briefing-room

name	Briefing Room
description	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.
metadata	{"openclaw":{"emoji":"📻","requires":{"bins":["curl"]}}}

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"):

1. Check config exists (run setup if not)
2. Play notification sound: afplay /System/Library/Sounds/Blow.aiff &
3. Spawn a sub-agent with the full pipeline task immediately
4. Reply: "📻 Briefing Room is firing up — gathering today's news. I'll ping you when it's ready!"
5. 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

Key	Default	Description
location.city	Bratislava	City name for weather + local news
location.latitude	48.15	Weather API latitude
location.longitude	17.11	Weather API longitude
location.timezone	Europe/Bratislava	Timezone for weather API
language	en	Default briefing language
output.folder	~/Documents/Briefing Room	Output directory
audio.enabled	true	Generate audio
audio.format	mp3	Audio format (mp3, wav, aiff)
audio.tts_engine	auto	TTS engine (auto, mlx, kokoro, builtin)
sections	all 11 (see below)	Which sections to include
host.name	(empty = agent name)	Radio host name for the briefing
trends.regions	united-states,united-kingdom,	X/Twitter trend regions (comma-separated, trailing comma = worldwide)
webtrends.regions	US,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.

Full Pipeline

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:

1. Opening — Date, quick teaser of top stories
2. Weather — Current + week outlook for configured city
3. Trending on X — What's hot on X/Twitter
4. Web Trends — What people are searching (Google Trends)
5. World — Top 3-5 global stories
6. Politics — US, EU, geopolitics
7. Tech & AI — Launches, breakthroughs
8. Local — News for configured city/country
9. Sports — Headlines, results
10. Markets — S&P 500, Dow, Nasdaq, movers
11. Crypto & Commodities — BTC, ETH, alts, gold, silver
12. This Day in History — 1-2 interesting events that happened on this date
13. 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

Issue	Action
No config file	Run python3 SKILL_DIR/scripts/config.py init
API timeout	Retry once, skip that source, note it
Web search empty	Try alternative query, note gaps
TTS fails	Fall back to Apple say (always available)
Pandoc not found	Skip DOCX, deliver MP3 only
No internet	Cannot generate — inform user