Fetch a calibrated P10–P90 revenue cone for a Steam game by appid.

Uses the same v1.1 model that powers the public steamforecast.app site. Returns a JSON object with cone bounds in cents + dollars, the model version, the genre cluster used for stratified calibration, and links back to the methodology page + latest calibration report.

Args: appid: Steam app ID (e.g. 1145360 for Hades). wishlist: Optional override for catalog wishlist count (what-if mode). followers: Optional override for catalog SteamCommunity follower count.

Returns: Dict with appid, name, genres, p10/p50/p90 revenue, methodology URL.

Raises: httpx.HTTPStatusError: 404 if appid not in v1.1 catalog (~49K apps); 503 if forecast model is briefly unloaded during a deploy.

Fetch top-K nearest-neighbor comparable Steam games for an appid.

Comps are surfaced via pgvector cosine-similarity over a 1024-dim BGE embedding of game metadata (genres, tags, language, platform support, multiplayer features). Useful for sanity-checking a forecast: if the nearest comps cluster in a tight revenue band, the cone is likely well-anchored; if they're dispersed, the cone correctly widens.

Args: appid: Steam app ID to find comps for. k: Number of comps to return (1-20, default 5).

Returns: Dict with appid + list of comps, each including release year, price, follower count, week-1 + lifetime revenue, cosine similarity.

Apply the Boxleiter rule-of-thumb (review_count × multiplier × price).

A heuristic sanity check, NOT a calibrated forecast. Per the formula's own author (Mike Boxleiter, 2023 retrospective), ~24% of games are off by more than 30% from a single-multiplier estimate. Useful to compare against get_forecast() — large divergence between the heuristic and the calibrated cone signals an interesting outlier worth investigating.

Args: review_count: Total Steam reviews on the game's page. price_cents: List price in cents (e.g. 2499 for $24.99).

Returns: Dict with low (×30) / median (×50) / high (×63) revenue brackets in cents + dollars + a calibration warning.

Return the latest published live calibration coverage summary.

Numbers are from the Q2 2026 quarterly report. Live-refreshed table is at https://steamforecast.app/methodology — fetch get_methodology() for the canonical current values.

Returns: Dict with aggregate coverage, per-stratum coverage table, sample sizes, and link to the live page + quarterly report.

Return the AI-crawler-friendly methodology summary (llms.txt).

Pulls the canonical content discovery file from steamforecast.app/llms.txt, which lists high-quality URLs (methodology, guides, reports, tools) for AI agents to ingest. Useful when a model wants the full sitemap of authoritative content rather than a single forecast.

Returns: Plaintext content of /llms.txt (markdown-formatted per llmstxt.org).