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model_development

model_development is a skill for Claude Code, Codex from DanielTomaro13/sportsdata-agents. It costs 40 tokens per session (852 once invoked), scanned A, original, MIT.

A general method for building prediction models for any market, such as winners, totals, lines, or player props. It covers defining the prediction, choosing data and features, testing honestly, and checking probability quality.

In plain words
What is it for?
Use it to frame a market, estimate the data needed, choose features and a baseline, validate with time-ordered data, record prediction times, and assess calibration and persistence.
Why use it?
It helps avoid overfitting small samples, using information that would not have been available at prediction time, or trusting models that give poorly calibrated probabilities.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

not rated 9repo 5d ago A scan 40 tokens original MIT

Good fit Use it to frame a market, estimate the data needed, choose features and a baseline, validate with time-ordered data, record prediction times, and assess calibration and persistence.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/danieltomaro13/sportsdata-agents/model_development
View source ↗ DanielTomaro13/sportsdata-agents sportsdata-ai.com
Install

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.

Any agent
npx skills add DanielTomaro13/sportsdata-agents --skill model_development
Clone the repo
git clone --depth 1 https://github.com/DanielTomaro13/sportsdata-agents

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for model_development

README.md
[![agentmods](https://agentmods.dev/badge/skills/danieltomaro13/sportsdata-agents/model_development/github.svg)](https://agentmods.dev/skills/danieltomaro13/sportsdata-agents/model_development)
Your own site
<a href="https://agentmods.dev/skills/danieltomaro13/sportsdata-agents/model_development"><img src="https://agentmods.dev/badge/skills/danieltomaro13/sportsdata-agents/model_development/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.

agentmods 80×15 button for model_development

Your own site · 80×15
<a href="https://agentmods.dev/skills/danieltomaro13/sportsdata-agents/model_development"><img src="https://agentmods.dev/badge/skills/danieltomaro13/sportsdata-agents/model_development.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 852 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00040 $0.00852
Opus 5.5 $0.00016 $0.00341
Sonnet 5.5 $0.00008 $0.00170
Haiku 4.5 $0.00004 $0.00085

Measured 2mo ago against content hash 75db9d41db75, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

model_development 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 2mo 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.

src/sportsdata_agents/skills/model_development/SKILL.md · 71 lines

How it starts

The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Model development — the general method

This is the method for every market (winners, totals, lines, props). Market recipes (build_a_totals_model, build_a_h2h_model) are worked examples OF this method, never replacements for it.

1. Frame the problem

  • What exactly is being predicted? One binary probability per selection is the default (P(home win), P(over)). Name the market convention (whose line, which book) before any code.
  • What is the decision the probability feeds? A backtest entry needs the prob BEFORE the price moves — timestamps are part of the model contract (record_predictions takes predicted_at; never backdate dishonestly).

2. Sample size BEFORE cleverness

  • Rule of thumb: 10–20 outcomes per model parameter as a floor. A logistic regression with 5 features wants 100+ decided events; anything fancier wants several times that.
  • "Last 20 games" is rarely enough — it estimates a mean with ±20%+ noise and one parameter eats half of it. Prefer multiple seasons with recency weighting (exponential decay) over tiny recent windows.
  • Sport cadence changes everything: an MLB team plays 162 games/season, an NFL team 17 — the same "two seasons of data" is 324 events in one sport and 34 in the other. Count EVENTS, not calendar time.
  • Watch regime changes: rule changes, roster turnover, venue moves. Old data is only valuable while the process that generated it still operates — say so when you truncate history and why.
  • When the data cannot support the model requested, SAY THAT PLAINLY and build the smaller model that it can support.

3. Features: ask AND measure

  • When the request is open-ended, ask the user which stats they believe matter for this market — domain priors are real information and it is their model.
  • Independently measure importance from the data (univariate signal, simple permutation importance in run_python) — then report where the user's priors and the data disagree, with numbers. Do not silently drop either.
  • Fewer features beat more: every feature is a parameter and §2 already priced parameters. Justify each one in a sentence.

Read the full file on GitHub · 71 lines

Changes

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.

  1. 2mo ago First seen · 71 lines · 40 tokens per session scan A 75db9d41db75

Subscribe to this mod's changes

model_development is a skill published in the GitHub repository DanielTomaro13/sportsdata-agents (9 stars, last pushed 5d ago), licensed MIT. It adds 40 tokens to every session and 852 once invoked, about $0.0002 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 2026-08-31.

Data and AIPythonai-agentsmcpsports-bettingsports-data
Related

model_development compared with skills from other repositories

model_development next to 6 skills from other repositories
ModTypeStarsLast pushLicenceScanTokensWorks with
model_developmentDanielTomaro13/sportsdata-agentsSkill95d agoMITA40Claude Code, Codex
pytorch-patternsaffaan-m/ECCSkill298k5d agoMITA32Claude Code, Kiro
optimize-for-gpuK-Dense-AI/scientific-agent-skillsSkill56k4d agoMITA151Claude Code, Codex
developing-genkit-pythongoogle/skillsSkill22kyesterdayApache-2.0A49Claude Code, Codex
thinking-out-loudShubhamsaboo/awesome-llm-appsSkill144kyesterdayApache-2.0A206Claude Code, Codex
pydantic-aidavila7/claude-code-templatesSkill33kyesterdayMITA32Claude Code, Codex
open-sourcebrowser-use/browser-useSkill120kyesterdayMITA137Claude Code