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/skills/dream11-scoring/SKILL.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/skills/ninjabeam20/sportiq-mcp/dream11-scoring)<a href="https://agentmods.dev/skills/ninjabeam20/sportiq-mcp/dream11-scoring"><img src="https://agentmods.dev/badge/skills/ninjabeam20/sportiq-mcp/dream11-scoring/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/ninjabeam20/sportiq-mcp/dream11-scoring"><img src="https://agentmods.dev/badge/skills/ninjabeam20/sportiq-mcp/dream11-scoring.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 | $0.00029 | $0.00353 |
| Opus 5 | $0.00015 | $0.00177 |
| Sonnet 5 | $0.00006 | $0.00071 |
| Haiku 4.5 | $0.00003 | $0.00035 |
Grade A, and why
dream11-scoring 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.
What it actually says
Dream11 Scoring Skill
Mirrors the wiki page at docs/wiki/models/dream11-scoring.md. Load this when working on the Dream11 solver, scoring tables, or captain/VC selection.
Role constraints (T20)
- WK-BAT: 1–4 players
- BAT: 3–5 players
- ALL: 1–3 players
- BOWL: 3–5 players
- Total: exactly 11 players
- Max 7 from one team
- Total credits ≤ 100
Scoring (key events)
- Batting run: +1 pt; Boundary bonus: +1 pt; Six bonus: +2 pts
- 25-run milestone: +4 pts; 50: +8; 75: +12; 100: +16
- Dismissal duck: -2 pts
- Wicket (excl. run-out): +25 pts; 3-wicket haul: +4; 4-wkt: +8; 5-wkt: +16
- Maiden over: +8 pts
- Captain multiplier: 2×; Vice-captain: 1.5×
ILP approach
PuLP CBC solver. Binary variable per player × role (selected, captain, VC). Objective: maximize projected_points with C×2 + VC×1.5 boosts. See models/dream11_solver.py for the full formulation.
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 · 32 lines · 29 tokens per session scan A 02bec6102a45
dream11-scoring is a skill published in the GitHub repository Ninjabeam20/SportIQ-MCP (10 stars, last pushed yesterday), licensed MIT. It adds 29 tokens to every session and 353 once invoked, about $0.0001 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
chat-pet-sprite-creation
Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.
cpu-profile-analysis
Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…
insight-error-page
Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…