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"Detecting Patterns and Conformations in Trading Charts" matching MCP servers:

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    Pattern-intelligence MCP server for ServiceNow Discovery that enables AI agents to read, search, validate, author, surgically edit, and reason about Discovery patterns, including NDL grammar, SNMP OIDs, data sources, and emulator blueprints.
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    Enables comprehensive security scanning of code projects, detecting vulnerabilities in dependencies, code patterns (XSS, eval, etc.), and exposed secrets, with detailed reports in Spanish prioritized by severity.
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    MCP server for AI security scanning. Detects prompt injections, jailbreaks, data exfiltration, and social engineering attacks in real-time using 42 regex patterns via the ClawGuard Shield API. Sub-10ms response times.
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    Audits Excel workbooks by tracing cell dependencies and detecting circular references, volatile functions, and other formula risks, enabling natural-language audit queries on local files.
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    Godot 4 MCP server with: Test running (GUT/GdUnit4) with structured pass/fail results API docs search with 30+ Godot 3→4 migration mappings Script analysis detecting 10 common GDScript pitfalls Scene/resource file parsing with antipattern detection Viewport screenshot capture LSP diagnostics from Godot's language server TypeScript, cross-platform (macOS/Windows/Linux), published on npm
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    Enables real-time code analysis for JavaScript, TypeScript, and Python through Claude Desktop and other MCP clients, detecting bugs, code smells, and security vulnerabilities with automated quick fixes.
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    An MCP server that audits LLM-as-judge evaluations, detecting judge drift across runs, measuring bias through controlled probes, and comparing judge agreement with human raters.
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    Enables LLMs to systematically develop and validate regex patterns by defining test cases with expected matches, testing patterns against them, and iteratively refining until all requirements are satisfied.
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    Provides tools to test regex patterns for correctness, performance (ReDoS), and memory usage, and suggests safe rewrites. Enables LLMs to iterate on regex generation with verifiable feedback.
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    MIT