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 agiprolabs/claude-trading-skills --skill kalshi-crypto-index-marketsgit clone --depth 1 https://github.com/agiprolabs/claude-trading-skillsWrote 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/agiprolabs/claude-trading-skills/kalshi-crypto-index-markets)<a href="https://agentmods.dev/skills/agiprolabs/claude-trading-skills/kalshi-crypto-index-markets"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/kalshi-crypto-index-markets/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/agiprolabs/claude-trading-skills/kalshi-crypto-index-markets"><img src="https://agentmods.dev/badge/skills/agiprolabs/claude-trading-skills/kalshi-crypto-index-markets.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00065 | $0.01951 |
| Opus 5 | $0.00032 | $0.00975 |
| Sonnet 5 | $0.00013 | $0.00390 |
| Haiku 4.5 | $0.00006 | $0.00195 |
Grade A, and why
kalshi-crypto-index-markets 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 13d 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 — 146 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Kalshi Crypto & Index Range Markets
Kalshi lists daily (and for crypto, hourly) range bracket markets on the level of four liquid underlyings: S&P 500, Nasdaq-100, Bitcoin, and Ethereum. The math is the same partition-and-Gaussian framework as weather brackets — the variable is just price/return and σ comes from the underlying's realized or implied volatility, not a temperature model.
Cross-references: for Kalshi API mechanics see
kalshi-api; for the strategy, sizing, and backtesting framework seeprediction-market-strategy; for the temperature counterpart seekalshi-weather-markets; for the shared bracket/overround formulas seeprediction-markets/references/brackets-and-settlement.md.
Series & Market-Type Mapping
SERIES_MARKET = {
"KXINX": "index", # S&P 500 index level
"KXNASDAQ100": "index", # Nasdaq-100 index level
"KXBTC": "crypto", # Bitcoin price (USD)
"KXETH": "crypto", # Ethereum price (USD)
}
market_type = "index"— daily range only; underlying is the index points level at the official market close.market_type = "crypto"— daily and hourly range markets; BTC and ETH each have multiple hourly events running in parallel with the daily.
Cadence is higher than weather: crypto hourlies open and settle throughout the day; daily markets open the prior session and settle at the reference close.
Bracket Structure
Each event is a mutually exclusive, collectively exhaustive partition of the underlying's possible values at settlement:
- An interior bracket
B<center>covers a contiguous price band (floor to cap, both-ends-inclusive). Bracket widths are set per-market — read the event's market list; do not assume a fixed width. - Two open-tail markets anchor the partition: a
less-than(below the lowest bracket floor) and agreater-than(above the highest bracket cap). - At settlement exactly one bracket is YES; all others are NO.
The overround (sum of all YES prices) is typically > 1.0. The excess is concentrated in the cheap tails — the same favorite–longshot bias seen in weather markets. See prediction-markets/references/brackets-and-settlement.md for the overround formula.
What ships with it
1 file 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.
- 13d ago First seen · 146 lines · 65 tokens per session scan A 822a1967bd3e
kalshi-crypto-index-markets is a skill published in the GitHub repository agiprolabs/claude-trading-skills (356 stars, last pushed 9d ago), licensed MIT. It adds 65 tokens to every session and 1,951 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.
Other skills, from other repositories
sector-rotation
An analysis framework for comparing industries in the Chinese A-share stock market, using business conditions, price momentum, valuation, and money flows. It produces rankings and higher- or lower-allocation suggestions.
strategy-pivot-designer
Detect backtest iteration stagnation and generate structurally different strategy pivot proposals when parameter tuning reaches a local optimum.
twitter-reader
Read Twitter/X for financial research using opencli (read-only). Use this skill whenever the user wants to read their Twitter feed, search for financial tweets, view bookmarks, look up user profiles, or gather market sentiment from Twitter/X. Triggers include: "check my feed", "search Twitter for", "show my…
chenhao-limit-up
A framework for judging Chinese A-share stocks that have reached the daily price-rise limit, using market mood, sector leadership, and trading momentum.
furusato
A Japanese hometown-tax donation manager for furusato nozei, a system where donations to municipalities can qualify for an income-tax or local-tax deduction. It reads donation receipts, stores donation records, and calculates deduction limits.
reading-receipt
An image-reading workflow for extracting structured information from receipts, invoices, and hometown-tax donation certificates. It can first extract text from PDFs and otherwise read their images.