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 withoneai/one-agent-plugin --skill college-football-datagit clone --depth 1 https://github.com/withoneai/one-agent-pluginWrote 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/withoneai/one-agent-plugin/college-football-data)<a href="https://agentmods.dev/skills/withoneai/one-agent-plugin/college-football-data"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/college-football-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/withoneai/one-agent-plugin/college-football-data"><img src="https://agentmods.dev/badge/skills/withoneai/one-agent-plugin/college-football-data.svg" alt="Reviewed on agentmods" width="80" 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 | $0.00135 | $0.05563 |
| Opus 5 | $0.00068 | $0.02781 |
| Sonnet 5 | $0.00027 | $0.01113 |
| Haiku 4.5 | $0.00014 | $0.00556 |
Grade A, and why
college-football-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 7d 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.
This is a copy
80% identical to 2-chat — 391 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 466 lines — stays where its author put it; the contents beside it link to each section on GitHub.
College Football Data through One
College Football Data is a sports data platform and API that provides structured college football statistics, rankings, play-by-play, betting lines, and historical records, allowing developers, analysts, and media teams to build applications, models, dashboards, and research workflows around NCAA football data.
One exposes College Football Data through four MCP tools. The table below carries real action ids from One's knowledge base, so for a common operation you can skip search and go straight to reading the action's parameters.
How to run an action
- Find the action in the table below, or call
search_one_platform_actionswith platformcollege-football-dataif it is not listed. - Call
get_one_action_knowledgewith the action id. Do this every time, including for actions in this table. The table gives you the id, not the parameters. - Call
execute_one_actionwith parameters copied from that knowledge.
Never guess a parameter name, a body field, or an enum value. The knowledge has the real schema, and a guessed field is either a 400 or a silent write of the wrong thing.
Before you start
Call list_one_integrations once and confirm College Football Data is connected. If it is missing, the user has not connected it: say so and point them at https://app.withone.ai rather than reaching for raw HTTP.
Each connection carries an access field. If it reports {"policy": "methods", "methods": ["GET"]} the agent is read-only here, so plan a read-only answer instead of attempting a write that will be refused.
Before a write
Creates, updates, deletes and sends land on a real College Football Data account and cannot be recalled. State the action and the specific target in one line before the first write in a task, and let the user stop you. Reads need no confirmation.
Actions
Teams
| Action | Method | Path | Action id |
|---|---|---|---|
| Get FBS Teams | GET | /teams/fbs |
conn_mod_def::GMitwiUJ4fg::Rme4dJLsRe-1hSAyyTQWWA |
| List Teams | GET | /teams |
conn_mod_def::GMitwtbueJg::GyCgtvvHS1Ke0GXCH00nFw |
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.
- 7d ago First seen · 466 lines · 0 tokens per session scan A adb0e14b7751
college-football-data is a skill published in the GitHub repository withoneai/one-agent-plugin (1 stars, last pushed 21d ago), licensed MIT. It adds 135 tokens to every session and 5,563 once invoked, about $0.0007 per session on Opus 5. A static security scan graded it A with 0 findings. It is 80% identical to 2-chat, differing in 391 lines, and is treated as a copy.
Other skills, from other repositories
TEMPLATE
../../../agents/personas/TEMPLATE.md.
document-service
This skill should be used when the user asks to "analyze this codebase", "document this service", "generate technical docs", "I inherited this code", "help me understand this system", "create docs for this project", "what does this system look like", "onboard me to this codebase", "this codebase has no docs"…
ov-experience-memory
Retrieve and apply OpenViking Experience memories through the Agent runtime's generic OpenViking search and read tools. Use before or during executable, multi-step, or tool-based work such as coding, file or data changes, configuration, deployment, workflow execution, and failure recovery when prior operational…
experience_loader
Load relevant OpenViking experience memories via case-linked experience candidates before solving a task.
sdk-getting-started
Validates the user's environment for SageMaker AI operations — checks SDK version, AWS region, and execution role. Use when the user says "set up", "getting started", "check my environment", "configure SDK", or as the first step in any plan involving SageMaker/Bedrock training, evaluation, or deployment.
using-agent-brain
Expert Agent Brain skill for document search with BM25 keyword, semantic vector, hybrid, graph, and multi retrieval modes. Use when asked to "search documentation", "query domain", "find in docs", "bm25 search", "hybrid search", "semantic search", "graph search", "multi search", "find dependencies", "code…