newsroom-investigate

name	newsroom-investigate
description	Research community signal across Reddit, X, YouTube, and the web with @side-quest/word-on-the-street, then fact-check claims against official sources. Use for community sentiment, recommendations, buzz, X vs Y, has anyone tried X, or what people are saying. Not for codebase exploration, generic web lookup, summarization, code review, or implementation research.
role	tool-workflow
argument-hint	"[topic(s)] [--topic "..."] [--quick|--deep] [--days N] [--sources reddit|x|both] [--no-fact-check]"
allowed-tools	Read, Agent, Bash, WebSearch, WebFetch

Newsroom Investigate

Research orchestrator that dispatches parallel agents to gather community intelligence and verify claims against official sources.

Preflight

Before dispatching any agents, verify the toolchain. Run these checks and stop with a clear error if any fail:

1. wots CLI: bunx --bun @side-quest/word-on-the-street --version

   • Fail: "The @side-quest/word-on-the-street CLI is not installed. Install with: bun add -g @side-quest/word-on-the-street"

2. yt-dlp: which yt-dlp

   • Fail: "yt-dlp is not installed (required for YouTube results). Install with: brew install yt-dlp"

3. API keys: Check key presence without printing values. Check environment variables first (test -n "${OPENAI_API_KEY:-}" and test -n "${XAI_API_KEY:-}"), then fall back to key names in ~/.config/wots/.env. At least one key must be available from either source.

   • Never echo, print, log, or paste API key values.
   • Fail: "No API keys found in environment or ~/.config/wots/.env. Set OPENAI_API_KEY and/or XAI_API_KEY in your shell exports or create ~/.config/wots/.env"

4. Context7 MCP (for fact-checking): Verify resolve-library-id tool is available by checking your available MCP tools

   • Fail (soft): "Context7 MCP not available — fact-checking will use WebSearch only (no library doc verification)"

Parse Arguments

Parse $ARGUMENTS to extract:

• TOPICS: Positional arguments (comma-split) or --topic "..." flags (repeatable). --topic takes precedence.
• DEPTH: --quick or --deep. Default: standard.
• SOURCES: --sources reddit|x|both. Default: auto (CLI decides based on available keys).
• DAYS: --days N (1-365). Default: 30.
• FACT_CHECK: Enabled by default. Disable with --no-fact-check.
• QUERY_TYPE per topic: Auto-detect from the topic text:
  • RECOMMENDATIONS: "best X", "top X", "recommended X" (wants a list of specific things)
  • NEWS: "what's happening with X", "X news", "latest on X"
  • GENERAL: everything else

If no topic is provided, ask the user for one. Once you have topic(s), print a brief summary and dispatch immediately:

Researching {topics} — {depth}, {sources}, {days} days{", no fact-check" if disabled}

No confirmation gate. Just go.

Broad topic detection: If a topic is a single generic word (e.g., "AI", "cloud", "programming", "security"), warn the user before dispatching: "That's a very broad topic — '{topic}' could return thousands of results. Want to narrow it down, or go as-is?" If they say go, proceed.

Security topic auto-detection: If any topic contains "security", "CVE", "vulnerability", or "advisory", auto-escalate the fact-check claim count from 3 to 5 and prioritize CVE/advisory claims.

Reject more than 5 topics: "Too many topics — cap at 5 or combine related ones."

Normalize Topics

Before dispatching, normalize each topic to improve CLI search precision. The CLI's topic string drives Reddit/X search prompts, YouTube queries, and web search — small phrasing changes have outsized impact on result quality.

Rules (check query type first, then trim):

1. Check query-type exceptions FIRST — these override filler removal in step 2:
   • RECOMMENDATIONS: preserve "best/top" if the user explicitly included them (e.g., "best React frameworks" stays)
   • NEWS: will append "release" or "announcement" in step 3
   • GENERAL: no exceptions
2. Trim to 3-6 core tokens — remove filler words NOT protected by step 1: "latest", "new", "guide", "tips", "what's", "what are the", "tell me about". Keep proper nouns and product/version identifiers.
3. Preserve disambiguators — keep tokens like: v2, 2.1.49, 2026, security, release, CVE
4. Apply query-type additions:
   • NEWS: append "release" or "announcement" only if the topic doesn't already contain a news-intent word (updates, news, changes, announcement, release, launch). If it does, the topic already signals recency — don't double up.
5. Shorten if > 8 tokens — keep: first 2 proper nouns, 1 version/date token, 1 intent token (release/security) if present

Examples:

Raw Topic	Query Type	Normalized
"what's new in Claude Code 2.1.49"	NEWS	"Claude Code 2.1.49 release"
"best TypeScript frameworks 2026"	RECOMMENDATIONS	"best TypeScript frameworks 2026"
"tips for prompt engineering with Claude"	GENERAL	"prompt engineering Claude"
"Rust memory safety discussion"	GENERAL	"Rust memory safety"

Pass the normalized topic to the Beat Reporter. If normalized, keep the original as raw_topic in the assignment for transparency.

Dispatch Beat Reporters

Read references/beat-reporter.md for the agent instructions.

For each topic, dispatch ONE beat-reporter Agent in the same message (parallel execution). Use model: "sonnet" for cost efficiency.

Build a structured JSON assignment for each reporter:

Agent({
  description: "Beat Reporter: [topic]",
  model: "sonnet",
  prompt: `Read skills/newsroom-investigate/references/beat-reporter.md and follow its instructions to execute this assignment.

{
  "topic": "[normalized topic]",
  "raw_topic": "[original topic, only if normalized]",
  "query_type": "RECOMMENDATIONS|NEWS|GENERAL",
  "cli_flags": "[--quick|--deep] [--sources=X] [--days=N]",
  "depth": "quick|standard|deep"
}`
})

Map flags to CLI flags:

• --quick → --quick
• --deep → --deep --strategy=two-phase --phase2-budget=5
• --sources reddit → --sources=reddit
• --sources x → --sources=x
• --sources both → --sources=both
• --days N → --days=N

Always include: --json --quiet --include-web --include-youtube

Collect Results

Wait for all reporter agents to return. Handle partial failures gracefully:

Scenario	Action
All reporters succeed	Full synthesis
Some reporters succeed	Synthesize available results, note which topics had gaps
CLI failed but web results exist	Report web findings, note "engagement data unavailable"
Reporter times out	Note gap, continue with others
All reporters fail	Report failure honestly, suggest checking API keys or retrying

Fact-Check Pass

Skip this step if --no-fact-check was passed.

Scan all reporter results for verifiable factual claims — bug reports, version/release claims, feature announcements, deprecation notices, performance claims, security issues.

Claim extraction rules:

• Default: extract top 3 claims
• NEWS query type: extract top 5 claims
• Security-related topic (detected earlier): extract top 5 claims, prioritize CVEs and advisories
• Prioritize claims that appear in multiple sources or have high engagement
• Focus on: version-specific claims, "official" statements, benchmark numbers, bug reports with no linked issue

Read references/fact-checker.md for the agent instructions.

Dispatch a single fact-checker Agent:

Agent({
  description: "Fact Checker",
  model: "sonnet",
  prompt: `Read skills/newsroom-investigate/references/fact-checker.md and follow its instructions.

{
  "claims": [
    {
      "id": 1,
      "assertion": "the specific claim",
      "source": "where it came from (e.g., r/ClaudeAI, 342 upvotes)",
      "category": "bug|release|security|pricing|performance|feature"
    }
  ],
  "topic": "[research topic for context]"
}`
})

Synthesize Output

Ground the synthesis in actual research data, not pre-trained knowledge.

Synthesis priority:

1. Engagement-ranked CLI data (Reddit, X, YouTube) is the strongest signal — these have verified upvotes, likes, comments
2. Web results are supplementary — no engagement verification, useful for official sources and articles
3. Cross-source patterns are gold — if something appears in both CLI and web results, it's a strong signal
4. Deduplicate — same story across sources? Merge and keep highest engagement numbers
5. Extract top 3-5 actionable findings per topic

For RECOMMENDATIONS queries:

# Research: [topic]

## What the community recommends

1. **[Specific name]** — mentioned {n}x ({sources with engagement})
2. **[Specific name]** — mentioned {n}x ({sources})
3. **[Specific name]** — mentioned {n}x ({sources})

Notable mentions: [others with 1-2 mentions]

## Key patterns
- [Pattern from research]
- [Pattern from research]

For NEWS queries:

# Research: [topic]

## Latest developments

1. **[Headline]** ({date}) — [1-2 sentence summary with attribution]
2. **[Headline]** ({date}) — [1-2 sentence summary]

## Emerging trends
- [Trend from research]

For GENERAL queries:

# Research: [topic]

## Key findings
- [Finding grounded in actual data]
- [Finding with source attribution]

## Community consensus
[What most sources agree on]

## Points of debate
[Where opinions split, with engagement numbers]

All queries get these sections:

---
**Sources** ({n} links)

Reddit:
- [title](url) ({score} pts, {comments} comments) — r/{subreddit}

X:
- [text](url) ({likes} likes) — @{handle}

YouTube:
- [title](url) ({views} views, {likes} likes) — {channel}

Web:
- [title](url) — {domain}
---

**Stats**
- Reddit: {n} posts | {total_upvotes} upvotes
- X: {n} posts | {total_likes} likes
- YouTube: {n} videos | {total_views} views
- Web: {n} pages

Verification section (only if fact-check ran):

## Verification ({n} claims checked)
- {verdict_emoji} **{claim}** — {evidence} ({source_url})

Verdict emojis: verified, contradicted, unverified

After Publishing

You are an expert on the topics covered for the rest of the conversation. Answer follow-ups from your research — do not re-search unless the user asks about a different topic.

Offer follow-up options:

• "Dig deeper" — re-run with --deep on a specific angle
• "New topic" — start fresh
• "Compare" — head-to-head on two items from the results (if RECOMMENDATIONS)