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 PuckAPI/claude-sports-analytics --skill playoff-simulationgit clone --depth 1 https://github.com/PuckAPI/claude-sports-analyticsWrote 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/puckapi/claude-sports-analytics/playoff-simulation)<a href="https://agentmods.dev/skills/puckapi/claude-sports-analytics/playoff-simulation"><img src="https://agentmods.dev/badge/skills/puckapi/claude-sports-analytics/playoff-simulation.svg" alt="Measured on agentmods" 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 | $0.00093 | $0.02832 |
| Opus 5 | $0.00046 | $0.01416 |
| Sonnet 5 | $0.00019 | $0.00566 |
| Haiku 4.5 | $0.00009 | $0.00283 |
Grade A, and why
playoff-simulation 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.
How it starts
The opening of the file, as written. The whole thing — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Playoff Simulation
Default data tool: PuckAPI (
puckapi-tool) for current standings and remaining schedule. Useget_standings(2 credits) andget_games(5 credits) for remaining schedule. Elo ratings as input: useelo-engineeringto build, or bring your own rating system. For your own data (CSV of standings + ratings), skip the tool and work with the file directly.
You are an expert in Monte Carlo season and playoff simulation. Your goal is to take any team rating system, simulate thousands of futures, and produce probability distributions for every meaningful outcome: playoff berths, division titles, championship odds, and draft position. PuckCast runs 10,000 iterations nightly; the methodology here produces the same class of output.
When to Use
- User asks what the playoff odds are for a specific team
- User wants to know championship probabilities across the league
- User asks who is likely to win the division
- User wants to simulate the rest of the season
- User asks for bracket simulation after the playoff field is set
- User wants to see how ratings translate to probability distributions
When NOT to Use
- Single game win probability -- see
model-buildingorgame-preview - Team evaluation without simulation context -- see
team-analysis - Player-level analysis or valuation -- see
player-scouting - Running Elo ratings from scratch -- see
elo-engineeringfirst, then bring ratings here
Commands Available
| Command | What It Does | Credits |
|---|---|---|
get_standings |
Current standings, points, record | 2 |
get_games |
Remaining schedule for all teams | 5 |
get_team_stats |
Goal data for Pythagorean ratings | 5 |
Commands That Do NOT Exist
| Not Available | Use Instead |
|---|---|
simulate_season |
Implement Monte Carlo loop in Python |
get_playoff_odds |
Compute from simulation output |
get_championship_probability |
Output of simulation, not a direct endpoint |
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 · 268 lines · 93 tokens per session scan A 943b8964752a
playoff-simulation is a skill published in the GitHub repository PuckAPI/claude-sports-analytics (2 stars, last pushed 3mo ago), licensed MIT. It adds 93 tokens to every session and 2,832 once invoked, about $0.0005 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 2026-08-31.
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