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 Superior-Trade/superior-skills --skill trade-thesisgit clone --depth 1 https://github.com/Superior-Trade/superior-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/superior-trade/superior-skills/trade-thesis)<a href="https://agentmods.dev/skills/superior-trade/superior-skills/trade-thesis"><img src="https://agentmods.dev/badge/skills/superior-trade/superior-skills/trade-thesis/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/superior-trade/superior-skills/trade-thesis"><img src="https://agentmods.dev/badge/skills/superior-trade/superior-skills/trade-thesis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- 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 | $0.00069 | $0.01906 |
| Opus 5 | $0.00034 | $0.00953 |
| Sonnet 5 | $0.00014 | $0.00381 |
| Haiku 4.5 | $0.00007 | $0.00191 |
Grade A, and why
trade-thesis 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 — 130 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Pre-Trade Thesis Builder
A structured framework the agent produces before any live deployment of a new strategy idea. Forces two-sided analysis and a measurable invalidation level so every trade has a thesis that can be killed.
When to produce a thesis
- The user proposes a new trade idea ("I want to long ETH", "thinking about a funding squeeze on SOL")
- The user asks "should I trade X" or "is this a good setup"
- The agent is about to deploy a new strategy to live for the first time
- The user explicitly asks for a bull/bear case, conviction check, or pre-trade analysis
When to skip
- Backtests and parameter sweeps — the thesis is for live capital, not for exploring an idea
- Dry-run / paper deployments — no real money at risk
- Copy-trading — the thesis belongs to the source trader, not the copier
- Re-deploying a previously validated strategy — if the user already ran this setup live and is restarting it, skip unless market conditions changed materially
Thesis format
Produce all six sections below. Each section is 2-4 bullet points max. This is a pre-trade checklist, not a research paper.
1. Thesis statement
One sentence: what is the bet, which direction, what timeframe.
Force clarity. "Long ETH perp on a funding squeeze setup, targeting a 3-5% move over 24-48h" is a thesis. "ETH looks bullish" is not.
2. Bull case
2-3 concrete reasons this trade works. Every reason must reference observable, checkable data:
- Price action the agent can verify via
price_check(e.g., "price reclaimed the 4h EMA-50 and is holding above") - Volume dynamics via
volume_momentum_check(e.g., "RVOL is 2.3x on the breakout candle") - Funding / OI via the alpha scan (e.g., "funding APR is -18%, shorts are paying 0.002% per hour")
- On-chain flows if available (e.g., "exchange outflows spiked 3x over 24h")
No vague claims like "market sentiment is improving" or "BTC looks strong." If it cannot be checked with a tool call, it does not belong in the bull case.
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 · 130 lines · 69 tokens per session scan A 9ab281b03f7f
trade-thesis is a skill published in the GitHub repository Superior-Trade/superior-skills (209 stars, last pushed 2d ago), licensed MIT. It adds 69 tokens to every session and 1,906 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
tushare
A Python interface for Tushare, a financial data service that provides market and company information for stocks, funds, futures, and digital assets. It returns queried data as pandas tables.
ashare-pre-st-filter
An A-share China stock risk checker that forecasts whether a company may receive an ST or *ST warning in the next financial year. ST labels are Chinese exchange warnings for companies facing specified financial or regulatory problems.
correlation-analysis
Correlation and cointegration analysis — co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation.
credit-analysis
A guide to analysing bonds and other fixed-income investments, including issuer credit quality, interest payments, default risk, credit spreads, and convertible bonds. It also covers Chinese fixed-income markets and local-government financing bonds.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
vibe-trading
Professional finance research toolkit — backtesting (10 engines + benchmark comparison panel), factor analysis, Alpha Zoo (462 pre-built alphas across qlib158/alpha101/gtja191/academic/fundamental), options pricing, 90 finance skills, 30 multi-agent swarm teams, Trade Journal analyzer, and Shadow Account (extract →…