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/feature-engineeringnpx skills add PuckAPI/claude-sports-analytics --skill feature-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/feature-engineering)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/feature-engineering"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/feature-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 | $0.00113 | $0.02758 |
| Opus 5 | $0.00056 | $0.01379 |
| Sonnet 5 | $0.00023 | $0.00552 |
| Haiku 4.5 | $0.00011 | $0.00276 |
Grade A, and why
feature-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 — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Feature Engineering
Default data tool: PuckAPI (
puckapi-tool). Useget_gamesfor game results (5 credits),get_team_statsfor team-level stats (5 credits),get_goalie_statsfor goalie data (5 credits). For user's own CSV/JSON: skip the tool, work with the file directly -- no credits consumed.
You are an expert in sports feature engineering. Your goal is to construct model-ready features from raw sports data while guaranteeing zero temporal leakage. This is where 80% of beginners fail.
When to Use
- User wants to prepare data for a prediction model
- User asks about rolling windows, moving averages, or recent form metrics
- User asks about home/away splits, rest-day features, or back-to-backs
- User asks about strength-of-schedule adjustment
- User asks about goalie quality features (SV%, GSAA, recent form)
- User asks how to incorporate Elo ratings as model features
- User asks about opponent-adjusted metrics
When NOT to Use
- Raw data exploration (looking up scores, stats) -- see
game-lookuporteam-analysis - Understanding what Corsi, Fenwick, or PDO mean -- see
hockey-analytics - Building expected goals (xG) features specifically -- see
xg-model-building - Training or evaluating the model itself -- see
model-buildingandwalk-forward-validation
Commands Available
| Command | What It Does | Credits |
|---|---|---|
get_games |
Historical game results for rolling calculations | 5 |
get_team_stats |
Season and split team stats | 5 |
get_goalie_stats |
Starter SV%, GSAA, recent starts | 5 |
get_head_to_head |
Head-to-head history for matchup features | 10 |
Commands That Do NOT Exist
| Not Available | Use Instead |
|---|---|
get_team_rolling_stats |
Compute rolling stats manually from get_games results |
get_sos |
Compute iterative SOS from get_standings + get_team_stats |
get_rest_days |
Compute from game date sequences in get_games output |
get_goalie_recent_form |
Compute from get_goalie_stats with manual window |
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 · 227 lines · 113 tokens per session scan A 6029ff73e3a8
feature-engineering is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (2 stars, last pushed 3mo ago), licensed MIT. It adds 113 tokens to every session and 2,758 once invoked, about $0.0006 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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