steam-market-opportunity-research

Evaluate Steam game niches using Crawlora's game catalog, chart and player-count history, live pricing, reviews, and release news. Use for comparable-title studies, positioning and price research, and evidence-backed game opportunity hypotheses.

Assess a defined game concept against comparable titles, activity, pricing, and player feedback. Keep observed metrics, third-party estimates, and commercial hypotheses separate.

Setup

Set CRAWLORA_API_KEY to your key from crawlora.net. Run the bundled scripts/crawlora.sh from this skill directory or by absolute path. It sends x-api-key to https://api.crawlora.net/api/v1; keep the key in the environment. Read reference/endpoints.md for selected Steam live tools and stored game, chart, player-count, news, and review data.

Build and compare a defensible cohort

1. Define the concept's player experience, genre/tags, platform, business model, release cohort, and region. Use /datasets/steam-games/facets to discover dataset labels, then /datasets/steam-games/search with type=game and the relevant filters. q searches names/developers/publishers; use tag for gameplay taxonomy. Live numeric community tag IDs come from /steam/tags/list.
2. Inspect candidate titles instead of treating all matches as competitors. Separate base games, DLC, demos, free-to-play titles, and unreleased games. Resolve appid; dataset searches for history use app_id, while live /steam/app, /steam/reviews, /steam/players, and /steam/news use appid.
3. Refresh shortlist metadata and prices with /steam/app or /steam/items (up to 100 IDs). Keep cc and currency consistent. Prices in cents require conversion to major units for display; compare regular versus discounted prices separately and retain observation dates.
4. Read /datasets/steam-playercounts/search?app_id=... for observed daily concurrent-player samples. These are not daily unique users or automatically daily peaks. For /datasets/steam-charts/search, keep chart and country fixed: concurrent, most_played (weekly peak), or top_sellers (country- specific sales ranking). Single-app history uses app_id and sort=date_desc; ordinary latest requests echo snapshot_date, which may lag today.
5. Inspect release dates and /steam/news before describing activity changes. Updates, discounts, and events are possible explanations, not proven causes. Match game age and observation windows; an established live-service game's concurrent usage is not directly comparable to a short single-player release.
6. Use live /steam/reviews for a bounded, comparable sample; preserve language, purchase/review filters, playtime, and recommendation IDs. Start with cursor=*; aggregate query_summary totals populate only on that first page. Stored /datasets/steam-reviews/search emphasizes helpful reviews, not a random sample. Report theme counts as unique eligible reviews n/N, with short cited examples.

sh

scripts/crawlora.sh /datasets/steam-games/search \
  type=game tag=Roguelike min_total_reviews=100 page_size=5
# Use a returned appid as app_id when requesting dataset player-count history.

Opportunity brief and limits

Return cohort selection rules, comparable-title table, matched activity series, prices, review themes, supported positioning gaps, conflicting evidence, and a next validation step for each opportunity hypothesis.

• Dataset owner ranges/midpoints come from SteamSpy estimates. They are not audited sales counts. Do not multiply an owner midpoint by today's price and call the result realized revenue; discounts, keys, refunds, and free access differ.
• review_score is a 0–1 positive-review ratio; Metacritic uses a different scale. Missing chart entries mean unobserved/unranked within coverage, not zero players.
• A daily concurrent count cannot establish retention or unique audience size. Do not sum overlapping weekly snapshots or infer revenue from chart positions.
• Dataset pages max at 100 and the result window is 10,000. Stop on repeated cursors/pages or the sample bound. Back off on 429, retry transient 5xx once, stop on 401/403, and check application code before interpreting empty data.