Focus Code Skill Marketplace

Skill Introduction

Overview

Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction

Core value

Turns reusable Development know-how into an installable skill, helping users complete github, community, claude, mcp work faster.

Developers, testers, and maintainers who handle Development tasks in Focus Code.
Teams that already trust workflows or content from ruvnet.
Users who want standardized prompts, steps, or conventions instead of repeating setup work.

Read the skill content first to confirm required inputs, expected outputs, and dependencies.
Try it on a small task before relying on it for critical work.
Add project-specific constraints such as coding style, target platform, test expectations, and delivery format.
For external sources, verify the source link, version, and recent maintenance activity.

Tasks related to github, community, claude, mcp that need a reusable execution flow.
Converting a community repo, team convention, or personal workflow into day-to-day assistance.
Starting from a proven skill instead of writing prompts or procedures from scratch.

Results depend on the quality of the original skill content and may need human correction.
It does not replace code review, tests, security review, or professional judgment.
External tools, APIs, account permissions, and local dependencies still need separate setup.

Structured around Development, making it easier to discover and reuse than loose prompt snippets.
Marked as GitHub, which helps users judge trust and maintenance expectations.
Keeps the original source link available for repository, documentation, or discussion follow-up.
Tagged with github, community, claude, mcp, so it can be filtered by concrete task intent.

Detail Preview

Primary filemarkdown3 KB

name: trader-signal description: Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction allowed-tools: Bash Read mcpclaude-flowmemory_store mcpclaude-flowmemory_retrieve mcpclaude-flowmemory_search mcpclaude-flowmemory_delete mcpclaude-flowneural_predict mcpclaude-flowagentdb_pattern-search argument-hint: "[--strategy NAME] [--symbols AAPL,MSFT]"

Generate trading signals using neural-trader's anomaly detection engine.

Steps:

Ensure neural-trader is available: npm ls neural-trader 2>/dev/null || npm install --ignore-scripts neural-trader

Scan for signals:

npx neural-trader --signal scan --symbols <TICKERS>

With a specific strategy:

npx neural-trader --signal scan --strategy <name> --symbols <TICKERS>

If --strategy specified, load strategy filters: mcp__claude-flow__memory_retrieve({ key: "strategy-NAME", namespace: "trading-strategies" })
neural-trader classifies anomalies automatically:
- spike (maxZ > 5): breakout — momentum entry or mean-reversion fade
- drift (sustained high Z): trend forming — trend-following signal
- flatline (low Z): consolidation — prepare for breakout
- oscillation (alternating): range-bound — mean-reversion at extremes
- pattern-break (multiple dims): regime change — close and reassess
- cluster-outlier (>50% dims): multi-factor dislocation — arbitrage
Use SONA for regime prediction: mcp__claude-flow__neural_predict({ input: "anomaly types: [DETECTED], scores: [SCORES]" })
Search historical pattern matches: mcp__claude-flow__agentdb_pattern-search({ query: "ANOMALY_TYPE score RANGE", namespace: "trading-signals" })
Present ranked signals: instrument, direction, confidence, anomaly type, entry/stop/target
Store signals with a 24-hour TTL (intraday signals shouldn't pollute long-running memory; the MemoryConsolidator.sweepExpired() pass introduced in ADR-125 Phase 4 — shipped in @claude-flow/memory@3.0.0-alpha.18 — sweeps them out after they expire): mcp__claude-flow__memory_store({ key: "signal-TIMESTAMP", value: "SIGNALS_JSON", namespace: "trading-signals", expiresAt: Date.now() + 24 * 60 * 60 * 1000 })