Game Scout

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name: game-scout description: >- 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. version: 0.1.0 metadata: {"openclaw":{"emoji":"🎮","requires":{"bins":["node","python3","yt-dlp"],"env":["EXA_API_KEY","BRIGHTDATA_API_KEY","BRIGHTDATA_ZONE"]},"install":[{"id":"yt-dlp","kind":"brew","formula":"yt-dlp","bins":["yt-dlp"],"label":"Install yt-dlp (brew)"}]}}

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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 [url2...] | Extract clean text content from URLs | | node {baseDir}/scripts/exa-similar.mjs | Find pages similar to a given URL | | node {baseDir}/scripts/bright-scrape.mjs [url2...] | Scrape URLs to markdown via Bright Data (bypasses bot detection, great for Reddit) | | node {baseDir}/scripts/bright-twitter.mjs | Get Twitter/X posts via Bright Data | | python3 {baseDir}/scripts/yt-transcript.py | 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.

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Phase 1 — Query Analysis

Before searching, analyze the user's question to determine the research strategy.

Identify These Elements

1. 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).

2. 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

3. 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

4. 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 |

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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

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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

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Phase 4 — Synthesis & Validation

Cross-reference extracted information to deliver validated insights.

Cross-Reference Protocol

1. Identify consensus: Do 3+ sources agree? That's high confidence. 2. Spot conflicts: If sources disagree, note both perspectives and explain why (different patch versions, skill levels, game modes). 3. Check recency: Is the advice from the current patch/season? If a source predates a relevant patch, flag it. 4. 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

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Phase 5 — Structured Response

Deliver the answer in a format adapted to the query type.

Universal Structure

1. TL;DR: One to three sentences with the direct answer 2. The Details: Actionable specifics (build specs, step-by-step, tier placements) 3. Why This Works: The underlying mechanic or synergy that makes it effective 4. Caveats: Patch dependency, skill floor, mode-specific, rank-dependent considerations 5. Sources: Numbered list with links, creator names, and dates 6. 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.

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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.

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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.