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 xctf-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/xctf-data)<a href="https://agentmods.dev/skills/machina-sports/sports-skills/xctf-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/xctf-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/xctf-data"><img src="https://agentmods.dev/badge/skills/machina-sports/sports-skills/xctf-data.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high YARA Match · line 27 YARA rule matched a known malware signature (reverse shell, backdoor, ransomware, C2 framework, or info stealer).Fix: Remove the malware payload or compromised file entirely. Investigate how it entered the skill and audit all other artifacts for additional indicators of compromise.
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 | $0.00186 | $0.02030 |
| Opus 5 | $0.00093 | $0.01015 |
| Sonnet 5 | $0.00037 | $0.00406 |
| Haiku 4.5 | $0.00019 | $0.00203 |
Grade A, and why
xctf-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 12d 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 — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
XC/TF Data (TFRRS — NCAA Cross Country & Track and Field)
Before writing queries, consult references/api-reference.md for parameters, URL conventions, and return shapes.
Setup
Before first use, check if the CLI is available:
which sports-skills || pip install sports-skills
If pip install fails, install from GitHub:
pip install git+https://github.com/machina-sports/sports-skills.git
Requires Python 3.10+. No API keys required. All data comes from TFRRS public pages and The Stride Report RSS feed.
Quick Start
CLI (preferred):
sports-skills xctf get_athlete_profile --athlete_id=9230145 --school=BYU --name=Jane_Hedengren
sports-skills xctf get_news --limit=5
Python SDK:
from sports_skills import xctf
profile = xctf.get_athlete_profile(
athlete_id="9230145",
school="BYU",
name="Jane_Hedengren",
)
CRITICAL: Before Any Query
All three parameters are required and must match the athlete's TFRRS URL exactly:
https://www.tfrrs.org/athletes/{athlete_id}/{school}/{name}.html
athlete_id— numeric ID (e.g.9230145)school— school slug with underscores, not spaces (e.g.BYU)name— athlete name slug (e.g.Jane_Hedengren)
Do NOT guess slugs. Find them by navigating to the athlete on tfrrs.org and copying the URL.
Commands
| Command | Description |
|---|---|
search_athlete |
Search the current team roster by name; returns athlete_id, school, and name slugs for use with get_athlete_profile. Searches both genders automatically. Current athletes only — graduated athletes require a direct TFRRS URL |
get_athlete_profile |
Athlete name, school, eligibility, all PRs, and full season-by-season meet results |
get_team_roster |
Full XC and/or TF roster for a team |
get_meet_results |
All event results and team scores from a TFRRS meet |
get_news |
Recent XC/TF articles from The Stride Report (thestridereport.com) |
See references/api-reference.md for full parameter details and return shapes.
What ships with it
2 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.
- 12d ago First seen · 163 lines · 186 tokens per session scan A 7c5f5777be30
xctf-data is a skill published in the GitHub repository machina-sports/sports-skills (218 stars, last pushed 4d ago), licensed MIT. It adds 186 tokens to every session and 2,030 once invoked, about $0.0009 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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