[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-mnemox-ai-strategy-validator-en":3,"guides-for-mnemox-ai-strategy-validator":395,"similar-k174qxt9na1secw6w4brrbw96s86m6n8-en":396},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":242,"isFallback":237,"parentExtension":247,"providers":248,"relations":253,"repo":255,"tags":391,"workflow":392},1778693539593.1848,"k174qxt9na1secw6w4brrbw96s86m6n8",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Validate trading strategies for overfitting using 4 statistical tests (DSR, Walk-Forward, Regime, CPCV)",{"claudeCode":12},"mnemox-ai/tradememory-protocol","strategy-validator","https://github.com/mnemox-ai/tradememory-protocol",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":225,"workflow":240},1778693588611.843,"kn7frm8qbjb1tv3tkf833n5gjh86ncp6","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":205,"promptVersionScoring":206,"purpose":207,"rationale":208,"score":209,"summary":210,"tags":211,"targetMarket":218,"tier":219,"useCases":220},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of validating trading strategies for overfitting using specific statistical tests.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers a unique capability beyond basic LLM functions by implementing specific statistical tests for strategy validation and providing a structured workflow.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill appears production-ready, with a clear workflow, defined inputs/outputs, and a callable MCP tool for strategy validation and report generation.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The extension focuses solely on validating trading strategies for overfitting, maintaining a single, well-defined domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately and concisely reflects the skill's purpose of validating trading strategies for overfitting using four statistical tests.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill utilizes a single, well-scoped tool `validate_strategy` with specific parameters, avoiding generalist command execution.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","All parameters for the `validate_strategy` tool and inputs for the skill itself are clearly defined in the SKILL.md.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","The single tool `validate_strategy` is descriptively named and within the declared domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The `validate_strategy` tool's inputs are clearly defined, and the output structure is documented, returning only the necessary validation results and statistics.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the MIT license, which is permissive and clearly stated in the LICENSE file.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on April 10, 2026, which is well within the last 3 months.",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","The extension does not appear to use any third-party dependencies beyond the tradememory-protocol itself, which is managed by pip.",{"category":66,"check":67,"severity":63,"summary":68},"Security","Secret Management","The skill does not handle any secrets.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill's workflow clearly distinguishes between user inputs (file paths, strategy names) and executable code, treating inputs as data.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill does not fetch external content at runtime; all necessary logic and data handling are within the provided bundle.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill operates locally, interacting with local files via absolute paths provided by the user, and does not attempt to modify files outside its intended scope.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were detected in the skill's logic.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill only processes local files and returns analysis results; there are no outbound calls or references to confidential data.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","Bundled content is free of hidden-steering tricks, and descriptions are clean printable ASCII.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The skill's logic is presented in plain Python and Bash, with no obfuscation or runtime code fetching.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The skill relies on user-provided absolute file paths, avoiding assumptions about project structure.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","There are 0 open and 0 closed issues in the last 90 days, indicating no recent activity but also no unresolved issues.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","A meaningful version (MIT license and project structure) is present, and the last commit is recent.",{"category":104,"check":105,"severity":24,"summary":106},"Code Execution","Validation","Input validation for file paths and formats is handled by the `validate_strategy` tool, and the output is structured.",{"category":66,"check":108,"severity":63,"summary":109},"Unguarded Destructive Operations","The skill is purely analytical and does not perform any destructive operations.",{"category":104,"check":111,"severity":24,"summary":112},"Error Handling","The SKILL.md specifies that errors from the `validate_strategy` tool should be explained to the user, and common issues are outlined for remediation.",{"category":104,"check":114,"severity":63,"summary":115},"Logging","The skill is analytical and does not perform destructive actions or outbound calls that would require local audit logging.",{"category":117,"check":118,"severity":63,"summary":119},"Compliance","GDPR","The skill operates on user-provided strategy data and does not handle personal data.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The skill is a generic statistical analysis tool with no regional or legal jurisdictional logic; targetMarket is 'global'.",{"category":92,"check":124,"severity":24,"summary":125},"Runtime stability","The skill uses standard Python and Bash, with a clear dependency on the `tradememory-protocol` package, and is expected to run on POSIX-like systems.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README provides a good overview of the TradeMemory protocol, but the SKILL.md is the primary source for the Strategy Validator's specific function.",{"category":33,"check":130,"severity":63,"summary":131},"Tool surface size","This is a single-tool extension.",{"category":40,"check":133,"severity":63,"summary":134},"Overlapping near-synonym tools","The extension only exposes one tool, so there are no overlapping synonyms.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, such as the 4 statistical tests and HTML report generation, are implemented and documented.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","The README provides clear installation instructions for `pip install tradememory-protocol` and configuration for Claude Desktop, with an example of how to invoke a related memory function.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","The SKILL.md details how errors from the `validate_strategy` tool should be explained to the user, including common issues and remediation steps.",{"category":147,"check":148,"severity":24,"summary":149},"Execution","Pinned dependencies","The extension relies on `tradememory-protocol` which can be pinned via pip, and the provided Python script has a shebang and clear interpreter declaration.",{"category":33,"check":151,"severity":63,"summary":152},"Dry-run preview","The extension is purely analytical and does not perform any state-changing operations or outbound data sending.",{"category":154,"check":155,"severity":63,"summary":156},"Protocol","Idempotent retry & timeouts","The extension does not perform remote calls or state-changing operations that would require idempotency or timeouts.",{"category":117,"check":158,"severity":63,"summary":159},"Telemetry opt-in","The extension does not emit any telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md clearly defines the purpose (validate trading strategies for overfitting using 4 statistical tests) and provides explicit workflow steps and usage examples.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and clearly states the skill's core capability and purpose.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is well-structured, under 500 lines, and delegates deeper material to the `references/` directory (though not explicitly used here, the structure is sound).",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines the workflow and delegates detailed explanations of the tests to sections within the same file, adhering to progressive disclosure principles.",{"category":170,"check":174,"severity":63,"summary":175},"Forked exploration","The skill is short-form and does not involve deep exploration or code review that would necessitate `context: fork`.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides a detailed example conversation demonstrating user input, tool invocation, and expected output interpretation.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md documents failure modes for the `validate_strategy` tool, such as wrong path or format, and suggests recovery steps.",{"category":104,"check":183,"severity":63,"summary":184},"Tool Fallback","The skill uses Claude-internal tools and does not rely on an external MCP server with a fallback.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The SKILL.md instructs the agent to explain errors and help the user fix them, implying a halt on unexpected states rather than proceeding destructively.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and does not implicitly rely on other skills, with clear instructions for its specific task.",1778693588501,"This skill validates trading strategies for overfitting using four statistical tests (DSR, Walk-Forward, Regime, CPCV) by calling an MCP tool. It interprets the results, explains them to the user in financial analyst terms, generates an HTML report, and provides actionable recommendations.",[195,196,197,198,199],"Validates trading strategies using DSR, Walk-Forward, Regime, and CPCV tests.","Interprets complex statistical results into plain language explanations.","Generates a detailed HTML report of the validation analysis.","Provides actionable recommendations based on the validation outcome.","Supports both QuantConnect trade log and general returns CSV formats.",[201,202,203,204],"Providing financial advice or specific buy/sell/hold recommendations.","Executing trades or managing trading accounts.","Performing live trading based on strategy validation.","Explaining the underlying mathematical formulas of the statistical tests in detail.","3.0.0","4.4.0","To help traders objectively assess the statistical robustness of their trading strategies and avoid overfitting by providing rigorous validation and clear explanations.","The skill is exceptionally well-documented and robust, with a clear purpose, meticulous workflow, and comprehensive error handling. No warnings or criticals were found.",99,"A high-quality skill for validating trading strategies against overfitting using rigorous statistical tests.",[212,213,214,215,216,217],"trading","finance","strategy","validation","overfitting","statistics","global","verified",[221,222,223,224],"Use when you have backtest results for a trading strategy and want to know if they are statistically sound or likely overfitted.","Use to get a second opinion on strategy performance from a quantitative analyst's perspective.","Use to identify strategies that perform well only in specific market conditions or are sensitive to data variations.","Use to gain confidence in a strategy before paper trading or live deployment.",{"codeQuality":226,"collectedAt":228,"documentation":229,"maintenance":232,"security":236,"testCoverage":239},{"hasLockfile":227},true,1778693570256,{"descriptionLength":230,"readmeSize":231},103,10941,{"closedIssues90d":8,"forks":233,"hasChangelog":227,"openIssues90d":8,"pushedAt":234,"stars":235},116,1775836242000,877,{"hasNpmPackage":237,"license":238,"smitheryVerified":237},false,"MIT",{"hasCi":227,"hasTests":227},{"updatedAt":241},1778693588611,{"basePath":243,"githubOwner":244,"githubRepo":245,"locale":18,"slug":13,"type":246},".skills/strategy-validator","mnemox-ai","tradememory-protocol","skill",null,{"evaluate":249,"extract":251},{"promptVersionExtension":205,"promptVersionScoring":206,"score":209,"tags":250,"targetMarket":218,"tier":219},[212,213,214,215,216,217],{"commitSha":252},"HEAD",{"repoId":254},"kd73z11kfekksxyrs8ds0snacs86ncdy",{"_creationTime":256,"_id":254,"identity":257,"providers":258,"workflow":387},1778693533831.6553,{"githubOwner":244,"githubRepo":245,"sourceUrl":14},{"classify":259,"discover":372,"github":375},{"commitSha":252,"extensions":260},[261,295,301,313,321,329,337,345,353],{"basePath":262,"description":263,"displayName":264,"installMethods":265,"rationale":266,"selectedPaths":267,"source":293,"sourceLanguage":18,"type":294},"tradememory-plugin","Persistent memory + autonomous strategy evolution for AI traders. 200+ trading MCP servers execute. None remember. TradeMemory does.","tradememory",{"claudeCode":264},"plugin manifest at tradememory-plugin/.claude-plugin/plugin.json",[268,271,273,276,278,280,282,285,287,289,291],{"path":269,"priority":270},".claude-plugin/plugin.json","mandatory",{"path":272,"priority":270},"README.md",{"path":274,"priority":275},"skills/evolution-engine/SKILL.md","medium",{"path":277,"priority":275},"skills/risk-management/SKILL.md",{"path":279,"priority":275},"skills/trading-memory/SKILL.md",{"path":281,"priority":270},".mcp.json",{"path":283,"priority":284},"commands/daily-review.md","high",{"path":286,"priority":284},"commands/evolve.md",{"path":288,"priority":284},"commands/performance.md",{"path":290,"priority":284},"commands/recall.md",{"path":292,"priority":284},"commands/record-trade.md","rule","plugin",{"basePath":243,"description":10,"displayName":13,"installMethods":296,"rationale":297,"selectedPaths":298,"source":293,"sourceLanguage":18,"type":246},{"claudeCode":12},"SKILL.md frontmatter at .skills/strategy-validator/SKILL.md",[299],{"path":300,"priority":270},"SKILL.md",{"basePath":302,"description":303,"displayName":264,"installMethods":304,"rationale":305,"selectedPaths":306,"source":293,"sourceLanguage":18,"type":246},".skills/tradememory","AI trading memory with outcome-weighted recall and autonomous strategy evolution. 17 MCP tools, 1,233 tests, works with any trading platform.",{"claudeCode":12},"SKILL.md frontmatter at .skills/tradememory/SKILL.md",[307,308,311],{"path":300,"priority":270},{"path":309,"priority":310},"scripts/install.sh","low",{"path":312,"priority":310},"scripts/setup_mt5.sh",{"basePath":314,"description":315,"displayName":316,"installMethods":317,"rationale":318,"selectedPaths":319,"source":293,"sourceLanguage":18,"type":246},"skills/binance-skills-hub/trade-memory","Compliance-grade decision audit trail for AI trading agents. Records every trading decision with full context (conditions, filters, indicators, risk state), SHA-256 tamper detection, and structured export for MiFID II / EU AI Act readiness. Works alongside Binance Spot, Futures, and Web3 skills — they execute trades, TradeMemory records why.","trade-memory",{"claudeCode":12},"SKILL.md frontmatter at skills/binance-skills-hub/trade-memory/SKILL.md",[320],{"path":300,"priority":270},{"basePath":322,"description":323,"displayName":324,"installMethods":325,"rationale":326,"selectedPaths":327,"source":293,"sourceLanguage":18,"type":246},"skills/tradememory-bridge","Bridge between Binance trading events and TradeMemory Protocol.\nAutomatically journals trades, recalls similar past setups, detects behavioral biases,\nand provides outcome-weighted recall for AI trading agents.\nUse this skill after executing Binance spot trades to build persistent memory.\n","tradememory-bridge",{"claudeCode":12},"SKILL.md frontmatter at skills/tradememory-bridge/SKILL.md",[328],{"path":300,"priority":270},{"basePath":330,"description":331,"displayName":332,"installMethods":333,"rationale":334,"selectedPaths":335,"source":293,"sourceLanguage":18,"type":246},"tradememory-plugin/skills/evolution-engine","Domain knowledge for the Evolution Engine — LLM-powered autonomous strategy discovery from raw OHLCV data. Covers the generate-backtest-select-evolve loop, vectorized backtesting, out-of-sample validation, and strategy graduation. Use when discovering trading patterns, running backtests, evolving strategies, or reviewing evolution logs. Triggers on \"evolve\", \"discover patterns\", \"backtest\", \"evolution\", \"strategy generation\", \"candidate strategy\".","evolution-engine",{"claudeCode":12},"SKILL.md frontmatter at tradememory-plugin/skills/evolution-engine/SKILL.md",[336],{"path":300,"priority":270},{"basePath":338,"description":339,"displayName":340,"installMethods":341,"rationale":342,"selectedPaths":343,"source":293,"sourceLanguage":18,"type":246},"tradememory-plugin/skills/risk-management","Risk management domain knowledge for trading agents — affective state monitoring, position sizing, drawdown management, tilt detection, and behavioral guardrails. Use when checking risk before trades, managing drawdowns, detecting behavioral drift, or enforcing discipline. Triggers on \"risk\", \"drawdown\", \"tilt\", \"position size\", \"lot size\", \"confidence\", \"revenge trading\", \"overtrading\", \"discipline\".","risk-management",{"claudeCode":12},"SKILL.md frontmatter at tradememory-plugin/skills/risk-management/SKILL.md",[344],{"path":300,"priority":270},{"basePath":346,"description":347,"displayName":348,"installMethods":349,"rationale":350,"selectedPaths":351,"source":293,"sourceLanguage":18,"type":246},"tradememory-plugin/skills/trading-memory","Domain knowledge for AI trading memory — Outcome-Weighted Memory (OWM) architecture, 5 memory types, recall scoring, and behavioral analysis. Use when recording trades, recalling similar contexts, analyzing performance, or checking behavioral drift. Triggers on \"record trade\", \"remember trade\", \"recall\", \"similar trades\", \"performance\", \"behavioral\", \"disposition\", \"affective state\", \"confidence\".","trading-memory",{"claudeCode":12},"SKILL.md frontmatter at tradememory-plugin/skills/trading-memory/SKILL.md",[352],{"path":300,"priority":270},{"basePath":354,"displayName":245,"installMethods":355,"rationale":356,"selectedPaths":357,"source":293,"sourceLanguage":18,"type":371},"",{"pypi":245},"server.json with namespace/server name at server.json",[358,360,362,363,365,367,369],{"path":359,"priority":270},"server.json",{"path":361,"priority":270},"pyproject.toml",{"path":272,"priority":270},{"path":364,"priority":284},"LICENSE",{"path":366,"priority":275},"src/tradememory/cli.py",{"path":368,"priority":275},"src/tradememory/server.py",{"path":370,"priority":310},"hosted/server.py","mcp",{"sources":373},[374],"manual",{"closedIssues90d":8,"description":376,"forks":233,"homepage":377,"license":238,"openIssues90d":8,"pushedAt":234,"readmeSize":231,"stars":235,"topics":378},"Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.","https://mnemox.ai/tradememory/",[379,380,371,381,382,212,383,384,332,385,386],"claude","forex","memory","mt5","ai-agents","crypto","mcp-server","outcome-weighted-memory",{"classifiedAt":388,"discoverAt":389,"extractAt":390,"githubAt":390,"updatedAt":388},1778693539413,1778693533831,1778693537570,[213,216,217,214,212,215],{"evaluatedAt":241,"extractAt":393,"updatedAt":394},1778693539593,1778693831914,[],[397,428,446,465,494,515],{"_creationTime":398,"_id":399,"community":400,"display":401,"identity":407,"providers":412,"relations":421,"tags":424,"workflow":425},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":402,"installMethods":403,"name":405,"sourceUrl":406},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":404},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":408,"githubOwner":409,"githubRepo":410,"locale":18,"slug":411,"type":246},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":413,"extract":420},{"promptVersionExtension":205,"promptVersionScoring":206,"score":414,"tags":415,"targetMarket":218,"tier":219},100,[213,212,416,417,418,419],"market-analysis","ai","typescript","cli",{"commitSha":252,"license":238},{"parentExtensionId":422,"repoId":423},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[417,419,213,416,212,418],{"evaluatedAt":426,"extractAt":427,"updatedAt":426},1778701108877,1778696691708,{"_creationTime":429,"_id":430,"community":431,"display":432,"identity":434,"providers":435,"relations":440,"tags":442,"workflow":443},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":347,"installMethods":433,"name":348,"sourceUrl":14},{"claudeCode":12},{"basePath":346,"githubOwner":244,"githubRepo":245,"locale":18,"slug":348,"type":246},{"evaluate":436,"extract":439},{"promptVersionExtension":205,"promptVersionScoring":206,"score":414,"tags":437,"targetMarket":218,"tier":219},[212,417,381,213,438],"python",{"commitSha":252},{"parentExtensionId":441,"repoId":254},"k170vxkqee48k2xq1v55a025nh86nzn7",[417,213,381,438,212],{"evaluatedAt":444,"extractAt":393,"updatedAt":445},1778693719816,1778693833320,{"_creationTime":447,"_id":448,"community":449,"display":450,"identity":453,"providers":454,"relations":460,"tags":461,"workflow":462},1778693539593.186,"k17bgwvhb6h29py715de1cm9xd86msq6",{"reviewCount":8},{"description":339,"installMethods":451,"name":452,"sourceUrl":14},{"claudeCode":12},"Risk Management",{"basePath":338,"githubOwner":244,"githubRepo":245,"locale":18,"slug":340,"type":246},{"evaluate":455,"extract":459},{"promptVersionExtension":205,"promptVersionScoring":206,"score":414,"tags":456,"targetMarket":218,"tier":219},[212,340,457,458,213],"ai-agent","behavioral-analysis",{"commitSha":252,"license":238},{"parentExtensionId":441,"repoId":254},[457,458,213,340,212],{"evaluatedAt":463,"extractAt":393,"updatedAt":464},1778693700524,1778693833120,{"_creationTime":466,"_id":467,"community":468,"display":469,"identity":475,"providers":479,"relations":488,"tags":490,"workflow":491},1778688112811.7527,"k17enr6rktmxh0enswrmze6et186mq12",{"reviewCount":8},{"description":470,"installMethods":471,"name":473,"sourceUrl":474},"Model best-case, worst-case, and likely revenue scenarios with sensitivity analysis for strategic planning. Use when: building financial forecasts; presenting board scenarios; planning headcount around revenue uncertainty; modeling pricing changes impact; preparing investor updates with upside/downside ranges",{"claudeCode":472},"guia-matthieu/clawfu-skills","forecast-scenarios","https://github.com/guia-matthieu/clawfu-skills",{"basePath":476,"githubOwner":477,"githubRepo":478,"locale":18,"slug":473,"type":246},"skills/revops/forecast-scenarios","guia-matthieu","clawfu-skills",{"evaluate":480,"extract":487},{"promptVersionExtension":205,"promptVersionScoring":206,"score":414,"tags":481,"targetMarket":218,"tier":219},[213,482,483,484,214,485,486],"forecasting","revenue","planning","sensitivity-analysis","mckinsey",{"commitSha":252},{"repoId":489},"kd72qvzyvm658ya7pbyh5ey47h86md53",[213,482,486,484,483,485,214],{"evaluatedAt":492,"extractAt":493,"updatedAt":492},1778690475880,1778688112811,{"_creationTime":495,"_id":496,"community":497,"display":498,"identity":502,"providers":504,"relations":511,"tags":512,"workflow":513},1778696691708.328,"k172nv5vbyw1c60vavz8f9esw186m2q7",{"reviewCount":8},{"description":499,"installMethods":500,"name":501,"sourceUrl":406},"Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction",{"claudeCode":404},"trader-signal",{"basePath":503,"githubOwner":409,"githubRepo":410,"locale":18,"slug":501,"type":246},"plugins/ruflo-neural-trader/skills/trader-signal",{"evaluate":505,"extract":510},{"promptVersionExtension":205,"promptVersionScoring":206,"score":209,"tags":506,"targetMarket":218,"tier":219},[212,213,507,508,509],"anomaly-detection","machine-learning","prediction",{"commitSha":252},{"parentExtensionId":422,"repoId":423},[507,213,508,509,212],{"evaluatedAt":514,"extractAt":427,"updatedAt":514},1778701148958,{"_creationTime":516,"_id":517,"community":518,"display":519,"identity":521,"providers":522,"relations":527,"tags":528,"workflow":529},1778693539593.1855,"k17em57x7pnqhv6x3a2s5g5wv586mjq6",{"reviewCount":8},{"description":323,"installMethods":520,"name":324,"sourceUrl":14},{"claudeCode":12},{"basePath":322,"githubOwner":244,"githubRepo":245,"locale":18,"slug":324,"type":246},{"evaluate":523,"extract":526},{"promptVersionExtension":205,"promptVersionScoring":206,"score":209,"tags":524,"targetMarket":218,"tier":219},[212,213,381,525,457],"journaling",{"commitSha":252},{"repoId":254},[457,213,525,381,212],{"evaluatedAt":530,"extractAt":393,"updatedAt":531},1778693660212,1778693832747]