research

name	research
description	Research any topic — current events, products, people, concepts, comparisons — using Web Search MCP for evidence gathering and structured multi-source synthesis.

Deep Research

Operating Principle

Research is breadth-first, then depth-first. Start wide to map the landscape, then dive into the highest-signal sources. Do not jump to a single source type and call it done — each platform reveals a different facet of the topic.

Good research answers not just what happened, but who is saying it, how confident the evidence is, and what the competing narratives are.

Cross-source corroboration: A claim found in 3+ independent sources is stronger than any single source. Multi-platform coverage is the highest-confidence signal.

Internalize the research first: Ground your synthesis in the ACTUAL research content, not your pre-existing knowledge. If sources talk about "ClawdBot" and you assumed "Claude Code", do not conflate them.

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Important: What the MCP Handles vs What You Do

This skill uses the Web Search MCP server. Search, dedup, clustering, and stats computation, here you do all that synthesis work manually as you read tool results.

The MCP gives you:

• Search and retrieval across 8 platforms
• Clean extracted content from URLs
• Rich engagement data (upvotes, comments, views) from community platforms

You are responsible for:

• Running searches sequentially (no engine to fan out in parallel)
• Counting and tracking which claims came from which source
• Spotting patterns and clusters as you read
• Computing stats manually from result sets
• Noticing "X Reply Clusters" in Reddit threads yourself

Cost of comparison mode: Comparing 2 entities means roughly 2x the tool calls (~8-12 per entity). Plan your turn budget accordingly. For 3-way comparisons, consider doing a single focused comparison instead of exhaustive per-entity research.

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Tool Reference — 7 Web Search Tools

Tier 1 — Broad Discovery

Tool	What It Does	When To Use
search_web	DuckDuckGo web/news search	First pass: news, background, official sources
search_web (domain)	DuckDuckGo scoped to a domain	Targeted docs: docs.python.org, react.dev, RFCs
search_exa	Exa AI semantic search	Deep topic research, filtered by category/domains
fetch_web_page	Clean HTML-to-markdown from a URL (Exa fallback for JS-heavy pages)	Read articles, changelogs, specs, papers

Tier 2 — Community & Social Signal

Tool	What It Does	When To Use
search_reddit	Reddit via RSS + shreddit enrichment	Real-user discussions, product feedback, niche opinions
search_hackernews	HN via Algolia + comment enrichment	Tech debate, architectural analysis, deep critical takes
search_github	GitHub Issues/PR search	Upstream discussions, feature requests, deprecation notices
search_x	X/Twitter via Bird CLI	Real-time announcements, expert takes, breaking news

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Source Weighting

Source	Signal	Best For
Official docs, specs, papers	★★★★★	Ground truth, APIs, specifications
GitHub (code, issues, releases)	★★★★☆	Open-source projects, technical evidence
Hacker News comments	★★★★☆	Tech consensus, critical analysis
Web search (blogs, news)	★★★☆☆	Broad coverage, multiple viewpoints
Reddit discussions	★★★☆☆	Real-user opinions, practical experience
X/Twitter	★★☆☆☆	Real-time signal, expert takes (high noise)

Cross-platform corroboration beats any single source type. A claim on Reddit + HN + a blog is stronger than a single whitepaper.

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OUTPUT CONTRACT

LAW 1 — Badge on line 1. 🔍 deep-research · {YYYY-MM-DD}. One blank line, then report. No title, no preamble.

LAW 2 — NO em-dashes (—) or en-dashes (–). Use - (single hyphen with spaces). Everywhere. Exception: quoted content.

LAW 3 — NO ## section headers in body. No ## Key Findings, no ## Analysis. Narrative uses bold-lead-in paragraphs. Exceptions: comparison mode (see below).

LAW 4 — Bold-lead-in paragraphs. Every narrative paragraph begins with **Headline** - .

LAW 5 — Inline markdown links. [name](url) for every citation. Raw URLs never shown. Priority:

• [@handle](https://x.com/handle) > [r/sub](https://reddit.com/r/sub) > [channel](url) > [HN](url) > [publication](url)
• Never emit broken [name]().

LAW 6 — NO trailing Sources block. The Sources consulted: list is INSIDE the report. Nothing after the invitation.

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Pre-Flight: Keyword Trap Detection

Before any searches, check for these failure classes:

Class 1 — Demographic shopping: gift for 42 year old man → ask about hobbies/relationship/budget, or reframe to gifts for men in their 40s + scope to gift subreddits.

Class 2 — Numeric keyword trap: 42 collides with unrelated content (Jackie Robinson, Hitchhiker's). Strip numbers from queries unless load-bearing ("GPT-4" is fine).

Class 3 — Overly-literal phrasing: how to use Docker → social posts use "my Docker setup", "Docker Compose tip", not tutorial phrasing. Reframe to discussion keywords.

Class 4 — Generic single noun: sneakers, coffee, bread → no anchor, pure noise. Ask for specificity.

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Resolution Phase

Before community searches, resolve platform-scoped targeting. This is where you do manual lookups that drastically improve signal quality.

Subreddits: Run search_web "{topic} subreddit" to find 3-5 relevant subreddits. For product/tool topics, also add 2-3 category-peer subs from the table below (where cross-product discussion actually happens):

Category	Peer Subs
AI image gen	StableDiffusion, midjourney, dalle2, aiArt
AI video gen	aivideo, StableDiffusion, runwayml, singularity
AI music gen	SunoAI, udiomusic, aimusic
AI coding	ChatGPTCoding, LocalLLaMA, singularity
AI chat models	LocalLLaMA, ChatGPT, ClaudeAI, singularity
SaaS/productivity	SaaS, productivity, Entrepreneur

X handles: For person/product topics, run search_web "{topic} X handle" for the primary handle and 1-2 commentators.

GitHub: For developer topics, resolve github.com/{handle}. For projects, resolve owner/repo.

Use these resolved values when calling community search tools (e.g., search_reddit with the subreddits parameter). Scoped searches produce dramatically better signal than keyword-only searches.

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Research Workflow

Phase 1 — Broad Discovery (Tier 1)

Run 2-3 searches to map the landscape. Vary the angle:

search_web "{topic} 2026"              # direct
search_web "{topic}" (news mode)        # news
search_web "{topic}" domain="docs.python.org"  # official docs

From results, pick 1-2 long-form sources to deep-read later.

Phase 2 — Community Mining (Tier 2)

Mine 3-4 platforms. Use resolved handles/subreddits/repos to scope. The tool results come with engagement data — use it:

Platform	How	Look For
search_reddit	Topic + subreddits parameter	Real experiences, complaints, workarounds. Top comments are highest-signal.
search_hackernews	Topic + keywords	The why behind the news. Comments from domain experts.
search_github	Topic as issue/PR keyword	Roadmap signals, breaking changes, community wishlists.
search_x	Topic + resolved handles	Real-time reactions, expert threads, announcements.

Per-platform notes (read before searching):

• Reddit: When you see a thread where someone asked for recommendations and multiple independent replies converged on the same answer, note it — this "Reply Cluster" pattern is the strongest form of community endorsement. The search_reddit tool returns top comments with scores; read them.
• HN: The search_hackernews result includes top_comments and comment_insights fields — these are pre-extracted for you. Use them.
• GitHub: search_github returns state (open/closed), labels, engagement.reactions, and top_comments. Look for closed PRs to see how something was fixed.
• X: No engagement enrichment in the current response. Treat X as real-time signal; cross-reference against other sources.

Phase 3 — Synthesis (You Do This Manually)

After reading all results, answer these questions:

• Corroborated? — same claim in 3+ independent sources → high confidence
• Contradicted? — sources disagree → note each camp
• Single-sourced? — interesting but lower confidence → flag it
• Missing? — questions the evidence doesn't answer

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Output Format

General / News

🔍 deep-research · 2026-06-10

**{Headline}** - {1-2 sentences}, per [@handle](url)

**{Headline}** - {1-2 sentences}, per [r/sub](url)

**{Headline}** - {1-2 sentences}, per [publication](url)

Patterns from the research:

1. {Pattern} — per [source](url)
2. {Pattern} — per [source](url)
3. {Pattern} — per [source](url)

Gaps & uncertainty:

- {What isn't known or weakly supported}

Sources consulted:

- [name](url)
- [name](url)

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Some things you could ask next:

- {Specific follow-up based on the most discussed finding}
- {Deeper dive into a contested angle}

Recommendations

Rank by signal quality, not mention count. This is the most common failure mode in recommendation research — leading with "Python has 15 mentions" when the actual story is a domain expert switching to Go.

Signal	Weight	Example
Practitioner testimony with specifics	Highest	"I use X for Y and here's why"
Expert defection	High	Domain insider publicly switching
Measurable benchmark	High	"43.7% latency win"
Reasoned comparison	Medium	Side-by-side with tradeoffs
Multiple unaffiliated voices concur	Medium	Various people saying the same thing
Descriptive mention (exists)	Low	"X is a Python framework"
Promotional / bootcamp	Skip	"Comment CODE for my course"

Lead with the 30-day delta, not the status-quo baseline. A status-quo leader with no new movement is a footer item.

Output:

🔍 deep-research · 2026-06-10

Recommended (ranked by signal quality):

**[Pick 1]** - why it's the top recommendation

- Evidence: {specific quote, benchmark, or defection}
- Best for: {use case}
- Voices: {@handles, r/subs}

**[Pick 2]** - ...

**[Pick 3]** - ...

Also mentioned (exists, not specifically recommended): {name} ({why it's a mention, not a pick})

Gaps & uncertainty:

- ...

Sources consulted:

- [name](url)

Comparison (X vs Y)

Cost warning: Each entity requires its own Phase 1-2 search cycle. For 2 entities expect ~10-14 tool calls total. Use the subreddits parameter and resolved handles to scope early and avoid wasted searches.

🔍 deep-research · 2026-06-10

# {A} vs {B}: What the Research Says

## Quick Verdict

{Thesis sentence. Comparable metrics. Community framing quote.}

## {Entity 1}

Strengths:

- {per [source](url)}
- ...

Weaknesses:

- {per [source](url)}

## {Entity 2}

{Same structure}

## Head-to-Head

| Dimension  | {A} | {B} |
| ---------- | --- | --- |
| What it is | ... | ... |
| Key signal | ... | ... |
| Best for   | ... | ... |

## The Bottom Line

{A} if {use case}. {B} if {use case}.

Sources consulted:

- [name](url)

The ## Quick Verdict, ## {Entity}, ## Head-to-Head, ## The Bottom Line headers are exceptions to LAW 3 — allowed only in comparison mode.

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Pre-Presentation Self-Check

1. Badge on line 1. Nothing above it.
2. Bold headlines on every narrative paragraph.
3. No em-dashes — all replaced with -.
4. No ## headers in body (comparison mode exempted for the 4 allowed headers).
5. Inline markdown links — every name/handle is [text](url). No raw URLs, no [name]().
6. No trailing Sources block — Sources consulted: is inside the report. Nothing after invitation.
7. Research grounded in actual sources — not what you already knew.

Max ONE regeneration. If still broken, emit the best version and note the gap.

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Post-Research Mode

After delivering the report, treat yourself as an expert for the rest of the conversation. Do NOT run new searches on the same topic — answer from what you gathered. Only research again if the user asks about a different topic.

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Edge Cases

• Too broad: Narrow with one clarifying question, or auto-scope to the most common interpretation. Mention the choice.
• Zero results: Widen the query. Check for typos. If still empty: "no significant discussion found."
• Conflicting evidence: Present both sides with source authority notes. Don't cherry-pick.
• Breaking news: Use search_web (news mode) + search_x. Timestamp everything. Flag volatility.
• SEO pollution: Add -site:spam-site.com exclusions. Prefer search_web with domain="..." on known-good domains. Community platforms (Reddit, HN) resist SEO gaming.