Ruflo is an execution and coordination layer for Claude Code and Codex that equips AI coding agents with tools, memory, control loops, sandboxes, and collaboration mechanisms. Developers use it to organize specialized agents into swarms, coordinate workflows, retain knowledge across sessions, and communicate across machines. The catalogue entries are Ruflo’s skills, commands, agents, hooks, and plugin components.
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 ruvnet/ruflo --skill agent-trading-predictorgit clone --depth 1 https://github.com/ruvnet/rufloWrote 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/ruvnet/ruflo/agent-trading-predictor)<a href="https://agentmods.dev/skills/ruvnet/ruflo/agent-trading-predictor"><img src="https://agentmods.dev/badge/skills/ruvnet/ruflo/agent-trading-predictor.svg" alt="Measured on agentmods" 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 | $0.00012 | $0.01026 |
| Sonnet 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 3d 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.
Copies of this mod
3 near-identical copies found in the catalogue:
- agent-trading-predictor — 100% identical, 0 lines differ
- agent-trading-predictor — 100% identical, 0 lines differ
- agent-trading-predictor — 100% identical, 0 lines differ
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.
- 3d ago First seen · 251 lines · 24 tokens per session scan A a1accd52f7fc
agent-trading-predictor is a skill published in the GitHub repository ruvnet/ruflo (70,734 stars, last pushed yesterday), licensed MIT. It adds 24 tokens to every session and 2,053 once invoked, about $0.0001 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 2026-09-03.
Other skills, from other repositories
compliance-frameworks
Swiss financial regulatory compliance — FINMA, GwG/AML/KYC, FIDLEG/FINIG licensing, BankG, crypto/DLT regulation, cross-border obligations (AEOI/CRS, FATCA). Trigger when: user asks about FINMA licensing, AML/KYC compliance, financial services cross-border, crypto regulation, or fintech data processing. Do NOT trigger…
compare-harnesses
Diff two scaffolded harnesses (ADR-031). Reports manifest meta drift + host list + per-file fingerprint changes (added/removed/changed). Exits 0 IDENTICAL, 1 DRIFT, 2 missing manifest. Use --bundle for the ADR-031 schema-1 JSON envelope.
diag-harness
Kernel-version skew check (ADR-027). Reports manifest surface + manifest kernel + installed kernel + verdict (match/patch-diff/minor-diff/major-diff). Exits 1 on minor/major skew with a copy-pasteable npm install @metaharness/[email protected] next step. Exits 2 if no .harness/manifest.json at path.
repo-genome
7-section readiness scorecard for a LOCAL repo. Reports repo type + agent topology + MCP risk + test confidence + release readiness + recommended harness plan + scorecard. Exit 0 ready, 1 needs-work, 2 blocked. --json for the 6-field scorecard, --bundle for the ADR-031 schema-1 envelope.
score-harness
5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.
upgrade-harness
Drift detection + apply for a scaffolded harness. Re-renders the template with the same vars, computes added/removed/changed file plan, and applies with Git-style conflict markers or .rej files. Default is dry-run.