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 nhl-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/nhl-data)<a href="https://agentmods.dev/skills/machina-sports/sports-skills/nhl-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/nhl-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/nhl-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/nhl-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 | $0.00248 | $0.02802 |
| Opus 5.5 | $0.00099 | $0.01121 |
| Sonnet 5.5 | $0.00050 | $0.00560 |
| Haiku 4.5 | $0.00025 | $0.00280 |
Grade A, and why
nhl-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 9d 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 — 212 lines — stays where its author put it; the contents beside it link to each section on GitHub.
NHL 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 nhl get_scoreboard
sports-skills nhl get_standings --season=2025
sports-skills nhl get_teams
CRITICAL: Before Any Query
CRITICAL: Before calling any data endpoint, verify:
- For the current season, omit
season/season_yearon ESPN commands — never hardcode one. - If only a team name is provided, call
get_teamsto resolve the team ID before using team-specific commands.
Choosing the Season
ESPN names an NHL season by the year it ends: 2026-27 is 2027, so in February 2026 the current season is 2026.
- If the user says "current", "this season", or doesn't specify: omit
season/season_year. Forget_leaders,get_futures,get_team_statsandget_player_stats, the default is a calendar-selected ending year, not necessarily a season with games yet: from October 1 the season ending next year, before October the one ending this year. - Between October 1 and opening night the default season may have no data.
get_leaderscan return empty or unavailable and does not fall back.get_futures,get_team_statsandget_player_statsretry the prior season only when ESPN returns an error (with awarningsnote), not when it returns an empty successful payload. - If the user names a past season: pass its ending year (2024-25 →
2025). An explicitly requested season never falls back. - The
get_nhlstats_*commands use the starting year instead (see "Using ESPN and the NHL API Together").
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.
- 9d ago Changed · +18 lines 84d6db67d639
- 1mo ago Changed · +9 lines 8f412422f22e
- 1mo ago Changed · +2 lines 323feb69aeec
- 2mo ago First seen · 183 lines · 248 tokens per session scan A 1658ed81b00b
nhl-data is a skill published in the GitHub repository machina-sports/sports-skills (264 stars, last pushed yesterday), licensed MIT. It adds 248 tokens to every session and 2,802 once invoked, about $0.0010 per session on Opus 5.5. 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.
nhl-data compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| nhl-datamachina-sports/sports-skills | Skill | 264 | yesterday | MIT | A | 248 | Claude Code, Codex |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |