CCXT is a software library that gives programs a common interface for accessing cryptocurrency exchanges and prediction markets. It is used by developers, traders, analysts, and data scientists for market-data analysis, automated trading, backtesting, and trading bots.
Borrowing it
Nothing to install: this file belongs to ccxt/ccxt. 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/ccxt/ccxt/master/.agents/skills/binance-sports-ai-analyzer/SKILL.mdgit clone --depth 1 https://github.com/ccxt/ccxtWrote 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/ccxt/ccxt/binance-sports-ai-analyzer)<a href="https://agentmods.dev/skills/ccxt/ccxt/binance-sports-ai-analyzer"><img src="https://agentmods.dev/badge/skills/ccxt/ccxt/binance-sports-ai-analyzer/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/ccxt/ccxt/binance-sports-ai-analyzer"><img src="https://agentmods.dev/badge/skills/ccxt/ccxt/binance-sports-ai-analyzer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Snyk warn
- NVIDIA SkillSpector pass
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 | $0.00060 | $0.01930 |
| Opus 5 | $0.00030 | $0.00965 |
| Sonnet 5 | $0.00012 | $0.00386 |
| Haiku 4.5 | $0.00006 | $0.00193 |
Grade A, and why
binance-sports-ai-analyzer 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 11d 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 — 115 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Sports AI Analyzer Skill
Use this skill to look up World Cup match slugs, resolve them to canonical match IDs, fetch AI prediction data, recompute final probabilities after user adjustments, and hand off to Binance Agentic Wallet prediction trading when the user explicitly wants to place an order.
Quick Workflow
- List matches and ask the user to choose.
Always call
recent-match-optionsfirst to get currently available matches with teams and kickoff time. Show the list in a readable form and ask which match to analyze. Do not show raw slug-only examples or default to the first match unless the user already gave a specific slug. - Resolve the selected slug to match details.
Call
resolve-by-slugand readcanonical_match_id,home_team,away_team, kickoff, and status. - Fetch the prediction bundle.
Call
predictionwithcmid, then callnews-insightsandmaster-analysisfor context. Only passplatform: "PREDICT_FUN"when the user explicitly asks for Predict Fun odds. - Recompute only after user changes correction signals.
Call
recompute-finalwith the editedsignals; this is stateless and does not write to the database. - Trade only after explicit confirmation.
Call
market-detail-by-slugto getmarketTopicIdand market/outcome details, then usebinance-agentic-walletprediction quote and order commands.
CLI
node <skill-dir>/scripts/cli.mjs <command> '<json_params>'
| Command | Purpose | Required params |
|---|---|---|
recent-unfinished |
List unfinished World Cup match slugs with active market bindings | none |
recent-match-options |
List match options with slug, teams, kickoff time, status, and canonical_match_id |
none |
resolve-by-slug |
Resolve one or more slugs to canonical_match_id and teams |
slug or slugs |
prediction |
Fetch base model probabilities, enabled signals, market probabilities, and 24h volume | cmid |
news-insights |
Fetch AI event cards related to the match | cmid |
recompute-final |
Recompute final probabilities with user-edited signals | cmid |
master-analysis |
Fetch localized AI master analysis | cmid |
market-detail-by-slug |
Fetch prediction-market topic/outcome details before trading | slug |
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 11d ago First seen · 115 lines · 60 tokens per session scan A 944cf5fc0fba
binance-sports-ai-analyzer is a skill published in the GitHub repository ccxt/ccxt (43,945 stars, last pushed today), licensed MIT. It adds 60 tokens to every session and 1,930 once invoked, about $0.0003 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 2026-08-30.
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