Borrowing it
Nothing to install: this file belongs to Ninjabeam20/SportIQ-MCP. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Ninjabeam20/SportIQ-MCP/main/AGENTS.mdgit clone --depth 1 https://github.com/Ninjabeam20/SportIQ-MCPWrote 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/instructions/ninjabeam20/sportiq-mcp/agents-md)<a href="https://agentmods.dev/instructions/ninjabeam20/sportiq-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/ninjabeam20/sportiq-mcp/agents-md/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/instructions/ninjabeam20/sportiq-mcp/agents-md"><img src="https://agentmods.dev/badge/instructions/ninjabeam20/sportiq-mcp/agents-md.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 | $0.03642 | $0.03642 |
| Opus 5 | $0.01821 | $0.01821 |
| Sonnet 5 | $0.00728 | $0.00728 |
| Haiku 4.5 | $0.00364 | $0.00364 |
Grade A, and why
SportIQ-MCP AGENTS.md 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 6d 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- SportIQ-MCP CLAUDE.md — 94% identical, 6 lines differ
How it starts
The opening of the file, as written. The whole thing — 186 lines — stays where its author put it; the contents beside it link to each section on GitHub.
sportiq-mcp
MCP server exposing AI-callable tools across FIFA World Cup 2026 football, Formula 1, and IPL cricket. The differentiator is the intelligence layer: football_simulate_bracket (Monte Carlo with Poisson xG), f1_predict_pit_strategy (tyre degradation model on OpenF1 telemetry), cricket_build_dream11_team (PuLP constraint solver). Raw data tools are table stakes — the three flagships are the product. Order of relevance everywhere (headings, lists, tool registration): football → F1 → cricket.
Who I Am
Utkarsh — software engineer building an automated job application pipeline. Familiar with the full stack here. Skip the basics; flag the gotchas.
Collaboration Rules
- Ask, do not assume. If anything is unclear, ask before writing a single line. No silent guesses about intent, architecture, or requirements.
- Simplest solution first. No abstractions or flexibility I did not explicitly request.
- Do not touch unrelated code. If a file or function is not part of the current task, leave it alone even if you think it could be improved.
- Flag uncertainty explicitly. If you are not confident about an approach, say so before proceeding.
- No filler openers. Match response length to task complexity. Show options before significant work. Admit uncertainty before inventing facts.
- Only modify lines directly related to the task. Ask before rewriting existing working code. Confirm before deletes, overwrites, migrations, or irreversible commands.
- Hard stops for production: deploys, schema changes, external API calls, and anything irreversible need an explicit "yes" in the current message.
- End every coding task with: files changed, what was modified per file, what was intentionally not touched, and any follow-up needed.
- Default reply structure. End every task/answer with three short, plain-language sections — 1. What I did, 2. What I'm unsure about / recommend, 3. What I need you to do. Keep it brief; cut background/sources unless asked; if a section is empty say "nothing." For coding tasks, fold the Rule #8 files-changed detail into section 1.
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.
- 6d ago Changed · +3 lines · +50 tokens per session 9e4c79dcc0c3
- 10d ago First seen · 183 lines · 3,592 tokens per session scan A d43e2ee67c5b
SportIQ-MCP AGENTS.md is an instructions file published in the GitHub repository Ninjabeam20/SportIQ-MCP (10 stars, last pushed 8d ago), licensed MIT. It adds 3,642 tokens to every session, about $0.0182 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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