Tencent SkillHub · Content Creation

Briefing Room

Daily news briefing generator — produces a conversational radio-host-style audio briefing + DOCX document covering weather, X/Twitter trends, web trends, world news, politics, tech, local news, sports, markets, and crypto. macOS only (uses Apple TTS and afplay). Use when user asks for a news briefing, morning briefing, daily update, or similar.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: CHANGELOG.md, README.md, SKILL.md, scripts/briefing.sh, scripts/config.py

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.0.3

Provenance

Publisher: matusvojtek
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 24 sections Open source page

Briefing Room 📻

Your personal daily news briefing — audio + document. On demand, research and compose a comprehensive ~10 minute news briefing in a conversational radio-host style. Output: audio file (MP3) + formatted document (DOCX).

💸 100% Free

No subscriptions, API keys, or paid services Uses free public APIs (Open-Meteo weather, Coinbase prices, Google Trends RSS), web search, and local TTS TTS is fully local, no keys needed: MLX-Audio Kokoro (English) or Apple say (any language) Reads/writes: ~/.briefing-room/config.json (settings) and ~/Documents/Briefing Room/ (output)

First-Run Setup

On first use, check if ~/.briefing-room/config.json exists. If not, run: python3 SKILL_DIR/scripts/config.py init This creates default config. The user can customize: Location — city, latitude, longitude, timezone (for weather) Language — en, sk, de, etc. Voices — per-language TTS engine and voice selection Sections — which news sections to include Output folder — where briefings are saved Show setup status: python3 SKILL_DIR/scripts/config.py status

Quick Start

When user asks for a briefing (e.g. "give me a briefing", "morning update", "what's happening today"): Check config exists (run setup if not) Play notification sound: afplay /System/Library/Sounds/Blow.aiff & Spawn a sub-agent with the full pipeline task immediately Reply: "📻 Briefing Room is firing up — gathering today's news. I'll ping you when it's ready!" DO NOT BLOCK — spawn and move on instantly Language override: If user says "po slovensky", "v slovenčine", "auf deutsch", "en français", etc. → pass that to the sub-agent. Otherwise use the configured default language. Any language macOS supports will work — the agent writes the script in that language and TTS auto-detects a matching voice.

Spawn Command

sessions_spawn( task="<full pipeline instructions — see below>", label="briefing-room", runTimeoutSeconds=600, cleanup="delete" ) The task message should include ALL the pipeline steps below so the sub-agent is fully self-contained. Replace all SKILL_DIR references with the actual absolute path to this skill's directory. Host name: Read host.name from config. If empty, use your own agent name (from your identity). Pass it to the sub-agent as the radio host name (e.g. "Good morning, I'm Jackie, and this is your Briefing Room...").

Configuration

Config file: ~/.briefing-room/config.json Read values: python3 SKILL_DIR/scripts/config.py get location.city python3 SKILL_DIR/scripts/config.py get language python3 SKILL_DIR/scripts/config.py get voices.en.mlx_voice Set values: python3 SKILL_DIR/scripts/config.py set location.city "Vienna" python3 SKILL_DIR/scripts/config.py set location.latitude 48.21 python3 SKILL_DIR/scripts/config.py set location.longitude 16.37 python3 SKILL_DIR/scripts/config.py set language "de"

Key Config Options

KeyDefaultDescriptionlocation.cityBratislavaCity name for weather + local newslocation.latitude48.15Weather API latitudelocation.longitude17.11Weather API longitudelocation.timezoneEurope/BratislavaTimezone for weather APIlanguageenDefault briefing languageoutput.folder~/Documents/Briefing RoomOutput directoryaudio.enabledtrueGenerate audioaudio.formatmp3Audio format (mp3, wav, aiff)audio.tts_engineautoTTS engine (auto, mlx, kokoro, builtin)sectionsall 11 (see below)Which sections to includehost.name(empty = agent name)Radio host name for the briefingtrends.regionsunited-states,united-kingdom,X/Twitter trend regions (comma-separated, trailing comma = worldwide)webtrends.regionsUS,GB,Google Trends regions (ISO codes, trailing comma = worldwide)

Voice Configuration Per Language

Each language can have its own TTS engine and voice: { "voices": { "en": { "engine": "mlx", "mlx_voice": "af_heart", "mlx_voice_blend": {"af_heart": 0.6, "af_sky": 0.4}, "builtin_voice": "Samantha", "speed": 1.05 }, "sk": { "engine": "builtin", "builtin_voice": "Laura (Enhanced)", "builtin_rate": 220 }, "de": { "engine": "builtin", "builtin_voice": "Petra (Premium)", "builtin_rate": 200 } } } Engine priority (when auto): English: mlx → kokoro → builtin Other languages: builtin (Apple TTS has good multilingual voices) Users can add any language by adding a voices entry + a matching builtin_voice from say -v '?'.

Output Structure

~/Documents/Briefing Room/YYYY-MM-DD/ ├── briefing-YYYY-MM-DD-HHMM.docx # Formatted document └── briefing-YYYY-MM-DD-HHMM.mp3 # Audio briefing (~10 min) Do NOT save the .md working file in the output folder. Use /tmp/ for working files, delete after.

Step 0: Setup

# Read config CITY=$(python3 SKILL_DIR/scripts/config.py get location.city) LAT=$(python3 SKILL_DIR/scripts/config.py get location.latitude) LON=$(python3 SKILL_DIR/scripts/config.py get location.longitude) TZ=$(python3 SKILL_DIR/scripts/config.py get location.timezone) LANG=$(python3 SKILL_DIR/scripts/config.py get language) OUTPUT_FOLDER=$(python3 SKILL_DIR/scripts/config.py get output.folder) DATE=$(date +%Y-%m-%d) TIMESTAMP=$(date +%Y-%m-%d-%H%M) OUTPUT_DIR="$OUTPUT_FOLDER/$DATE" mkdir -p "$OUTPUT_DIR"

Step 1: Gather Data — Weather

Use the configured location coordinates: # Current weather TZ_ENC="${TZ/\//%2F}" BASE="https://api.open-meteo.com/v1/forecast" CURRENT="temperature_2m,relative_humidity_2m" CURRENT="$CURRENT,apparent_temperature,precipitation" CURRENT="$CURRENT,weather_code,wind_speed_10m" curl -s "$BASE?latitude=$LAT&longitude=$LON\ &current=$CURRENT&timezone=$TZ_ENC" # 7-day forecast DAILY="temperature_2m_max,temperature_2m_min" DAILY="$DAILY,precipitation_sum,weather_code" curl -s "$BASE?latitude=$LAT&longitude=$LON\ &daily=$DAILY&timezone=$TZ_ENC" Or use the helper: bash SKILL_DIR/scripts/briefing.sh weather Map weather_code to descriptions: 0: Clear sky ☀️ 1-3: Partly cloudy ⛅ 45-48: Fog 🌫️ 51-55: Drizzle 🌦️ 61-65: Rain 🌧️ 71-75: Snow ❄️ 80-82: Rain showers 🌦️ 95-99: Thunderstorm ⛈️

Step 2: Gather Data — News (Web Search)

Use web_search tool for each section. Add current date to queries for freshness. Use the configured $CITY for local news. X/Twitter Trends (from getdaytrends.com — real-time, no API key): bash SKILL_DIR/scripts/briefing.sh trends This fetches top 25 trends from US, UK, and Worldwide. Use the output to: Identify the most interesting/newsworthy trends (skip generic ones like "Good Tuesday", "Taco Tuesday") Filter out non-Latin script trends unless they're globally significant Pick ~5-10 trends that overlap across regions or seem newsworthy Use web_search to get context on the top trends you selected Web Trends (from Google Trends RSS — what people are searching): bash SKILL_DIR/scripts/briefing.sh webtrends This fetches trending Google searches from US, UK, and Worldwide with: Search term and approximate traffic volume Top news headline explaining why it's trending Use this data for the Web Trends section. The headlines already provide context — no extra searching needed for most items. World News: web_search("top world news today {date}", count=8) web_search("breaking news today", count=5) Politics: web_search("US politics news today {date}", count=5) web_search("EU politics news today {date}", count=5) web_search("geopolitics news today", count=5) ⚠️ Source diversity: All sources have bias. For balanced reporting: Search the same story with different framing Present what happened factually, note what each side says Don't adopt any outlet's framing as truth Stick to verifiable facts: numbers, dates, quotes, actions Tech & AI: web_search("tech news today {date}", count=5) web_search("AI artificial intelligence news today {date}", count=5) Local news (based on configured city): web_search("$CITY news today {date}", count=5) Also search in the configured language if not English: web_search("$CITY [news today] in $LANG {date}", count=5) Examples: Slovak: "Bratislava správy dnes" German: "Wien Nachrichten heute" Czech: "Praha zprávy dnes" Sports: web_search("sports news today {date}", count=5) web_search("football soccer results today", count=5)

Step 3: Gather Data — Markets & Crypto (APIs + Search)

# Or use helper: bash SKILL_DIR/scripts/briefing.sh crypto curl -s "https://api.coinbase.com/v2/prices/BTC-USD/spot" curl -s "https://api.coinbase.com/v2/prices/ETH-USD/spot" curl -s "https://api.coinbase.com/v2/prices/SOL-USD/spot" curl -s "https://api.coinbase.com/v2/prices/XRP-USD/spot" web_search("S&P 500 Dow Jones Nasdaq today {date}", count=5) web_search("stock market today movers {date}", count=5) web_search("gold price silver price today", count=3) web_search("crypto market today {date}", count=5)

Step 4: Compose the Briefing Script

Write as a conversational radio-host monologue. Style guidelines: Write like a smart, engaging radio host — NOT a list of headlines Use the host name — introduce yourself: "Good morning, I'm [host name], and this is your Briefing Room for [date]..." Sprinkle the name naturally throughout (sign-off, transitions) — don't overdo it Do NOT start markdown with a # Title header — pandoc adds title from metadata Connect stories with transitions Add context: "here's why this matters" Stay neutral and balanced — report facts, present sides, let listener decide Target ~2,500-3,500 words for ~10 minutes No emojis in the script (break TTS) Write out numbers/abbreviations for TTS: "$96,500" → "ninety-six thousand five hundred dollars" "S&P 500" → "S and P 500" "BTC" → "Bitcoin" "°C" → "degrees celsius" If language is not English, write the entire script in that language. Section order: Opening — Date, quick teaser of top stories Weather — Current + week outlook for configured city Trending on X — What's hot on X/Twitter Web Trends — What people are searching (Google Trends) World — Top 3-5 global stories Politics — US, EU, geopolitics Tech & AI — Launches, breakthroughs Local — News for configured city/country Sports — Headlines, results Markets — S&P 500, Dow, Nasdaq, movers Crypto & Commodities — BTC, ETH, alts, gold, silver This Day in History — 1-2 interesting events that happened on this date Closing — Wrap-up, sign-off This Day in History: No research needed — use your own knowledge. Pick 1-2 interesting, surprising, or fun events that happened on today's date. Mix it up: science, culture, politics, weird stuff. Keep it conversational: "And before I let you go — did you know that on this day in 1996..." Only include sections from the configured sections list. Skip sections the user has removed. Save as /tmp/briefing_draft_$TIMESTAMP.md (working file). For the markdown, include: Section headers with emojis: ## 🌤️ Weather, ## 🌍 World, ## 📜 This Day in History, etc. Source links after key facts Key data in bold

Step 5: Generate DOCX

pandoc "/tmp/briefing_draft_$TIMESTAMP.md" \ -o "$OUTPUT_DIR/briefing-$TIMESTAMP.docx" \ --metadata title="Briefing Room - $DATE" If pandoc is not available, skip DOCX and note it.

Step 6: Generate Audio

Read the config to determine TTS engine and voice for the current language. MLX-Audio (English, or if configured for language): python3 SKILL_DIR/scripts/config.py get voices.$LANG.engine # → if "mlx": import os, re, glob, json, subprocess from datetime import datetime timestamp = datetime.now().strftime("%Y-%m-%d-%H%M") # must match TIMESTAMP from Step 0 # Read config config_path = os.path.expanduser("~/.briefing-room/config.json") with open(config_path) as f: config = json.load(f) lang = config.get("language", "en") voices = config.get("voices", {}) voice_cfg = voices.get(lang, voices.get("en", {})) # Read and strip markdown from draft with open(f"/tmp/briefing_draft_{timestamp}.md", "r") as f: text = f.read() text = re.sub(r'#+ ', '', text) text = re.sub(r'\*\*([^*]+)\*\*', r'\1', text) text = re.sub(r'\*([^*]+)\*', r'\1', text) text = re.sub(r'\[([^\]]+)\]$[^)]+$', r'\1', text) text = re.sub(r'---+', '', text) text = re.sub(r'\n{3,}', '\n\n', text) # Resolve voice blend = voice_cfg.get("mlx_voice_blend") voice = voice_cfg.get("mlx_voice", "af_heart") if blend: model = config.get("mlx_audio", {}).get("model", "mlx-community/Kokoro-82M-bf16") model_slug = model.replace("/", "--") cache_dir = os.path.expanduser(f"~/.cache/huggingface/hub/models--{model_slug}") parts = [] for v, w in sorted(blend.items(), key=lambda x: -x[1]): parts.append(f"{v}_{int(w * 100)}") blend_name = "_".join(parts) + ".safetensors" matches = glob.glob(os.path.join(cache_dir, "snapshots/*/voices", blend_name)) if matches: voice = matches[0] speed = voice_cfg.get("speed", 1.05) lang_code = config.get("mlx_audio", {}).get("lang_code", "a") # Find MLX-Audio mlx_path = config.get("mlx_audio", {}).get("path", "") if not mlx_path: for p in ["~/.openclaw/tools/mlx-audio", "~/.local/share/mlx-audio"]: ep = os.path.expanduser(p) if os.path.exists(os.path.join(ep, ".venv/bin/python3")): mlx_path = ep break # Generate via subprocess (uses MLX-Audio's venv) python_bin = os.path.join(mlx_path, ".venv/bin/python3") # ... generate_audio call with resolved voice, speed, lang_code Built-in Apple TTS (any language): If there's no voice configured for the language, auto-detect one: # Try to get configured voice, fall back to auto-detect VOICE=$(python3 SKILL_DIR/scripts/config.py get voices.$LANG.builtin_voice) if [ "$VOICE" = "None" ] || [ -z "$VOICE" ]; then # Auto-detect: match locale (e.g. sk_SK, de_DE, fr_FR) # Prefer Enhanced/Premium voices, fall back to any VOICE=$(say -v '?' | grep "${LANG}_" \ | grep -i "Enhanced\|Premium" | head -1 \ | sed 's/ *[a-z][a-z]_[A-Z][A-Z].*//' | xargs) [ -z "$VOICE" ] && VOICE=$(say -v '?' \ | grep "${LANG}_" | head -1 \ | sed 's/ *[a-z][a-z]_[A-Z][A-Z].*//' | xargs) fi RATE=$(python3 SKILL_DIR/scripts/config.py get voices.$LANG.builtin_rate) # Strip markdown for TTS DRAFT="/tmp/briefing_draft_$TIMESTAMP.md" TTS_TXT="/tmp/briefing_tts_$TIMESTAMP.txt" sed -E 's/#+//g; s/\*+//g; s/\[([^]]*)\]$[^)]*$/\1/g' \ "$DRAFT" > "$TTS_TXT" say -v "$VOICE" ${RATE:+-r $RATE} \ -o "$OUTPUT_DIR/briefing-$TIMESTAMP.aiff" \ -f "$TTS_TXT" rm -f "/tmp/briefing_tts_$TIMESTAMP.txt" Kokoro PyTorch (fallback): Similar to MLX but uses PyTorch backend. See TubeScribe skill for Kokoro usage patterns.

Step 6b: Convert to MP3

# Find the raw audio file (MLX outputs .wav, Apple TTS outputs .aiff) RAW="" for ext in wav aiff; do if [ -f "$OUTPUT_DIR/briefing-$TIMESTAMP.$ext" ]; then RAW="$OUTPUT_DIR/briefing-$TIMESTAMP.$ext" break fi done if [ -n "$RAW" ]; then ffmpeg -y \ -i "$RAW" \ -codec:a libmp3lame -qscale:a 2 \ "$OUTPUT_DIR/briefing-$TIMESTAMP.mp3" if [ -s "$OUTPUT_DIR/briefing-$TIMESTAMP.mp3" ]; then rm "$RAW" fi fi

Step 6c: Cleanup

rm -f "/tmp/briefing_draft_$TIMESTAMP.md"

Step 7: Open Output Folder

open "$OUTPUT_DIR" Do NOT auto-play. Briefings are long and need playback controls.

Step 8: Report

Report back with: Date and language of briefing Sections covered Top 3-4 headlines Audio duration File locations

Helper Script

bash SKILL_DIR/scripts/briefing.sh setup # Check dependencies + config bash SKILL_DIR/scripts/briefing.sh weather # Fetch weather (uses config location) bash SKILL_DIR/scripts/briefing.sh trends # Fetch X/Twitter trends (US + UK + Worldwide) bash SKILL_DIR/scripts/briefing.sh webtrends # Fetch Google Trends (US + UK + Worldwide) bash SKILL_DIR/scripts/briefing.sh crypto # Fetch crypto prices bash SKILL_DIR/scripts/briefing.sh open # Open today's folder bash SKILL_DIR/scripts/briefing.sh list # List all briefings bash SKILL_DIR/scripts/briefing.sh clean # Remove briefings >30 days old bash SKILL_DIR/scripts/briefing.sh config # Show raw config JSON

Tips

Full pipeline takes 3-5 minutes (research + composition + TTS) For shorter briefing, say "quick briefing" — cover top 3 sections only If markets are closed (weekend/holiday), note it and skip detailed data The agent IS the intelligence — read search results, compose the script, decide what matters Users can add new languages by adding a voices entry + installing the voice via say -v '?'

Dependencies

Required: curl — API calls (built into macOS) web_search tool — News research (OpenClaw built-in) Recommended: MLX-Audio Kokoro — fast English TTS pandoc — DOCX generation: brew install pandoc ffmpeg — MP3 conversion: brew install ffmpeg Built-in (macOS): Apple say — multilingual TTS (always available as fallback)

Error Handling

IssueActionNo config fileRun python3 SKILL_DIR/scripts/config.py initAPI timeoutRetry once, skip that source, note itWeb search emptyTry alternative query, note gapsTTS failsFall back to Apple say (always available)Pandoc not foundSkip DOCX, deliver MP3 onlyNo internetCannot generate — inform user

Category context

Writing, remixing, publishing, visual generation, and marketing content production.

Source: Tencent SkillHub

Largest current source with strong distribution and engagement signals.

Package contents

Included in package

3 Docs2 Scripts

SKILL.md Primary doc
CHANGELOG.md Docs
README.md Docs
scripts/briefing.sh Scripts
scripts/config.py Scripts