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 skills add machina-sports/sports-skills --skill wnba-datagit clone --depth 1 https://github.com/machina-sports/sports-skillsWrote 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/machina-sports/sports-skills/wnba-data)<a href="https://agentmods.dev/skills/machina-sports/sports-skills/wnba-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/wnba-data/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/machina-sports/sports-skills/wnba-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/wnba-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00122 | $0.01456 |
| Opus 5 | $0.00061 | $0.00728 |
| Sonnet 5 | $0.00024 | $0.00291 |
| Haiku 4.5 | $0.00012 | $0.00146 |
Grade A, and why
wnba-data 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 13d 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 — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
WNBA Data
Before writing queries, consult references/api-reference.md for endpoints, ID conventions, and data shapes.
Setup
Before first use, check if the CLI is available:
which sports-skills || pip install sports-skills
If pip install fails with a Python version error, the package requires Python 3.10+. Find a compatible Python:
python3 --version # check version
# If < 3.10, try: python3.12 -m pip install sports-skills
# On macOS with Homebrew: /opt/homebrew/bin/python3.12 -m pip install sports-skills
No API keys required.
Quick Start
Prefer the CLI — it avoids Python import path issues:
sports-skills wnba get_scoreboard
sports-skills wnba get_standings --season=2025
sports-skills wnba get_teams
CRITICAL: Before Any Query
CRITICAL: Before calling any data endpoint, verify:
- Season year is derived from the system prompt's
currentDate— never hardcoded. - If only a team name is provided, call
get_teamsto resolve the team ID before using team-specific commands.
Choosing the Season
Derive the current year from the system prompt's date (e.g., currentDate: 2026-02-18 → current year is 2026).
- If the user specifies a season, use it as-is.
- If the user says "current", "this season", or doesn't specify: The WNBA season runs May–October. If the current month is May–October, use
season = current_year. If November–April (offseason), useseason = current_year - 1.
Commands
| Command | Description |
|---|---|
get_scoreboard |
Live/recent WNBA scores |
get_standings |
Standings by conference |
get_teams |
All WNBA teams |
get_team_roster |
Full roster for a team |
get_team_schedule |
Schedule for a specific team |
get_game_summary |
Detailed box score and scoring plays |
get_leaders |
WNBA statistical leaders |
get_news |
WNBA news articles |
get_play_by_play |
Full play-by-play for a game |
get_win_probability |
Win probability chart data |
get_schedule |
Schedule for a specific date or season |
get_injuries |
Injury reports across all teams |
get_transactions |
Recent transactions |
get_futures |
Futures/odds markets |
get_team_stats |
Team statistical profile |
get_player_stats |
Player statistical profile |
What ships with it
3 files 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.
- 13d ago First seen · 151 lines · 122 tokens per session scan A 8c424575518e
wnba-data is a skill published in the GitHub repository machina-sports/sports-skills (218 stars, last pushed 5d ago), licensed MIT. It adds 122 tokens to every session and 1,456 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 2026-08-30.
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