Scry

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name: scry version: "1.0.0" description: "Research any topic across 26+ sources: Reddit, X, YouTube, GitHub, HN, Bluesky, ArXiv, Dev.to, Polymarket, and more. The most comprehensive research skill available." argument-hint: 'scry AI agents, scry best Claude Code skills, scry bitcoin ETF --deep' allowed-tools: Bash, Read, Write, WebSearch homepage: https://github.com/khalidsh/scry-skill user-invocable: true

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SCRY v1.0 — Research Any Topic Across 26+ Sources

Search Reddit, X, YouTube, GitHub, Hacker News, Bluesky, Dev.to, ArXiv, Polymarket, Stack Overflow, Product Hunt, Mastodon, Wikipedia, GDELT, CoinGecko, SEC EDGAR, TikTok, Instagram, HuggingFace, Substack, and more. Surface what people are discussing, building, citing, betting on, and debating right now.

CRITICAL: Parse User Intent

Before doing anything, parse the user's input for:

1. TOPIC: What they want to learn about 2. TARGET TOOL (if specified): Where they'll use the prompts 3. QUERY TYPE: - RECOMMENDATIONS — "best X", "top X" → wants a LIST - NEWS — "what's happening with X" → wants current events - PROMPTING — "X prompts" → wants techniques + copy-paste prompts - GENERAL — anything else → wants broad understanding 4. DOMAIN: Auto-detected or user-specified - tech — programming, AI, software, frameworks - science — research, papers, experiments - finance — stocks, earnings, markets - crypto — blockchain, tokens, DeFi - news — politics, geopolitics, events - entertainment — movies, music, gaming, social - general — everything else

Store these variables:

TOPIC = [extracted topic]

TARGET_TOOL = [extracted tool, or "unknown"]

QUERY_TYPE = [RECOMMENDATIONS | NEWS | PROMPTING | GENERAL]

DOMAIN = [auto-detected or user-specified]

DISPLAY your parsing:

I'll research {TOPIC} across 26+ sources to find what's been discussed in the last 30 days.
Parsed intent:
TOPIC = {TOPIC}
DOMAIN = {DOMAIN}
QUERY_TYPE = {QUERY_TYPE}
TARGET_TOOL = {TARGET_TOOL or "unknown"}
Research typically takes 1-3 minutes. Starting now.

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Research Execution

Step 1: Run the SCRY script (FOREGROUND — do NOT background this)

CRITICAL: Run in FOREGROUND with 5-minute timeout. Read the ENTIRE output.

for dir in \
  "." \
  "${CLAUDE_PLUGIN_ROOT:-}" \
  "$HOME/.claude/skills/scry" \
  "$HOME/.agents/skills/scry"; do
  [ -n "$dir" ] && [ -f "$dir/scripts/scry.py" ] && SKILL_ROOT="$dir" && break
done
if [ -z "${SKILL_ROOT:-}" ]; then
  echo "ERROR: Could not find scripts/scry.py" >&2
  exit 1
fi
python3 "${SKILL_ROOT}/scripts/scry.py" "$ARGUMENTS" --emit=compact

Use a timeout of 300000 (5 minutes) on the Bash call.

The script will automatically:

Detect your domain (tech/science/finance/crypto/news/entertainment/general)

Discover available API keys and binaries

Search all available sources in parallel

Score results with domain-aware weights

Deduplicate and cross-link across sources

Detect conflicts between sources

Output a comprehensive research report

Read the ENTIRE output. It contains sections for every source that returned results.

Add --domain=DOMAIN if you detected the domain in intent parsing. Add --deep if the user asked for comprehensive results.

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Step 2: WebSearch Supplement

After the script finishes, do WebSearch to supplement with blogs, tutorials, and news.

Choose queries based on QUERY_TYPE:

RECOMMENDATIONS: best {TOPIC} recommendations, {TOPIC} list examples

NEWS: {TOPIC} news 2026, {TOPIC} announcement update

PROMPTING: {TOPIC} prompts examples 2026, {TOPIC} techniques tips

GENERAL: {TOPIC} 2026, {TOPIC} discussion

Exclude reddit.com, x.com, twitter.com (covered by script).

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Judge Agent: Synthesize All Sources

Ground your synthesis in the ACTUAL research content, not pre-existing knowledge.

1. Weight sources by domain relevance (tech: GitHub/HN/SO highest; science: ArXiv/S2 highest; etc.) 2. Cross-platform signals are strongest — items with [also on: ...] tags are most important 3. Note conflicts between sources (flagged in the output) 4. Extract top 3-5 actionable insights

CITATION RULES

Cite sparingly: 1-2 per topic, 1 per pattern

Priority: @handles > r/subreddits > YouTube channels > GitHub repos > HN > ArXiv > web

Never paste raw URLs — use source names

BAD: "per https://arxiv.org/abs/..." → GOOD: "per ArXiv"

BAD: "per @x, @y, @z" → GOOD: "per @x" (pick strongest)

If QUERY_TYPE = RECOMMENDATIONS

Extract SPECIFIC NAMES — products, tools, repos, people. Count mentions. List by popularity.

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Display Format

FIRST — What I learned:

What I learned:
{Topic 1} — [1-2 sentences, per @handle or r/sub]
{Topic 2} — [1-2 sentences, per @handle or r/sub]
KEY PATTERNS from the research:
1. [Pattern] — per @handle
2. [Pattern] — per r/sub
3. [Pattern] — per GitHub repo

THEN — Stats (copy EXACTLY, replacing placeholders):

The script outputs a stats block — display it as-is. If it doesn't appear, build one:

---
✅ All agents reported back!
├─ 🟡 HN: {N} stories │ {N} points │ {N} comments
├─ 🦞 Lobsters: {N} items │ {N} points
├─ 📝 Dev.to: {N} articles │ {N} reactions
├─ 🐙 GitHub: {N} repos │ {N}★
├─ 🦋 Bluesky: {N} posts │ {N} likes
├─ 🟠 Reddit: {N} threads │ {N} upvotes
├─ 🔵 X: {N} posts │ {N} likes
├─ 🔴 YouTube: {N} videos │ {N} views
├─ 📄 ArXiv: {N} papers │ {N} citations
├─ 📊 Polymarket: {N} markets │ {odds summary}
├─ 🌐 Web: {N} pages — Source, Source, Source
└─ 🗣️ Top voices: @handle1, @handle2 │ r/sub1, r/sub2

Omit any source line that returned 0 results.

LAST — Invitation (adapt to QUERY_TYPE):

Include 2-3 SPECIFIC suggestions based on research findings.

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WAIT FOR USER'S RESPONSE

After showing results, STOP and wait.

WHEN USER RESPONDS

Question → Answer from research (no new searches)

Go deeper → Elaborate using findings

Create something → Write a tailored prompt

Different topic → Run new research

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Agent Mode (--agent flag)

If --agent in ARGUMENTS: 1. Skip intro display 2. Skip AskUserQuestion calls 3. Run research + output report 4. Stop (no follow-up invitation)

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Security & Permissions

What this skill does:

Searches 26+ public APIs and RSS feeds for research data

Runs gh CLI for GitHub search (uses your existing auth)

Runs yt-dlp for YouTube search (public data)

Optionally uses ScrapeCreators API for TikTok/Instagram

Stores cached results in ~/.cache/scry/ (24h TTL)

What this skill does NOT do:

Does not post, like, or modify content on any platform

Does not access private accounts or data

Does not share API keys between providers

Does not write to any external service

Bundled scripts: scripts/scry.py (orchestrator), scripts/lib/ (shared utilities + source modules)