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 varrdinc/varrd --skill varrd-hypothesisgit clone --depth 1 https://github.com/varrdinc/varrdWrote 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/varrdinc/varrd/varrd-hypothesis)<a href="https://agentmods.dev/skills/varrdinc/varrd/varrd-hypothesis"><img src="https://agentmods.dev/badge/skills/varrdinc/varrd/varrd-hypothesis/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/varrdinc/varrd/varrd-hypothesis"><img src="https://agentmods.dev/badge/skills/varrdinc/varrd/varrd-hypothesis.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 | $0.00037 | $0.00548 |
| Opus 5.5 | $0.00015 | $0.00219 |
| Sonnet 5.5 | $0.00007 | $0.00110 |
| Haiku 4.5 | $0.00004 | $0.00055 |
Grade A, and why
varrd-hypothesis 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 1mo 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
VARRD Hypothesis — Strategy Details
Use this skill when a user wants to see full details on a specific strategy from their library.
Command
varrd hypothesis <hypothesis_id>
How It Works
Returns complete details for a saved strategy: formula, setup code, statistical metrics, version history, and horizon results.
Example
varrd hypothesis hyp_abc123
Reading the Output
| Field | Description |
|---|---|
| Name | Strategy name (e.g. "RSI Oversold ES Daily") |
| Formula | The boolean pattern expression |
| Market | Primary market tested on |
| Direction | LONG or SHORT |
| Edge verdict | STRONG EDGE, MARGINAL, or NO EDGE |
| Win rate | Historical win percentage |
| Sharpe ratio | Risk-adjusted return metric |
| Profit factor | Gross profits / gross losses |
| Total trades | Number of historical signals |
| Horizon results | Per-horizon stats (1, 3, 5, 10, 20 bars) |
| Versions | Formula revision history |
Important: Trade Levels May Be Stale
Trade levels from varrd hypothesis are from when the strategy was last tested. They may be outdated. For fresh current prices:
- Use
varrd scan— if the strategy is firing, you get fresh levels - Use
varrd research "Load hypothesis <id>"then "show me the trade setup" — forces fresh calculation
Tips
- Get hypothesis IDs from
varrd scanorvarrd searchresults - This command is free — no credits consumed
- Use this to understand a strategy before acting on scan signals
Cost
Free. No credits consumed.
Python SDK Equivalent
from varrd import VARRD
v = VARRD()
hyp = v.get_hypothesis("hyp_abc123")
print(f"{hyp.name}: {hyp.win_rate:.0%} WR, {hyp.sharpe:.2f} Sharpe")
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.
- 1mo ago First seen · 71 lines · 37 tokens per session scan A 2b93ce230d44
varrd-hypothesis is a skill published in the GitHub repository varrdinc/varrd (24 stars, last pushed 1mo ago), licensed MIT. It adds 37 tokens to every session and 548 once invoked, about $0.0001 per session on Opus 5.5. 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.
social-media-intelligence
Social media intelligence: financial signal extraction from Twitter/X, Telegram, Discord, and Reddit for sentiment-driven trading strategies.
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.
geopolitical-risk
Geopolitical risk analysis: quantify crisis signals, identify precursors, and build event-driven strategies for war, sanctions, and supply disruption scenarios.