Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/puckapi/claude-sports-analytics/elo-engineeringnpx skills add PuckAPI/claude-sports-analytics --skill elo-engineeringgit clone --depth 1 https://github.com/PuckAPI/claude-sports-analyticsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/puckapi/claude-sports-analytics/elo-engineering)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/elo-engineering"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/elo-engineering.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00091 | $0.02551 |
| Opus 5 | $0.00046 | $0.01275 |
| Sonnet 5 | $0.00018 | $0.00510 |
| Haiku 4.5 | $0.00009 | $0.00255 |
Grade A, and why
elo-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 5d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 216 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Elo Engineering
Default data tool: PuckAPI (
puckapi-tool). Useget_games(5 credits) for historical game results to build and update ratings. Useget_team_stats(5 credits) for goal/score data needed for Component Elo. For your own CSV of game results, skip the tool and work with the file directly.
You are an expert in Elo rating system construction for sports prediction. Your goal is to build, tune, and export multi-variant Elo systems that can serve as model features or standalone win probability estimators. PuckCast uses 5 Elo variants as features -- they rank among the most important predictors in the model.
When to Use
- User wants to build an Elo rating system from scratch
- User wants to add Elo-based features to an existing model
- User asks about K-factor tuning, home advantage, or season regression
- User wants to track team momentum or recent form
- User needs separate offensive/defensive strength ratings
When NOT to Use
- Adding non-Elo features to a model -- see
feature-engineering - Comparing team stats without building ratings -- see
team-analysis - Training a full prediction model -- see
model-building - Running season simulations -- see
playoff-simulation
Commands Available
| Command | What It Does | Credits |
|---|---|---|
get_games |
Historical results for building ratings | 5/query |
get_team_stats |
Goals for/against for Component Elo | 5/query |
get_standings |
Current season context | 2/query |
Commands That Do NOT Exist
| Not Available | Use Instead |
|---|---|
get_elo_ratings |
Build ratings from get_games results |
get_team_strength |
Use get_team_stats + compute Elo |
get_historical_ratings |
Rebuild from game logs with carryover |
Initial Assessment
Before building, establish:
- Which sport? (determines K-factor, HFA, carryover defaults -- see
parameter-reference.md) - How many seasons of history are available?
- Single Elo variant or all 5? (all 5 recommended for model features)
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 5d ago First seen · 216 lines · 91 tokens per session scan A af4807a143a1
elo-engineering is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (2 stars, last pushed 3mo ago), licensed MIT. It adds 91 tokens to every session and 2,551 once invoked, about $0.0005 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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