name: buzzsearch version: "1.3.0" description: "Search what people are actually saying across Reddit, X/Twitter, Bluesky, Hacker News, Polymarket, GitHub, YouTube, and the Web. Supply a topic to search, or invoke without one to get today's hot topics." argument-hint: 'buzzsearch AI agents | buzzsearch react vs vue | buzzsearch (no args = hot topics)' allowed-tools: Bash, WebSearch, Read user-invocable: true metadata: emoji: "📡" requires: env: [] optionalEnv: - XAI_API_KEY - BSKY_HANDLE - BSKY_APP_PASSWORD - GITHUB_TOKEN - BRAVE_API_KEY - BRAVE_SEARCH_API_KEY - EXA_API_KEY - SERPER_API_KEY - PARALLEL_API_KEY tags: - research - reddit - x - twitter - youtube - hackernews - polymarket - github - bluesky - trends - social-media - multi-source

buzzsearch - Multi-Source Social Intelligence

Search what people are actually saying across 7 live sources. Supply a topic to search it across all platforms. Invoke without a topic to discover today's hot topics.

Sources (always free, no auth required):

Reddit (public JSON + RSS fallback when search blocked)
Hacker News (Algolia API)
Polymarket (Gamma API)
GitHub (Search API, rate-limited without token)

Sources (auth-optional):

X/Twitter (via xAI API key: XAI_API_KEY)
Bluesky (via app password: BSKY_HANDLE + BSKY_APP_PASSWORD)
YouTube (via yt-dlp if installed) – extracts transcript highlights from the first three results by default
Web (multi-backend: Brave, Exa, Serper, Parallel; auto-detects from BRAVE_API_KEY, EXA_API_KEY, SERPER_API_KEY, or PARALLEL_API_KEY)

STEP 0: DECIDE - TOPIC OR HOT TOPICS

If the user provided a topic (argument text present), set MODE=search and continue to STEP 1.
If no topic was provided, set MODE=hot_topics and jump to STEP 3.

STEP 1: RUN THE SEARCH SCRIPT

Run the Python search script from this skill's scripts directory:

SKILL_DIR="$(dirname "$(realpath "$0")" 2>/dev/null || echo "$HOME/.hermes/skills/buzzsearch")"
# Fallback: resolve from SKILL.md location
if [ ! -f "$SKILL_DIR/scripts/buzzsearch.py" ]; then
  SKILL_DIR="$HOME/.hermes/skills/buzzsearch"
fi
python3 "$SKILL_DIR/scripts/buzzsearch.py" "TOPIC_HERE"

The script prints JSON to stdout. Capture it:

OUTPUT=$(python3 "$SKILL_DIR/scripts/buzzsearch.py" "TOPIC_HERE" 2>/dev/null)

If the script fails or returns empty JSON, fall back to WebSearch for each source manually.

STEP 2: SYNTHESIZE THE OUTPUT

Read the JSON output from the script. It contains items keyed by source. Synthesize into the canonical output format below.

BADGE (MANDATORY, FIRST LINE OF OUTPUT): \n📡 buzzsearch v1.3.0 · synced YYYY-MM-DD\n

For GENERAL topic searches:

📡 buzzsearch v1.3.0 · synced YYYY-MM-DD\n\nWhat I learned:

**Bold headline phrase** - 1-2 sentences about what people are saying, per [@handle](https://x.com/handle) or [r/sub](https://reddit.com/r/sub)

**Bold headline phrase** - 1-2 sentences, per [@handle](https://x.com/handle) or [r/sub](https://reddit.com/r/sub)

**Bold headline phrase** - 1-2 sentences, per [source](url)

KEY PATTERNS from the research:
1. Pattern - per [@handle](https://x.com/handle)
2. Pattern - per [r/sub](https://reddit.com/r/sub)
3. Pattern - per [HN](https://news.ycombinator.com/item?id=N)

---
✅ All agents reported back!
├─ 🟠 Reddit: N threads · M upvotes · K comments
├─ 🔵 X: N posts · M likes · K reposts
├─ 🔴 YouTube: N videos · M views · K/N with transcripts
├─ 🟡 HN: N stories · M points · K comments
├─ 📊 Polymarket: N markets │ odds summary
├─ 🦋 Bluesky: N posts · M likes · K reposts
├─ 🐙 GitHub: N items · M reactions · K comments
└─ 🌐 Web: N pages - source names

I'm now an expert on {TOPIC}. Some things I can help with:
- [Specific follow-up based on most discussed aspect]
- [Specific creative/practical application of what you learned]
- [Deeper dive into a pattern or debate from the research]

I have all the links to the {N} {source list} I pulled from. Just ask.

For COMPARISON queries (topics containing "vs" or "versus"):

📡 buzzsearch v1.3.0 · synced YYYY-MM-DD\n\n# {TOPIC_A} vs {TOPIC_B}: What the Community Says (/BuzzSearch)

## Quick Verdict
One paragraph framing the relationship with scale stats.

## {Entity 1}
**Community Sentiment:** Positive/Mixed/Negative
**Strengths:** bullet points with source attributions
**Weaknesses:** bullet points with source attributions

## {Entity 2}
Same structure

## Head-to-Head
| Dimension | Entity 1 | Entity 2 |
|---|---|---|
| What it is | ... | ... |
| Best for | ... | ... |

## The Bottom Line
**Choose {Entity 1} if** ... **Choose {Entity 2} if** ...

---
✅ All agents reported back!
├─ [footer lines as above]
└─ ...

I've compared {TOPIC_A} vs {TOPIC_B}. Some things you could ask:
- Deep dive into {Entity} alone
- Focus on a specific dimension from the comparison table

STEP 3: HOT TOPICS MODE (no topic provided)

When no topic is supplied, discover what's trending right now. Run:

python3 "$SKILL_DIR/scripts/buzzsearch.py" --hot 2>/dev/null

This queries:

Reddit trending subreddits (www.reddit.com/r/trending.json or popular.json)
HN front page (hn.algolia.com front page)
Polymarket trending markets (gamma-api trending)
GitHub trending repos (github.com/trending)

Synthesize the top 5-8 trending stories across sources using the same output format but with the badge line 📡 buzzsearch v1.1.0 · hot topics · synced YYYY-MM-DD.

VOICE CONTRACT (NON-NEGOTIABLE)

LAW 1 - NO Sources: BLOCK AT THE END. The emoji-tree footer IS the citation block. Do not append a trailing Sources:, References:, or Further reading: section. The output ends at the invitation.

LAW 2 - NO INVENTED TITLE LINE. The badge IS the title. After the badge + blank line, the prose label What I learned: begins the body. No ## Topic - Last 30 Days headers. Comparison queries are the exception (they get # A vs B).

LAW 3 - NO EM-DASHES OR EN-DASHES. Use - (single hyphen with spaces). Em-dashes are the most reliable AI-slop tell.

LAW 4 - NO ## SECTION HEADERS IN BODY. The narrative is bold-lead-in paragraphs + KEY PATTERNS numbered list. Comparison queries get their specific ## headers only.

LAW 5 - ENGINE FOOTER PASS-THROUGH. Include the ✅ All agents reported back! emoji-tree block verbatim between KEY PATTERNS and the invitation.

LAW 6 - NO RAW RANKED EVIDENCE. Transform engine data into prose. Never dump raw JSON tuples or cluster scores.

LAW 7 - BOLD HEADLINE PER PARAGRAPH. Every narrative paragraph starts with **Headline phrase** - .

CITATION PRIORITY:

@handles from X - per [@handle](https://x.com/handle)
r/subreddits - per [r/sub](https://reddit.com/r/sub)
YouTube channels - per [channel](https://youtube.com/@channel) on YouTube
HN discussions - per [HN](https://news.ycombinator.com/item?id=N)
Polymarket - [Polymarket](https://polymarket.com/event/...) at X%
GitHub repos - per [owner/repo](https://github.com/owner/repo)
Web sources - per [Publication](url) (only when social sources don't cover it)

Lead with people, not publications. The user came for the conversation, not the press release.

WHAT THIS SKILL DOES

Searches Reddit (public JSON + RSS fallback when search blocked)
Searches Hacker News (Algolia API)
Searches Polymarket (Gamma API public-search endpoint)
Searches GitHub (Search API, rate-limited without token, better with GITHUB_TOKEN)
Searches X/Twitter (via xAI Live Search API when XAI_API_KEY is set)
Searches Bluesky (via AT Protocol API when BSKY_HANDLE + BSKY_APP_PASSWORD are set)
Searches YouTube (via yt-dlp when installed)
When no topic is given, discovers hot/trending topics
Returns structured JSON that the agent synthesizes into the output format above

WHAT THIS SKILL DOES NOT DO

Does not post, like, or modify content on any platform
Does not access user accounts beyond read-only search
Does not share API keys between providers
Does not require paid services (all free-tier or no-auth sources work immediately)

PRACTICAL CONSIDERATIONS

Source-Specific Notes

Based on real-world usage, here are important notes about each source's behavior:

Reddit: The public JSON search endpoint frequently returns HTTP 403; the skill automatically falls back to RSS feeds when this occurs
Polymarket: Uses the public-search endpoint correctly; avoid older endpoints that may return validation errors
YouTube: Requires yt-dlp binary to be installed (installed via pip3 install yt-dlp)
YouTube transcript extraction: Attempts to fetch subtitles for the first three videos; if unavailable, transcript_highlights will be empty.
GitHub: Functions without authentication but is subject to strict rate limits; setting GITHUB_TOKEN significantly increases limits
X/Twitter: Requires XAI_API_KEY environment variable for xAI Live Search API access
Bluesky: Requires both BSKY_HANDLE and BSKY_APP_PASSWORD environment variables for AT Protocol access
Hacker News: Consistently reliable via Algolia API with no authentication required
Web: Tries Brave, Exa, Serper, and Parallel in order (first available API key wins). Set BRAVE_API_KEY (or BRAVE_SEARCH_API_KEY), EXA_API_KEY, SERPER_API_KEY, or PARALLEL_API_KEY in ~/.hermes/.env.\n - Brave freshness parameter uses format YYYY-MM-DDtoYYYY-MM-DD (works for 30-day lookback).\n - Exa requires EXA_API_KEY; uses /search POST endpoint.\n - Serper uses X-API-KEY header; sends cdr:1,cd_min:...,cd_max:... date filter.\n - Parallel requires bearer token; POSTs to /v1/search.
X Cookie Auth (Camofox): As an alternative to xAI API, the skill can authenticate to X/Twitter via Camofox (a Camoufox-based Firefox browser) to extract live session cookies. The skill provides two ways to perform the login:
1. Inside a Hermes session — run the buzzsearch script directly: python3 ~/.hermes/skills/research/buzzsearch/scripts/buzzsearch.py --x-login. This uses the hermes_tools.browser_* imports which require the Hermes agent process.
2. Standalone via Camofox CLI — run the Camofox browser commands directly (see references/camofox-cli-x-login.md). The CLI is at /root/.hermes/node/bin/camofox-browser and works without Hermes. ⚠️ Important: hermes skill run buzzsearch --x-login is NOT a valid command. hermes skill has no run subcommand. Always invoke the script directly as python3 <script_path> --x-login, or in a Hermes session via delegate_task/cron that runs the script.
In both cases, cookies are stored to cache/x_cookies.json and used by search_x_via_cookies() to call X's internal web search API (/i/api/2/search/adaptive.json) directly. The xAI API is the fallback if cookies expire or are missing.
Hermes tools from within a skill: The --x-login flow imports Hermes browser tools (browser_navigate, browser_type, browser_press, browser_snapshot, browser_console) from hermes_tools using a try/except ImportError pattern. Critical limitation: these imports only work when the skill runs inside a Hermes agent session (e.g., via hermes chat or when the agent invokes the skill). Running the script standalone with python3 buzzsearch.py --x-login will fail with "Hermes tools not available" because hermes_tools is not exposed outside the agent. For standalone use, use the Camofox CLI (see reference).
.env file location: The skill loads environment variables from ~/.hermes/.env (the Hermes root .env). If you set API keys (e.g., BRAVE_SEARCH_API_KEY) in a different .env file, the skill won't see them without adjusting the _load_dotenv(default_path) call.

Reference Documents

This skill ships with reference files that document specific techniques or API details in depth:

references/x-cookie-auth.md — X/Twitter adaptive.json API, bearer token, cookie format, and the complete cookie auth flow.
references/camofox-cli-x-login.md — Standalone X/Twitter login using the Camofox CLI (/root/.hermes/node/bin/camofox-browser) when hermes_tools is unavailable (running outside a Hermes session).
references/hermes-tools-from-skill.md — How to import Hermes agent tools (browser_*, web_*, terminal, etc.) from within a skill script, with guarded import pattern and known limitations.
references/api-quirks-and-workarounds.md — Observed API behaviors, error patterns, and workarounds for each source (Reddit 403s, X cookie auth failures, GitHub 422s, Polymarket empty results, etc.).

Buzzsearch - Recherche multi-source

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