Install
openclaw skills install game-scoutGame Scout
Video game strategy specialist. Amalgamates tactics, builds, guides, and meta knowledge from Reddit, YouTube creators, wikis, Twitter/X, and game databases to unlock a higher gaming experience. Trigger on: builds, loadouts, tier lists, meta, strategy, "best build for", "what's meta in", "how to play", "is X still good", "what does X do", patch notes, weapon stats, item guides, pro play, or any question mentioning a video game by name.
Game Tips — Multi-Source Research Pipeline
Deliver real, tested, actionable, current gaming insight by orchestrating parallel research across multiple sources. The goal is to replace hours of searching and watching content with a single, well-sourced answer that gives the player a competitive edge.
Available Scripts
| Script | What It Does |
|---|---|
node {baseDir}/scripts/exa-search.mjs "query" | Semantic web search via Exa AI (understands intent, handles negation) |
node {baseDir}/scripts/exa-contents.mjs <url> [url2...] | Extract clean text content from URLs |
node {baseDir}/scripts/exa-similar.mjs <url> | Find pages similar to a given URL |
node {baseDir}/scripts/bright-scrape.mjs <url> [url2...] | Scrape URLs to markdown via Bright Data (bypasses bot detection, great for Reddit) |
node {baseDir}/scripts/bright-twitter.mjs <profile-or-post-url> | Get Twitter/X posts via Bright Data |
python3 {baseDir}/scripts/yt-transcript.py <youtube-url> | Extract YouTube video transcript + metadata |
Script Options Quick Reference
exa-search.mjs: -n 10 (results count), --domain reddit.com (limit to domain), --exclude bad.com, --after 2026-01-01 (date filter), --contents (include page text), --summary (include AI summary), --category tweet|news
exa-contents.mjs: Multiple URLs supported. --summary "question" for targeted summary.
exa-similar.mjs: -n 10, --domain, --after, --contents
bright-scrape.mjs: Multiple URLs supported. --country us for geo-targeting.
bright-twitter.mjs: Pass one or more tweet URLs. --timeout 60 (wait time in seconds). Collects by URL — find tweet URLs first via Exa search.
yt-transcript.py: --no-meta to skip metadata.
Phase 1 — Query Analysis
Before searching, analyze the user's question to determine the research strategy.
Identify These Elements
-
Game: Exact title. Resolve abbreviations (PoE = Path of Exile, ER = Elden Ring, Val = Valorant, D2 = Destiny 2 or Diablo 2 depending on context, LoL = League of Legends, WoW = World of Warcraft, MH = Monster Hunter, FFXIV = Final Fantasy XIV).
-
Topic Type:
- Build/Loadout: Weapon, armor, skill, talent, or gear combinations
- Strategy/Guide: How to approach encounters, modes, or progression
- Mechanic/Interaction: How a specific system, item, or ability works
- Meta/Tier List: What's currently strongest or most popular
- Patch/Balance: Recent changes and their impact
- Pro Play/Esports: What competitive or high-level players are using
-
Recency Requirements:
- Critical (live-service games with frequent patches): Must find current-patch info
- Moderate (games with periodic updates): Recent info preferred, older is OK
- Low (stable/single-player games): Evergreen guides are fine
-
Scope:
- Narrow ("does X proc bleed?"): Target wiki/database, skip broad search
- Broad ("best builds for class X"): Full pipeline
Decision Table
| Scope | Recency | Action |
|---|---|---|
| Narrow + Low | Skip to Phase 3: scrape relevant wiki directly | |
| Narrow + Critical | Phase 2 (limited) + Phase 3: wiki + Reddit for patch confirmation | |
| Broad + Any | Full pipeline: Phase 2 → 3 → 4 → 5 |
Phase 2 — Parallel Discovery
Cast a wide net. Run multiple search commands to discover the best sources.
Read references/search-strategies.md for game-specific query templates and community hub URLs.
A. Exa AI Semantic Search (2-3 queries)
Exa understands intent — phrase queries naturally. Run these in parallel:
# Primary search
node {baseDir}/scripts/exa-search.mjs "best [topic] for [game] [current patch/season]" -n 10 --after 2026-01-01
# Reddit-focused
node {baseDir}/scripts/exa-search.mjs "[game] [topic] discussion recommendations" -n 5 --domain reddit.com --after 2025-06-01
# YouTube video discovery
node {baseDir}/scripts/exa-search.mjs "[game] [topic] guide tutorial" -n 5 --domain youtube.com --after 2025-06-01
B. Twitter/X (recent community takes)
First find tweet URLs via Exa, then extract full data via Bright Data:
# Step 1: Find relevant tweets via Exa search
node {baseDir}/scripts/exa-search.mjs "[game] [topic] meta" --domain twitter.com -n 5
# Step 2: Extract full tweet data for the URLs found
node {baseDir}/scripts/bright-twitter.mjs "https://x.com/user/status/123" "https://x.com/user/status/456"
Evaluate Discovery Results
From all results, identify the best 5-8 sources to extract in depth:
- Prefer recent content (check dates)
- Prefer high-engagement Reddit threads
- Prefer YouTube videos from known guide creators
- Prefer wiki/database pages for factual/stat questions
- Include at least 2 different source types for cross-referencing
Phase 3 — Deep Extraction
Go deep on the best sources. Run extraction commands for each source type.
Read references/source-extraction.md for detailed extraction patterns.
Reddit Threads
Use Bright Data scraper — it bypasses Reddit's bot detection. Prepend old. for cleaner scrapes:
node {baseDir}/scripts/bright-scrape.mjs "https://old.reddit.com/r/[sub]/comments/[id]/[slug]" "https://old.reddit.com/r/[sub]/comments/[id2]/[slug2]"
Focus on: OP content, top-voted comments, comments with specific data. Discard: jokes, deleted comments, tangents.
YouTube Videos
python3 {baseDir}/scripts/yt-transcript.py "https://www.youtube.com/watch?v=VIDEO_ID"
In the transcript, look for: section markers ("first/second/third"), build specifications, stat numbers, specific item/weapon names, caveats.
Fallback if no subtitles: scrape the YouTube page for description + comments:
node {baseDir}/scripts/bright-scrape.mjs "https://www.youtube.com/watch?v=VIDEO_ID"
Articles & Wiki Pages
Use Exa for clean extraction (works best on articles/wikis):
node {baseDir}/scripts/exa-contents.mjs "https://fextralife.com/..." "https://maxroll.gg/..." --summary "What build is recommended?"
For sites that block Exa, fall back to Bright Data:
node {baseDir}/scripts/bright-scrape.mjs "https://example.com/guide"
Context Management
Summarize extracted content as you go. For each source, distill to:
- Key recommendations/findings
- Specific data points (stats, percentages, item names)
- Date/patch version
- Source URL for attribution
Phase 4 — Synthesis & Validation
Cross-reference extracted information to deliver validated insights.
Cross-Reference Protocol
- Identify consensus: Do 3+ sources agree? That's high confidence.
- Spot conflicts: If sources disagree, note both perspectives and explain why (different patch versions, skill levels, game modes).
- Check recency: Is the advice from the current patch/season? If a source predates a relevant patch, flag it.
- Validate specifics: If a build claims specific stats, verify against wiki/database data when possible.
Confidence Assessment
| Level | Criteria |
|---|---|
| HIGH | 3+ recent sources agree, current patch confirmed, community consensus |
| MEDIUM | 2 sources agree, or sources are slightly dated but no known nerfs/buffs |
| LOW | Single source, pre-patch info, or actively contested in community |
Red Flags
- Source is from a previous patch and the topic is affected by balance changes
- Reddit thread has top comments disagreeing with OP
- YouTube video has comments saying "this was nerfed"
- Conflicting info between wiki and community — community is usually more current
Phase 5 — Structured Response
Deliver the answer in a format adapted to the query type.
Universal Structure
- TL;DR: One to three sentences with the direct answer
- The Details: Actionable specifics (build specs, step-by-step, tier placements)
- Why This Works: The underlying mechanic or synergy that makes it effective
- Caveats: Patch dependency, skill floor, mode-specific, rank-dependent considerations
- Sources: Numbered list with links, creator names, and dates
- Confidence: HIGH/MEDIUM/LOW with brief reasoning
Format by Query Type
Build/Loadout queries: Use a table or structured list with alternatives.
Meta/Tier list queries: Use S/A/B tier format with explanations per tier.
Mechanic/Interaction queries: Direct yes/no answer first, then detailed breakdown.
Strategy/Guide queries: Numbered step-by-step with reasoning per step.
See examples/sample-queries.md for full format examples of each type.
Fallbacks & Error Handling
If a script fails, degrade gracefully rather than abandoning the research.
| Script Failing | Fallback |
|---|---|
| exa-search | Use summarize CLI if available, or ask user to provide URLs |
| bright-scrape | Use exa-contents for the same URLs |
| bright-twitter | Search Twitter via exa-search with --domain twitter.com --category tweet |
| yt-transcript | Use bright-scrape on the YouTube URL for description + comments |
| exa-contents | Use bright-scrape for the same URLs |
If a search returns no relevant results, broaden the query or try alternative phrasing before giving up.
Reference Files
references/search-strategies.md— Read when formulating search queries. Contains game-specific community hubs, subreddit names, and query templates by topic type.references/source-extraction.md— Read when extracting content from sources. Contains patterns for Reddit parsing, YouTube transcript processing, and wiki extraction.references/game-databases.md— Read when the query involves specific game data or interactive build planners. Contains URLs and navigation hints per game.examples/sample-queries.md— Read for calibration. Shows the full pipeline applied to four different query types.