research: Deep Research Any Topic

Research ANY topic across Reddit, X, community forums, official docs, academic papers, and industry publications. Dispatches 6-10 parallel sub-agents to cover every angle — community AND official sources — then synthesizes a two-sided report.

Use cases:

• Prompting: "photorealistic people in Nano Banana Pro" — learn techniques from community tips AND official guides
• Recommendations: "best Claude Code skills" — get specific names from community + official feature comparisons
• News: "what's happening with OpenAI" — community reactions + official announcements
• General: any topic — understand what the community says AND what officials report

Parse User Intent

User input: $ARGUMENTS

Extract four variables from user input before proceeding:

TOPIC

TARGET_TOOL

QUERY_TYPE

DEPTH

--quick

--deep

See

references/intent_parsing.md

Do not ask about target tool before research. If unspecified, ask after showing results.

Setup Check

The Python script works in three modes based on available API keys:

1. Full Mode (both keys): Reddit + X with real engagement metrics
2. Partial Mode (one key): Reddit-only or X-only
3. Web-Only Mode (no keys): Script provides no data, sub-agents do all the work

API keys are optional. The skill always dispatches sub-agents regardless. Determine mode quickly.

MCP Tool Detection

Discover available search MCP tools before dispatching agents. Run

ToolSearch

AVAILABLE_MCP_TOOLS

"searxng"

searxng_web_search

web_url_read

"brave search"

brave_web_search

brave_news_search

brave_local_search

brave_image_search

brave_video_search

"exa"

exa_search

exa_find_similar

exa_get_contents

"tavily"

tavily_search

tavily_extract

"firecrawl"

firecrawl_search

firecrawl_scrape

firecrawl_crawl

firecrawl_map

"perplexity"

perplexity_ask

"jina"

jina_search

jina_read_url

"kagi"

kagi_search

kagi_summarize

"serper"

serper_google_search

serper_news_search

"google search"

google_web_search

Run all ToolSearch calls in a single parallel message. Any tool that appears in the results is loaded and available.

Construct the

MCP_TOOLS

• MCP tools found: List discovered tools by name. Instruct agents to use them as PRIMARY search tools, falling back to harness tools only on errors or empty results.
• No MCP tools found: Instruct agents to use harness-provided tools in this order:

  1. WebSearch

     /

     WebFetch

     (Claude Code built-in)

  2. antigravity_search

     / Antigravity search tools (if available)

  3. codex_search

     / Codex search tools (if available)

Phase 1: Run Python Script (Reddit + X)

Run the research script synchronously — it provides Reddit/X data with real engagement metrics that sub-agents cannot replicate.

RESEARCH_SCRIPT="$([ -f .claude/skills/research/scripts/research.py ] && echo .claude/skills/research/scripts/research.py || echo ~/.claude/skills/research/scripts/research.py)" && python3 "$RESEARCH_SCRIPT" "$TOPIC" --emit=compact 2>&1

The

$DEPTH

--quick

--quick

--deep

--deep

Store the output as

SCRIPT_DATA

• "Mode: both" / "Mode: reddit-only" / "Mode: x-only": Script found data
• "Mode: web-only": No API keys, sub-agents provide all data

Do not stop or warn if web-only. Proceed to Phase 2.

Phase 2: Sub-Agent Dispatch

Dispatch the exact agent count below. Each agent targets its specific focus area -- do not reduce the count or use generic agents.

Step 1: Read Prompt Templates

Use the Read tool to read

references/subagent_prompts.md

Step 2: Agent Roles

Agent count by depth:

--quick

--deep

Community agents (exclude reddit.com and x.com from all searches):

Official agents:

Step 3: Dispatch

For each agent, fill the matching template from

references/subagent_prompts.md

{TOPIC}

{QUERY_TYPE}

{FOCUS}

{QUERIES}

references/subagent_prompts.md

{DATE_FROM}

{MCP_TOOLS}

Every Task call uses

subagent_type: "general-purpose"

"C1: HN + tech forums"

"O2: industry pubs"

Dispatch ALL agents in a SINGLE message with parallel Task calls. Do not dispatch sequentially.

Phase 3: Collect Results

After dispatching, collect results from all agents:

1. Call

   TaskOutput

   for each dispatched agent
2. Organize into:

   COMMUNITY_FINDINGS

   (C1-C5) and

   OFFICIAL_FINDINGS

   (O1-O5)
3. Graceful failure: If an agent fails or returns empty, log which agent failed, continue with remaining results, note the gap in the final report. Do not retry.

Phase 4: Judge Synthesis

Synthesize all findings (SCRIPT_DATA + COMMUNITY_FINDINGS + OFFICIAL_FINDINGS) into a coherent two-sided report.

Weighting

Cross-Reference Analysis

Identify 3-5 topics where community and official sources can be compared:

• Aligned: Both sides agree
• Divergent: Community says one thing, officials say another
• Gap: One side has information the other lacks

Internalize the Research

Ground your synthesis in actual research content, not pre-existing knowledge. Read all agent outputs carefully, paying attention to exact names, specific insights, and real engagement numbers.

If QUERY_TYPE = RECOMMENDATIONS

Extract specific names from all sources (script + community + official agents). Count mentions across sources, note which sources recommend each, list by popularity.

For All Query Types

From the actual research output, identify:

• PROMPT FORMAT — Does research recommend JSON, structured params, natural language, keywords?
• Top 3-5 patterns/techniques that appeared across multiple sources
• Specific keywords, structures, or approaches mentioned by the sources
• Common pitfalls mentioned by the sources

If research says "use JSON prompts" or "structured prompts", deliver prompts in that format later.

Self-check: Re-read your synthesis before displaying. If it does not match what the research actually says, rewrite it.

Display Two-Sided Report

Refer to

references/output_format.md

1. What the Community Says

Synthesize SCRIPT_DATA (Reddit/X) + COMMUNITY_FINDINGS (C1-C5):

Most Mentioned:

1. [Specific name] - mentioned {n}x (r/sub, HN, @handle, blog.com)
2. [Specific name] - mentioned {n}x (sources)
3. [Specific name] - mentioned {n}x (sources)

Notable mentions: [other specific things with 1-2 mentions]

If PROMPTING/NEWS/GENERAL:

What the community is saying:

[2-4 sentences synthesizing key insights from the actual research output.]

Key patterns:

1. [Pattern from research]
2. [Pattern from research]
3. [Pattern from research] </example>

2. What the Official Sources Say

Synthesize OFFICIAL_FINDINGS (O1-O5):

• Key findings with authority attribution
• Recent official changes with dates
• Gaps in official coverage

3. Where They Agree and Disagree

Cross-reference table (3-5 rows):

4. Stats Footer

Display real numbers from the research:

All agents reported back!
|- Reddit: {n} threads | {upvotes} upvotes | {comments} comments
|- X: {n} posts | {likes} likes | {reposts} reposts
|- Community web: {n} sources (HN, forums, blogs)
|- Official web: {n} sources (docs, papers, reports)
|- Agents dispatched: {total} ({C}C + {O}O)
|- Cross-reference: {agree} aligned, {disagree} divergent, {gaps} gaps

If web-only mode, omit Reddit/X lines and add the API key hint from

references/output_format.md

5. Invitation

Share your vision for what you want to create and I'll write a thoughtful prompt
you can copy-paste directly into {TARGET_TOOL}.

Use real numbers from the research output. Patterns should be actual insights, not generic advice.

If TARGET_TOOL is still unknown after showing results, ask now:

What tool will you use these prompts with?

Options:
1. [Most relevant tool based on research]
2. Nano Banana Pro (image generation)
3. ChatGPT / Claude (text/code)
4. Other (tell me)

After displaying the report and invitation, wait for the user to respond.

Prompt Generation

When the user shares their vision, write ONE tailored prompt using expertise from BOTH community and official sources.

Match the format the research recommends (JSON, structured params, natural language, keywords).

See

references/prompt_generation.md

Context Memory

After research completes, retain topic expertise for follow-up questions. See

references/context_memory.md