Fan out a research query across three source types — Reddit (practitioner sentiment), local YouTube transcript RAG via yt-rag (curated long-form), and Exa web search (semantic crawl) — then synthesize a distilled brief. Use when the user wants the newest/highest-quality information on a topic — vendor diligence, "what's actually working right now," tooling shootouts, technique comparisons, library evaluations, strategy/idea research, "is X dead," "compare X vs Y." Triggers on "research", "find approaches", "what's working", "scan reddit", "scan youtube", "deep dive", "latest on X", "is X dead", "compare X vs Y", "evaluate X."
Fan out a research query across three source types — Reddit (practitioner sentiment), local YouTube transcript RAG via yt-rag (curated long-form), and Exa web search (semantic crawl) — then synthesize a distilled brief. Use when the user wants the newest/highest-quality information on a topic — vendor diligence, "what's actually working right now," tooling shootouts, technique comparisons, library evaluations, strategy/idea research, "is X dead," "compare X vs Y." Triggers on "research", "find approaches", "what's working", "scan reddit", "scan youtube", "deep dive", "latest on X", "is X dead", "compare X vs Y", "evaluate X."
Run parallel deep searches across Reddit, the local YouTube RAG (yt-rag), and Exa (with native WebSearch as a comparison probe), then synthesize into one brief. Main agent stays lean: subagents do the raw fetches and return distilled bullets plus the concrete specifics worth keeping (see the detail-preservation rule in Step 3). The written brief favors — it is a reusable artifact, not a one-off chat answer (see Step 5).
claude mcp add --transport http exa "https://mcp.exa.ai/mcp?tools=web_search_exa,web_fetch_exa,deep_search_exa,web_search_advanced_exa" --header "x-api-key: $EXA_API_KEY" -s user
apify-reddit (HTTP)
mcp__apify-reddit__macrocosmos--reddit-scraper (wraps the Apify actor macrocosmos/reddit-scraper)
claude mcp add --transport http apify-reddit "https://mcp.apify.com/?tools=macrocosmos/reddit-scraper" --header "Authorization: Bearer $APIFY_TOKEN" -s user
Why not reddit-mcp-buddy? As of May 2026 Reddit edge-blocks all anonymous/unauthenticated access (403 — keys on missing OAuth, not IP), which kills buddy's anonymous mode and live-scrape actors like trudax/reddit-scraper-lite. macrocosmos/reddit-scraper serves pre-harvested data ($0.50/1k results) so it dodges the block. See [[reddit-data-access-2026]] in memory.
If apify-reddit is missing, do not block — run the YouTube + Exa legs and note the gap. Ask the user whether to install before retrying.
Domain overlays
This skill is domain-agnostic by default. If the working directory or topic is finance/trading flavored, read references/trading-lens.md before launching legs and merge its defaults (subreddit list, channel ingest suggestions, Exa angles, invariant check, plus two crypto-only legs: Twitter + Telegram). Detection cues:
mcp__exa__deep_search_exa (with outputSchema — only when you need structured columns, see §"Advanced Exa")
mcp__exa__web_search_advanced_exa (with category/date/domain filters — only when filtering matters)
mcp__apify-reddit__macrocosmos--reddit-scraper (the Apify Reddit actor — the ONLY Reddit tool)
WebSearch — only as the A/B probe described in §"Exa vs WebSearch"
Bash — only to run the global Apify actor catalog (python3 ~/.config/apify/actors.py …) for the crypto-only Twitter/Telegram legs defined in references/trading-lens.md. Never for anything else.
Agent (for subagent fan-out), Write (for the brief file)
Do NOT use WebFetch for content already returned by Exa/Reddit/yt-rag. Do NOT mix in random web tools.
Architecture: three legs, one synthesis
Main agent (lean — never reads raw posts/chunks)
├── Leg A: Reddit subagent ──┐
├── Leg B: YouTube (yt-rag) ──┤ launched IN PARALLEL via Agent
├── Leg C: Exa web ──┘ (+ optional WebSearch A/B probe)
└── Synthesis: merge → brief.md → present to user
Trading lens adds two legs. When references/trading-lens.md is active (crypto/perps/funding topics), legs D Twitter and E Telegram run in the SAME parallel message via the global actor catalog (actors.py) — see that file for templates. They never run in generic (non-crypto) research.
Why subagents? A single Reddit thread or 5 yt-rag chunks can be 5-20k tokens. Three legs at 30+ items each = 100k+ tokens of raw text. Subagents process, dedupe, and return ~30 distilled bullets per leg plus a ### Keep verbatim block of concrete specifics (≤2-3k tokens each). Main stays well under ~20k tokens regardless of query depth — while still receiving the details the final guide needs.
Parallelism: launch all three Agent tool calls in a single message with multiple tool_use blocks. Do not serialize.
Step 1 — Classify and scope the query
Before fan-out, decide three things:
Recency window. "Latest on X" → last 30d. "Is X dead?" → 6mo. "How does X work?" → no recency filter.
Domain lens. Check the overlay cues above. If trading lens fires, load references/trading-lens.md and apply its defaults. Otherwise treat the topic generically.
Depth. Quick scan = top_k=5 per leg, ≤6 Exa results. Deep dive = top_k=15, 20+ Exa results, follow-up web_fetch_exa on the best 3 URLs.
Tell the user the scope in one line before launching: "Scoping: last 30d, {lens}, deep dive — three legs in parallel."
Step 2 — Inventory check + auto-enrich (yt-rag)
Before the YouTube leg runs, call mcp__yt-rag__list_repositoryonce in main context (it's cheap — namespaces + counts, no chunks). Use the output to pick relevant namespaces for the subagent's queries.
If the corpus is THIN for the topic (no namespace fits, or the best matches are clearly off-domain), do NOT silently accept a thin leg — but enrichment is opt-in:
Only run the discover → ingest → search enrichment when the user explicitly asked for a YouTube search / to include YouTube. If the user did not ask for YouTube and the corpus is thin, just note the gap in the synthesis and skip the leg — never auto-ingest unprompted (ingestion is a slow side-effect that mutates the global corpus shared across all repos).
When enrichment IS warranted, run the loop instead of asking for a channel handle:
Discover (Exa). Spawn an Exa subagent to find the highest-signal YouTube videos on the topic — specific watch URLs + @handles, scored 1–5, 2025–2026, rejecting hype/news-roundups/off-topic.
Ingest (curated). Prefer mcp__yt-rag__ingest_videos(urls, namespace) into a themed namespace yt_<topic_slug> — cherry-picking on-topic videos across creators beats ingest_channel's 30 mostly-off-topic uploads. Use ingest_channel / ingest_channels only when a whole creator is on-topic. Surface the candidate list, but once the user has asked for YouTube a curated ingest needs no per-video sign-off.
Search. Run Leg B (Step 3) scoped to the new namespace.
Compounding: the corpus is global, so an enrichment ingest benefits every future run. The yt-rag MCP server has no web/Exa access, so the discovery half MUST live here in the skill — the server only exposes ingest_videos / ingest_channel(s).
Step 3 — Launch the three legs in parallel
Use one message with three Agent tool calls. Templates below. Each subagent must return a structured markdown block, not raw text.
Detail-preservation rule (applies to ALL legs — overrides any "Return ONLY" / "no raw quotes" line in the templates). The word caps in the templates keep the ranked-findings bullets lean — they are NOT a license to discard substance. In addition to its template sections, every leg must append a ### Keep verbatim block holding the concrete specifics worth carrying into the final guide: exact example snippets, exact numbers/figures, named tools/formats/fields, precise phrasings, short high-signal quotes — each with its source URL/creator/subreddit. This block does not count toward the word cap. Rule of thumb: the synthesizer can always cut a detail, but it can never recover one a leg never returned — so when in doubt, preserve the specific over the summary.
Leg A — Reddit subagent prompt template
You are the Reddit leg of an multi-source-research fan-out. Topic: "{topic}".
Recency window: last {N} days. Domain lens: {lens or "generic"}.
Use ONLY the `mcp__apify-reddit__macrocosmos--reddit-scraper` tool. It returns
POSTS (title, score, url, body/selftext) — there is NO comment-tree tool, so
work from titles + bodies + scores. Input shape:
{ "subreddits": ["sub1","sub2"], // names without r/; omit for site-wide
"keyword": "{search terms}", // optional; omit to just browse the subs
"limit": 15, // posts PER CALL (cost ≈ limit × $0.0005)
"sort": "top" } // "top" | "hot" | "new" — use "top" for recency scans
Make 2 calls in parallel (one message):
1. Subreddit scan — subreddits = 3-8 most relevant subs (one broad catch-all
like r/{broad-sub-for-topic} + 2-5 niche), keyword="{topic}", sort="top", limit=15.
{if lens overlay provided a subreddit list, use it instead of picking}
2. Site-wide scan — omit `subreddits`, keyword="{topic}", sort="top", limit=10
(catches relevant posts in subs you didn't list).
For each post returned:
- Read title + body; weight by score.
- For a high-signal post needing more depth, make ONE follow-up call with
`{ "url": "{post url}" }` to pull full content. Do this sparingly (cost).
- Flag: practitioner reports, broken-edge confessions, contrarian dissent,
vendor warnings, "what's actually working" posts
- Score 1-5: 5 = first-hand result with specifics; 1 = noise/recycled news
Return this markdown — no preamble (short high-signal verbatim snippets belong in the Keep-verbatim block):
### Reddit findings ({N} threads scanned)
- **[score/5]** {one-line thesis in ≤20 words} — r/{sub}, {date} — {url}
- ...
### Sentiment summary
{2-3 sentences on net tilt: bullish/bearish/mixed/contested, with named camps}
### Surprising contradictions
{bullet anything where high-karma comments contradict the OP or each other}
### Keep verbatim
{concrete specifics worth carrying into the final guide — exact techniques, numbers, named tools/formats, short quotes — each with its post url. Does NOT count toward the word cap.}
Cap the bullet sections at ~400 words (Keep-verbatim is uncapped). If a subreddit returns nothing, list it under "### Empty".
Leg B — YouTube (yt-rag) subagent prompt template
You are the YouTube leg of an multi-source-research fan-out. Topic: "{topic}".
Use ONLY mcp__yt-rag__search. Namespaces in scope: {namespace_list_from_step_2}
(omit `namespace` to search the full corpus when no scope fits).
Run 3-5 search() calls in parallel, varying query phrasing:
- Literal: "{topic}"
- Semantic rewrite: "{rewrite that names the mechanism, not the buzzword}"
- Contrarian: "why {topic} doesn't work" / "{topic} broken" / "{topic} dead"
- Adjacent: pick one nearby concept (e.g. for "funding rate MR" → "basis trade")
Use top_k=10 per call.
For each chunk returned:
- Read the prefix [Channel — "Title" — date — MM:SS] and the content
- Score 1-5: 5 = creator gives concrete numbers/code/timing; 1 = vibes only
- Dedupe by video_id (keep highest-scoring chunk per video)
Return this markdown:
### YouTube findings (top {N} of {M} chunks)
- **[score/5]** {one-line takeaway in ≤25 words} — {Channel} — {date} — {source_url}
- ...
### Cross-creator consensus
{2-3 bullets — claims made by ≥2 independent creators}
### Lone-wolf claims
{bullets — interesting claims made by exactly one creator, worth checking}
### Keep verbatim
{concrete techniques/steps/numbers a creator actually demonstrates — name the channel + keep the MM:SS deep-link. Does NOT count toward the word cap.}
Cap the bullet sections at ~400 words (Keep-verbatim is uncapped). If a query returns nothing, suggest one channel to ingest.
Leg C — Exa subagent prompt template
You are the Exa leg of an multi-source-research fan-out. Topic: "{topic}".
Use ONLY mcp__exa__web_search_exa and mcp__exa__web_fetch_exa.
Phase 1 — broad search (one call):
web_search_exa(query="{describe the IDEAL page, not keywords}", numResults={10|20})
Exa works best with descriptive page-shape queries:
GOOD: "technical blog post analyzing funding rate mean-reversion in crypto perps with backtest code"
BAD: "funding rate strategy crypto"
Phase 2 — targeted searches (run in parallel, 2-4 calls):
- One academic/paper angle: "research paper on {mechanism}"
- One github angle: "github repo implementing {topic}" or "code example {topic}"
- One substack/blog angle: "{topic} substack analysis 2025"
- {if lens overlay specifies additional angles, append them here}
Phase 3 — fetch the best 1-3 URLs with web_fetch_exa(maxCharacters=4000)
Only fetch if the search highlight is insufficient. Skip paywalled / login-walled.
Return this markdown:
### Exa findings ({N} pages reviewed)
- **[score/5]** {one-line thesis ≤20 words} — {domain} — {date if known} — {url}
- ...
### Highest-signal source
{1 paragraph on the single best page found, with the key claim and any numbers}
### Gaps
{what the search did NOT surface — code? recent data? institutional view?}
### Keep verbatim
{concrete techniques + example snippets (prompt fragments, config, code, exact structures/figures) extracted from the pages — each with its url. Preserve specifics rather than paraphrasing them away. Does NOT count toward the word cap.}
Cap the bullet sections at ~400 words (Keep-verbatim is uncapped).
Step 4 — Optional: Exa vs WebSearch A/B probe
When the user is evaluating Exa (default until they say otherwise), spawn a fourth small subagent that runs the same primary query through native WebSearch and returns the top 5 results in the same format. Then in the synthesis section, compare:
Overlap: how many URLs appear in both Exa and WebSearch top-10
Unique-to-Exa: URLs Exa found that WebSearch missed (and vice versa)
Quality tilt: which set leans more toward primary sources / code / data vs SEO blogspam
Skip the probe on deep dives (too much overhead) or when the user has already decided to commit to Exa.
Step 5 — Synthesize
Main agent receives 3-4 distilled blocks (findings bullets + each leg's ### Keep verbatim block). Do not read raw chunks again. You produce two artifacts at different altitudes:
Chat reply = concise. Print the resolved file path, a tight TL;DR (3-5 bullets), and the 2-3 most decision-relevant findings. This is the only place brevity is the goal.
Written file = comprehensive. Completeness over brevity. This file gets reused later, so do NOT strip detail to look clean or to seem efficient. Fold in every crucial, novel, or worth-keeping detail the legs surfaced — especially everything in the ### Keep verbatim blocks: concrete techniques, exact numbers, named tools/formats, example snippets, edge cases, contradictions. Give each claim an inline source reference — the [R]/[Y]/[E] tag and the specific creator/site/subreddit (and URL/timestamp where available) — so any reader can trace it. Cut only true noise and duplication, never substance. When unsure whether a detail earns its place, keep it (tagged) rather than cut it.
Default file template — these sections are minimums, not caps; expand any section as far as the material warrants:
# {topic} — research brief_scope: {recency}, {lens}, {depth} • generated {date}__source tags: [R]=Reddit • [Y]=YouTube (yt-rag) • [E]=Exa web. Inline citations name the specific creator/site so every claim is traceable._## TL;DR
{3-5 bullets — highest-confidence claims that survived ≥2 sources}
## Key findings (in depth)
{The substance — one subsection per major theme. Under each, ALL the concrete detail for that
theme: techniques, mechanisms, exact numbers, named tools/formats, example snippets, caveats —
each with an inline source ref. Prose or bullets, whatever carries the detail best. No length cap.}
## What's working (claimed)
{bullets, each tagged + named source}
## What's broken / contested
{bullets — broken edges, vendor warnings, cross-source contradictions, each sourced}
## Numbers worth verifying
{every specific number — perf, cost, throughput, token counts, %, dates — with where it came from}
## Next moves- One concrete experiment or follow-up action
- One follow-up search to run if needed
- {if lens overlay specifies an invariant check, include its bullets here}
## Sources### Reddit- {bullets with urls}
### YouTube- {bullets with deep-link urls — keep the MM:SS timestamps}
### Exa- {bullets with urls}
## Method notes- Legs run: {A/B/C, ± A/B probe} • Empty legs: {any}
- {if A/B probe ran} Exa vs WebSearch: {overlap %, tilt}
Guide / playbook / reference mode. When the user asks for a "guide", "design guide", "playbook", "reference", "best practices", "how-to", or anything meant to be read and reused later (not a one-off answer), upgrade the file beyond the template above:
Add a "Ready-to-paste examples / worked scaffolds" section: concrete, copyable artifacts (prompt snippets, configs, code, templates) reconstructed from the legs' findings — each block annotated with the exact source it came from.
Add a "Practice → source quick-reference" table with columns Practice | Why it works | Source | Leg, one row per actionable recommendation, so a future reader can trace every practice to its origin.
Preserve the example snippets from the ### Keep verbatim blocks verbatim — don't paraphrase the specifics away.
Add a short "how to read this" note up top, and state honestly where claims are practitioner-experience vs. benchmarked, so readers know what to trust.
Output path. If the current working directory is inside a git repo, write to research/{slug}-{YYYY-MM-DD}.md (relative to repo root — git rev-parse --show-toplevel). Create research/ if missing. Otherwise fall back to ~/research/{slug}-{YYYY-MM-DD}.md. Print the resolved path and the TL;DR in the chat reply.
Source-strength cheat sheet
Use this when deciding how much to weight a claim:
conversational opinion, very recent reddit/twitter
describe the ideal page, not keywords
WebSearch
breaking news, mainstream coverage, official docs
depth, niche sources
use mostly as A/B probe vs Exa
Advanced Exa — when to escalate beyond web_search_exa
Default to web_search_exa + web_fetch_exa. Escalate only when the query shape demands it:
Use web_search_advanced_exa when you need filters that the basic search can't express:
Date-bounded: startPublishedDate: "2025-10-01" for "what's been published since {event}"
Category-bounded: category: "financial report" for SEC filings / earnings; also accepts research paper, github, news, pdf, personal site, linkedin profile, company
Domain-locked: includeDomains: ["github.com"] or includeDomains: ["substack.com"] for source isolation
Required-text: includeText: ["funding rate"] (single-item arrays only — multi-item 400s)
Constraint: excludeText is NOT supported on the financial report category specifically
Use deep_search_exa when you need structured columns extracted across many sources (the lead-gen pattern). Required params on every call: structuredOutput: true, numResults: 50, highlightMaxCharacters: 1, type: "deep". Schema caps: ≤10 properties total across all nesting; array items must be flat primitives only. Every string field in the schema MUST carry a word/char limit in its description (e.g. "in 12 words or less") — otherwise responses bloat. Always spawn a subagent for deep_search_exa — one call returns 30-50 rows (5-15k tokens) and you don't want that in main context.
Don't reach for these tools for ordinary "find me an article about X" queries — basic web_search_exa is faster and cheaper.
Performance & limits
A standard run = 3 legs in parallel, ~30-60s wall clock.
Each leg subagent stays under 8 tool calls. If a leg needs more, it's drifting — kill and re-scope.
Reddit (Apify macrocosmos) is pay-per-result (~$0.0005/post). One call fans across ALL listed subreddits, so keep subreddits ≤8 and limit ≤15 per call — 2 calls per leg is plenty. (Free Apify tier ≈ 10k posts/mo.)
yt-rag ingest_channel is slow (30s-2min for 30 videos). Never call it inside a fan-out — only on explicit user OK. Remember: the corpus is global, so an ingest in one repo benefits every future run.
web_fetch_exa costs more than web_search_exa. Cap at 3 fetches per leg.
Common failure modes
Echo chamber. All three legs cite the same Twitter thread. Down-score and note in synthesis.
Stale yt-rag. If published_at on every chunk is >3mo old for a fast-moving topic, recommend ingest before trusting.
Vendor astroturf. Reddit posts shilling a paid product. Score 1, flag in "What's broken / contested".
Exa hallucination via summary. If web_search_exa returns a highlight that sounds too clean, fetch the URL with web_fetch_exa and verify before quoting.
Empty corpus. If yt-rag returns zero hits and no namespace matches, don't fake it — say so and skip the leg.
