extract structured data, archive raw sources, maintain canonical tracker pages, deduplicate. Parameterized via YAML recipes for investor updates, donations, company updates, or any email-to-structured-data pipeline.
日本語の概要は準備中です。原文の説明を表示しています。
Synthesize user research from interviews, surveys, and feedback into structured insights. Use when you have a pile of interview notes, survey responses, or support tickets to make sense of, need to extract themes and rank findings by frequency and impact, or want to turn raw feedback into roadmap recommendations.
日本語の概要は準備中です。原文の説明を表示しています。
Generate contextual briefings for legal work — daily summary, topic research, or incident response. Use when starting your day and need a scan of legal-relevant items across email, calendar, and contracts, when researching a specific legal question across internal sources, or when a developing situation (data breach, litigation threat, regulatory inquiry) needs rapid context.
日本語の概要は準備中です。原文の説明を表示しています。
Synthesize user research into themes, insights, and recommendations. Use when you have interview transcripts, survey results, usability test notes, support tickets, or NPS responses that need to be distilled into patterns, user segments, and prioritized next steps.
日本語の概要は準備中です。原文の説明を表示しています。
Multi-source research on a customer question or topic with source attribution. Use when a customer asks something you need to look up, investigating whether a bug has been reported before, checking what was previously told to a specific account, or gathering background before drafting a response.
日本語の概要は準備中です。原文の説明を表示しています。
Authoring playbook for building agents that search, read, and synthesize information into a report. Use this when the user wants an agent to research a topic, summarize sources, compare options, do competitive analysis, monitor news, generate briefs, or pull together a citation-backed report from the web or internal documents.
日本語の概要は準備中です。原文の説明を表示しています。
Use when coordinating product work across the 12 bundled product sub-skills (RICE, OKRs, UX research, design tokens, competitive teardown, analytics, experiments, discovery, roadmaps, spec-to-repo, landing pages, SaaS scaffolding) or the 4 standalone product-team plugins (user stories, Apple HIG, code-to-PRD, research summarizer). Triggers on 'help me prioritize', 'plan a product experiment', 'we ship features nobody uses', 'run the discovery loop', 'is our OST sound'. Forks context to route to one sub-skill via a deterministic signal router and returns a digest; can also drive a continuous-discovery loop (Torres cadence tracker + OST linter as machine gates) or a full goal→plan→execute→verify→close run through the repo-wide agent-harness. Distinct from project-management (how to deliver vs what to build), marketing/landing (from-scratch pages), and engineering/agent-harness (the generic loop engine this orchestrator plugs into).
日本語の概要は準備中です。原文の説明を表示しています。
App Store Optimization (ASO) toolkit for researching keywords, analyzing competitor rankings, generating metadata suggestions, and improving app visibility on Apple App Store and Google Play Store. Use when the user asks about ASO, app store rankings, app metadata, app titles and descriptions, app store listings, app visibility, or mobile app marketing on iOS or Android. Supports keyword research and scoring, competitor keyword analysis, metadata optimization, A/B test planning, launch checklists, and tracking ranking changes.
日本語の概要は準備中です。原文の説明を表示しています。
Research across Notion and synthesize into structured documentation; use when gathering info from multiple Notion sources to produce briefs, comparisons, or reports with citations.
日本語の概要は準備中です。原文の説明を表示しています。
Create refined user personas from research data — 3 personas with JTBD, pains, gains, and unexpected insights. Use when building personas from survey data, creating user profiles from research, or segmenting users for product decisions.
日本語の概要は準備中です。原文の説明を表示しています。
Find the questions people ask about a market, how ChatGPT answers them, and which sites get cited in the answers. Use when the user asks what people ask AI about their niche, wants prompt ideas, or wants to know where their brand is missing before choosing prompts to track. Research only; it never saves tracking or starts answer collection.
日本語の概要は準備中です。原文の説明を表示しています。
Synthesize repeated themes from selected research conversations or notes.
日本語の概要は準備中です。原文の説明を表示しています。
Runbook for turning a new HubSpot lead into a researched, personalized follow-up email and a proposed call slot for {{projectName}}. Covers company research, the sales playbook (positioning, tone, qualifying questions), HubSpot record updates, Google Calendar availability, and the human approval gate before anything sends.
日本語の概要は準備中です。原文の説明を表示しています。
Runbook for turning a new HubSpot lead in {{hubspot_list}} into a researched, personalized outbound sequence for {{projectName}}. Covers enrichment research, the outreach playbook (angles, proof points, what to avoid), sequence drafting, HubSpot logging, the daily cap, and the human approval gate before anything sends.
日本語の概要は準備中です。原文の説明を表示しています。
Research first, then draft. This skill never sends generic outreach - it always researches the prospect first to personalize the message. Works standalone with web search, super...
日本語の概要は準備中です。原文の説明を表示しています。
Three-layer bias analysis on any URL, file, or text — auto-fetches the content and any cited study, then audits data-level biases, source conflicts of interest, and journalism-added distortions, separating what the data supports from what's editorialized. USE WHEN bias analysis, analyze bias, bias check, check this study, who funded this, is this source biased, fact-check article, methodological flaws, source credibility, what's wrong with this claim. NOT FOR psychological author analysis, research synthesis (use Research), entity due diligence.
日本語の概要は準備中です。原文の説明を表示しています。
Query curated cybersecurity market and company intelligence. USE WHEN security market data OR cybersecurity funding OR cyber M&A OR security-company/investor research OR market maps OR deal activity OR Return on Security OR The Signal. NOT FOR vulnerability research, security news, or investment advice.
日本語の概要は準備中です。原文の説明を表示しています。
Expert knowledge for AI deep research — methodology, source evaluation, search optimization, cross-referencing, synthesis, and citation formats
日本語の概要は準備中です。原文の説明を表示しています。
Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls
日本語の概要は準備中です。原文の説明を表示しています。
External research synthesis from Trail of Bits, SlowMist, ConsenSys, Immunefi, and Cyfrin. Use this for advanced audit methodology, Echidna/Medusa fuzzing setup, Slither custom detector writing, attack pattern deep dives, or the 4-phase learning roadmap.
日本語の概要は準備中です。原文の説明を表示しています。
Build a working artifact from a plain "implement X for me" request: a running end-to-end spine first, then one feature per rung, with every under-determined decision written to an assumption ledger BEFORE the code that depends on it and a sweep for the ones that slipped through undeclared (same-family provisional in the base Codex mirror). Use when user says "给我实现", "implement X", "帮我做一个能跑的", "先搭个原型再加功能", "build this feature", "prototype then extend", or hands over a capability description rather than an experiment plan.
日本語の概要は準備中です。原文の説明を表示しています。
Autonomous multi-round research review loop. Repeatedly reviews using Gemini via gemini-review MCP, implements fixes, and re-reviews until positive assessment or max rounds reached. Use when user says "auto review loop", "review until it passes", or wants autonomous iterative improvement.
日本語の概要は準備中です。原文の説明を表示しています。