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-searchgit 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-search)<a href="https://agentmods.dev/skills/varrdinc/varrd/varrd-search"><img src="https://agentmods.dev/badge/skills/varrdinc/varrd/varrd-search/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-search"><img src="https://agentmods.dev/badge/skills/varrdinc/varrd/varrd-search.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 | $0.00029 | $0.00494 |
| Opus 5.5 | $0.00012 | $0.00198 |
| Sonnet 5.5 | $0.00006 | $0.00099 |
| Haiku 4.5 | $0.00003 | $0.00049 |
Grade A, and why
varrd-search 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 Search — Find Saved Strategies
Use this skill when a user wants to find strategies in their library by topic, keyword, or market.
Command
varrd search "<query>"
varrd search "RSI oversold" --market ES
varrd search "momentum strategies" --limit 5
How It Works
Searches all saved strategies using keyword and semantic matching. Returns matches ranked by relevance with key stats (win rate, Sharpe, edge status).
Options
| Flag | Description |
|---|---|
--market ES |
Filter results to a specific market |
--limit N |
Max results to return (default 10) |
Examples
varrd search "momentum strategies"
varrd search "RSI oversold"
varrd search "corn seasonal"
varrd search "mean reversion" --market ES
varrd search "volatility" --limit 20
Reading the Output
Each result includes:
- Strategy name and hypothesis ID
- Formula — the pattern expression
- Market and direction
- Edge status — whether a validated edge was found
- Win rate and Sharpe ratio
- Similarity score — how closely it matches your query
Tips
- Use natural language: "strategies that work on crude oil" works just as well as "CL"
- Use the hypothesis ID from results with
varrd hypothesis <id>for full details - Search is free — no credits consumed
Cost
Free. No credits consumed.
Python SDK Equivalent
from varrd import VARRD
v = VARRD()
results = v.search("momentum on grains", limit=5)
for r in results.results:
print(f"{r.name} ({r.market}) — edge: {r.has_edge}, WR: {r.win_rate}")
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 · 72 lines · 29 tokens per session scan A 4b1aca8ca31a
varrd-search is a skill published in the GitHub repository varrdinc/varrd (24 stars, last pushed 1mo ago), licensed MIT. It adds 29 tokens to every session and 494 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.