Steam Launch Forecast

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.

1. 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.

1. Build a game profile.

• Genre, tags, production scale, IP status, localization, demo/Next Fest

history, publisher credibility, and main audience regions.

1. 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.

1. 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.

1. 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.

1. 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](references/calibration-contract.md) 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.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Sttrevens
• Source: Sttrevens/4dgames-skills
• License: MIT

Install and usage instructions live in the source repository linked above.