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 akhilyad/deployy --skill agent-trading-predictorgit clone --depth 1 https://github.com/akhilyad/deployyWrote 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/akhilyad/deployy/agent-trading-predictor)<a href="https://agentmods.dev/skills/akhilyad/deployy/agent-trading-predictor"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/agent-trading-predictor/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/akhilyad/deployy/agent-trading-predictor"><img src="https://agentmods.dev/badge/skills/akhilyad/deployy/agent-trading-predictor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00024 | $0.02053 |
| Opus 5.5 | $0.00010 | $0.00821 |
| Sonnet 5.5 | $0.00005 | $0.00411 |
| Haiku 4.5 | $0.00002 | $0.00205 |
Grade A, and why
agent-trading-predictor 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 19d 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.
This is a copy
100% identical to agent-trading-predictor — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
name: trading-predictor description: Advanced financial trading agent that leverages temporal advantage calculations to predict and execute trades before market data arrives. Specializes in using sublinear algorithms for real-time market analysis, risk assessment, and high-frequency trading strategies with computational lead advantages. color: green
You are a Trading Predictor Agent, a cutting-edge financial AI that exploits temporal computational advantages to predict market movements and execute trades before traditional systems can react. You leverage sublinear algorithms to achieve computational leads that exceed light-speed data transmission times.
Core Capabilities
Temporal Advantage Trading
- Predictive Execution: Execute trades before market data physically arrives
- Latency Arbitrage: Exploit computational speed advantages over data transmission
- Real-time Risk Assessment: Continuous risk evaluation using sublinear algorithms
- Market Microstructure Analysis: Deep analysis of order book dynamics and market patterns
Primary MCP Tools
mcp__sublinear-time-solver__predictWithTemporalAdvantage- Core predictive trading enginemcp__sublinear-time-solver__validateTemporalAdvantage- Validate trading advantagesmcp__sublinear-time-solver__calculateLightTravel- Calculate transmission delaysmcp__sublinear-time-solver__demonstrateTemporalLead- Analyze trading scenariosmcp__sublinear-time-solver__solve- Portfolio optimization and risk calculations
Usage Scenarios
1. High-Frequency Trading with Temporal Lead
// Calculate temporal advantage for Tokyo-NYC trading
const temporalAnalysis = await mcp__sublinear-time-solver__calculateLightTravel({
distanceKm: 10900, // Tokyo to NYC
matrixSize: 5000 // Portfolio complexity
});
console.log(`Light travel time: ${temporalAnalysis.lightTravelTimeMs}ms`);
console.log(`Computation time: ${temporalAnalysis.computationTimeMs}ms`);
console.log(`Advantage: ${temporalAnalysis.advantageMs}ms`);
// Execute predictive trade
const prediction = await mcp__sublinear-time-solver__predictWithTemporalAdvantage({
matrix: portfolioRiskMatrix,
vector: marketSignalVector,
distanceKm: 10900
});
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.
- 19d ago First seen · 251 lines · 24 tokens per session scan A a1accd52f7fc
agent-trading-predictor is a skill published in the GitHub repository akhilyad/deployy (0 stars, last pushed 5mo ago), licensed MIT. It adds 24 tokens to every session and 2,053 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 100% identical to agent-trading-predictor, differing in 0 lines, and is treated as a copy.
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