/topic-research

name: topic-research
description: "Research any topic end to end and get back a fact-checked, branded HTML report plus an agent-readable markdown brief. Use when the user wants to research a topic, do a deep dive, build a research report or briefing, gather evidence before writing a piece of content, understand 'what does the research say / what are people saying about X', or study a subject across papers, forums, YouTube, and vendor/web sources. Fans out parallel sub-agents (one per source type), verifies every hard claim with the fact-checker, synthesizes one sourced markdown brief, renders it as a branded report, and deploys it live. Connector-adaptive: uses Firecrawl, Apify, a scholarly/PubMed MCP, YouTube/vidIQ, and Reddit where available, and falls back to web search, web fetch, and browser-use where they are not. Trigger on 'research this topic', 'topic research', 'do a deep dive on', 'build me a research report', 'what does the evidence say about', or 'research X before I write about it'."
disable-model-invocation: true

Topic Research

Turns a topic into a rigorous, sourced, fact-checked report. Two artifacts come out every time: a **markdown brief** (agent-readable, the handoff to a content-writer skill) and a **branded HTML report** (human-facing, deployable). The workflow is: interview the user so the research is targeted, fan out parallel research sub-agents across source types, verify every hard claim, synthesize to markdown, render to HTML, deploy.

> [!important] The two rules that make this good > 1. **Targeted, not generic.** The Phase 0 Q&A shapes every sub-agent. Research aimed at a purpose beats a topic dump every time. > 2. **Verify before you publish.** No hard number reaches the report without a fact-check verdict and a caveat. Fact and opinion stay visibly separate.

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Phase 0: Targeted Q&A + capability probe

Do both before any research runs. Ask conversationally, one thing at a time, adapting to answers.

A. Targeted Q&A (this shapes the whole run)

1. **Topic**, and what they already believe or suspect about it. 2. **Purpose**: is this to write a specific piece of content (which format? which audience?), to make a decision, to brief a team, to prep for a talk? A content purpose changes what the report emphasizes and how the markdown is structured downstream. 3. **Angle / biases / skepticism to reflect**: a point of view they want the research to support or pressure-test, claims they are suspicious of, hot takes to stress-test. These become explicit search directives for the sub-agents. If they have none, the research stays neutral and simply reports the tension it finds. 4. **Depth**: Quick / Standard / Deep (see below). 5. **Output**: report + markdown always; deploy target is a Claude live artifact (instant, no infra) or Vercel (stable custom URL). Ask which. 6. **Brand**: default to the neo-brutalist "Signal Report" look in `assets/report-template.html`. If they have a brand (site, design system, colors/fonts/logo), extract and restyle; the CSS is token-driven at the top of the template.

Capture the answers in a short `brief-config` (topic, purpose, audience, directives, depth, deploy, brand) that you carry through every phase.

B. Capability probe

Detect which connectors are available (see `references/connectors.md` for the exact checks and how to connect each): Firecrawl, Apify, a scholarly/PubMed MCP, YouTube MCP / vidIQ, Reddit MCP, browser-harness, and the Vercel deploy path.

Show the user the research plan you will run given what is connected, then **offer to connect the high-value missing ones** (Firecrawl and Apify are the biggest upgrades; PubMed only matters for medical topics). If they decline, name the fallback each stream will use and continue. Never hard-fail for a missing connector; degrade.

Depth control (user preference + topic-aware)

• **Quick**: 2-3 streams, shallow read, ~1 fact-check pass. For fast-moving or lightly-researched topics.
• **Standard**: all 4 streams, ~15 sources, full fact-check. The default.
• **Deep**: all 4 streams with more agents per stream, wider reading, adversarial fact-check (multiple verifiers per claim).
• **Auto-downshift**: if a topic has little scholarly literature (true for most marketing topics), lighten or skip the scholarly stream rather than padding it, and say so. Do not fake depth.

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Phase 1: Parallel research fan-out

Launch **one sub-agent per source type, concurrently** (send them in a single batch). Give each the topic + purpose + the user's angle/biases as explicit directives. Each returns structured markdown: findings (one source URL each), a "hard claims to verify" list, and vendor/anecdotal claims flagged. Full parameterized prompts are in `references/research-agents.md`.

| Stream | Job | Primary connector | Fallback ladder | |---|---|---|---| | **Scholarly / evidence** | Credible papers, studies, citations only. Domain-aware: medical → PubMed/PMC (+ bioRxiv/medRxiv, ClinicalTrials); AI/CS → arXiv; general → Semantic Scholar / Scholar | Scholarly/PubMed MCP + Firecrawl paper index | web search on journal / `.edu` / `.gov` domains → browser-use | | **Forums / community** | Real language, pain points, firsthand results (anecdotal), objections | Apify Reddit scraper | Reddit MCP → web search `site:reddit.com` + fetch → browser-use | | **Web / vendor** | Vendor docs, industry data, reputable blogs, the tool/landscape | Firecrawl search + scrape | web search + web fetch → browser-use for JS-heavy pages | | **Creators / video** | What top creators teach; recurring tactics + contrarian takes | YouTube MCP + vidIQ + transcripts (yt-dlp / `watch`) | Apify YouTube scraper → web search for summaries |

Keep per-stream reading within the depth budget and have each agent `log`/report anything it deliberately skipped. No silent truncation.

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Phase 2: Fact-check pass

Collect every "hard claim" from all streams, dedupe, and verify with the **`fact-checker` skill**