steam-launch-forecast

name	steam-launch-forecast
description	Use when a user needs a Steam game launch forecast, wishlist-to-sales read, comparable-game analysis, regional demand split, or market signal review for first-week or first-month performance.

Steam Launch Forecast

Use this skill to produce a market-research style forecast for Steam launch performance. It is strongest when the user provides a Steam page, wishlist count, launch date, price, publisher, social links, trailer data, or a list of comparable games.

Workflow

1. Parse the target game or game set.
   • Names, release dates, prices, wishlist counts, publishers, regions, notes.
   • Treat relative dates using the current date and verify fast-moving data online.
2. Set the forecast boundary before interpreting signals.
   • Declare Forecast mode: blind_prelaunch, retrospective_backcast, or post_launch_nowcast.
   • Record a Knowledge cutoff: the latest instant at which a permitted input was public or available. Only blind_prelaunch is eligible to be described or scored as a blind forecast.
   • A date-only source captured on release day is not blind-prelaunch evidence unless its order relative to the release is demonstrated with a timestamp and IANA time zone.
3. Build a game profile.
   • Genre, tags, production scale, IP status, localization, demo/Next Fest history, publisher credibility, and main audience regions.
4. Collect permitted signals.
   • Steam page, SteamDB followers, wishlist rank when visible, discussions, reviews/CCU if released, launch discount, supported languages.
   • YouTube, Bilibili, Reddit, Discord, creator coverage, press, and community heat when relevant.
   • Mark each material input as verified, provisional, unverified, or disputed; exclude disputed inputs from the estimate.
   • In blind_prelaunch mode, do not use post-launch reviews, CCU, sales, or any signal published after the knowledge cutoff.
5. Build a dynamic comparable set.
   • Use 3-7 recent comparables, preferably within 6-24 months.
   • Match genre, price, scale, audience region, visibility path, and launch condition.
6. Model the funnel.
   • Wishlists are an input, not the answer.
   • Adjust by wishlist freshness, genre conversion, price, review risk, Steam visibility, creator coverage, localization, and regional split.
   • If calculating first-week units / prelaunch wishlist snapshot, call it a snapshot-normalized sales ratio, not cohort conversion.
7. Output a forecast.

Required Output

Game:
Launch status:
Forecast mode:
Knowledge cutoff:
Evidence tier:
Calibration eligibility:
Known inputs:
Comparable logic:
Funnel read:
First-week forecast:
Confidence:
Main risks / upside triggers:

For multiple games, add a ranking table first.

Use the calibration contract when the request compares forecasts with outcomes, audits a prior call, or adds a result to a calibration dataset.

Rules

• Prefer ranges over false precision.
• Say what data is missing.
• Separate China-facing and Western/global demand when evidence supports it.
• Do not make investment or publishing claims from wishlist count alone.
• Keep the forecast issue time separate from the knowledge cutoff and cite the source and capture time for material inputs.
• Call a released-game analysis post_launch_nowcast or retrospective_backcast; never present it as a blind forecast.
• Do not call an estimate unbiased without a stated benchmark and error method; a post-launch nowcast is never an unbiased blind forecast.
• Label calibration provisional when actuals are not verified or the sample is too small to generalize.

Common Failure Modes

• Converting wishlists to sales with one fixed multiplier.
• Using old comparables when the genre or Steam visibility environment changed.
• Treating Western press silence as global demand weakness for China-facing games.
• Inventing private wishlist rank, revenue, or publisher data.
• Calling a same-day, date-only snapshot pre-launch without proving the time order.
• Letting post-launch reviews or CCU leak into a claimed blind forecast.
• Calling a snapshot-normalized sales ratio a conversion rate for a defined wishlist cohort.