Steam Daily Deals

Retrieve and present current Steam game deals and special offers with comprehensive game information.

Overview

This skill fetches real-time Steam discount data from the Steam Storefront API (no API key required) and presents it in a user-friendly format. It supports regional pricing, language localization, and detailed game information enrichment.

When to Use

• User asks about current Steam deals, discounts, or sales
• User wants to find cheap games on Steam
• User asks "今天Steam有什么打折游戏" or similar questions
• User wants a summary of current Steam promotions
• User asks about specific game pricing or discount history

Workflow

Step 1: Determine User Preferences

Before fetching data, determine the following from the user's request:

If the user does not specify preferences, use the defaults above.

Step 2: Fetch Steam Deals

Run the bundled fetch script to retrieve deal data:

python3 {SKILL_DIR}/scripts/fetch_steam_deals.py \
  --country CN \
  --language schinese \
  --limit 15 \
  --detailed \
  --sort discount \
  --min-discount 0 \
  --output markdown \
  --output-file /tmp/steam_deals_today.md

Parameter mapping from user requests:

Step 3: Present Results

After the script completes, read the output file and present it to the user:

1. Read /tmp/steam_deals_today.md (or the output file path used)
2. Present the formatted content directly to the user
3. Highlight notable deals:
• Games with 75%+ discount (🔥 super deals)
• AAA games on sale
• Games with "特别好评" or higher review ratings
4. Offer to:
• Show more deals (--limit increase)
• Show different regions
• Filter by genre or minimum discount
• Get details on a specific game

Step 4: Follow-up Actions

After presenting the initial results, handle follow-up requests:

• "Tell me more about [game]": Use the appdetails endpoint via the script or direct API call
• "Show deals in [region]": Re-run with different --country parameter
• "Only show RPGs": Filter results by genre from the detailed data
• "What about free games?": Run with --min-discount 100 to find free-to-play promotions

Script Details

The main script is at scripts/fetch_steam_deals.py. It:

1. Fetches featured specials from store.steampowered.com/api/featuredcategories
2. Supplements with search results from store.steampowered.com/search/results?specials=1
3. Optionally enriches each game with full details from store.steampowered.com/api/appdetails
4. Fetches Steam user review summary (positive rate, score description) from store.steampowered.com/appreviews/?json=1
5. Outputs in markdown, JSON, or plain text format

Dependencies: Python 3.7+ standard library only (no pip packages required).

Game information returned includes:

• Game name, App ID, store link
• Original price, discounted price, discount percentage
• Discount expiration time
• Steam user review score description (e.g. 好评如潮, 特别好评, 多半好评)
• Positive review rate (percentage), total positive/negative counts
• Developer and publisher
• Genres and categories (e.g., Action, RPG, Multiplayer)
• Platform support (Windows, macOS, Linux)
• Metacritic score
• Total user reviews count
• Achievement count
• Controller support level
• Supported languages
• DLC count
• Age rating
• Short description
• Header image and screenshots
• Trailer links

API Reference

For detailed Steam API endpoint documentation, field definitions, rate limits, and country/language codes, refer to references/steam_api_reference.md.

Error Handling

• If the Steam API is unreachable, the script prints error messages to stderr and returns an empty or partial result set
• If individual game details fail to load, the deal entry is preserved with basic information only
• Rate limiting is handled with built-in delays between detail requests (300ms)

Tips

• The --detailed flag significantly increases data richness but also execution time (due to per-game API calls). For quick overviews, omit it.
• Chinese region (CN) typically has the lowest prices globally.
• Steam featured specials update periodically; for the freshest data, run the script each time rather than caching.
• The script uses only Python standard library modules, ensuring maximum portability.