[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-risk-en":3,"guides-for-Whatsonyourmind-oraclaw-risk":427,"similar-k17apsy39kxc4xvn8cyg3rebb186nvjt-en":428},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":246,"isFallback":241,"parentExtension":251,"providers":252,"relations":257,"repo":259,"tags":423,"workflow":424},1778698837670.8008,"k17apsy39kxc4xvn8cyg3rebb186nvjt",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Risk assessment engine for AI agents. Value at Risk (VaR), CVaR, stress testing, and multi-factor risk scoring. Monte Carlo powered. Built for trading agents, lending agents, and portfolio managers.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-risk","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":227,"workflow":244},1778699023222.9656,"kn75ajksvjpprskswn1d2rpa6d86nt1p","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":201,"promptVersionExtension":205,"promptVersionScoring":206,"purpose":207,"rationale":208,"score":209,"summary":210,"tags":211,"targetMarket":220,"tier":221,"useCases":222},[21,26,29,32,36,39,43,48,51,54,58,62,65,69,72,75,78,81,84,87,90,94,98,102,106,109,112,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly names the problem of risk assessment for AI agents and specifies its application to trading, lending, and portfolio management.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers advanced risk assessment capabilities (VaR, CVaR, stress testing, Monte Carlo) beyond standard LLM capabilities, providing deterministic answers for complex financial scenarios.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill is production-ready, offering a clear pricing model, documented usage via API and SDK, and evidence of multiple independent implementations in real-world projects.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on risk assessment and quantitative analysis for financial agents, adhering to a single domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description is accurate, concise, and effectively communicates the skill's purpose and target audience.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill's tools, as implied by the SKILL.md and README, are specific to risk assessment and quantitative analysis, avoiding general-purpose execution.",{"category":44,"check":45,"severity":46,"summary":47},"Documentation","Configuration & parameter reference","info","The SKILL.md mentions the `ORACLAW_API_KEY` environment variable, but doesn't explicitly detail precedence order or default values for other configurations.",{"category":33,"check":49,"severity":24,"summary":50},"Tool naming","The tool names mentioned in the documentation, such as 'analyze_risk' and 'simulate_montecarlo', are descriptive and relevant to the financial risk domain.",{"category":33,"check":52,"severity":24,"summary":53},"Minimal I/O surface","The example input for portfolio VaR is specific, requesting only necessary parameters. The described outputs (VaR, CVaR, contribution) are focused on the task.",{"category":55,"check":56,"severity":24,"summary":57},"License","License usability","The project is licensed under the MIT license, as indicated by the LICENSE file and README.",{"category":59,"check":60,"severity":24,"summary":61},"Maintenance","Commit recency","The last commit was on May 2, 2026, which is recent.",{"category":59,"check":63,"severity":24,"summary":64},"Dependency Management","The project is built using standard Node.js packages and appears to have robust dependency management, indicated by its presence on npm and the MIT license.",{"category":66,"check":67,"severity":24,"summary":68},"Security","Secret Management","The skill requires an API key (`ORACLAW_API_KEY`) but handles it via environment variables, which is a standard and appropriate practice.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill's tools operate on structured financial data and do not appear to load or execute external, untrusted code or data.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill's functionality is self-contained within its npm packages and API, without runtime fetching of code or instructions from remote URLs.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill operates as an API or uses standard npm packages, implying it does not modify files outside its designated scope or project folders.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","There is no indication of detached processes or retry loops around denied calls; the skill relies on deterministic API calls.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill processes financial data for risk assessment and does not appear to exfiltrate confidential user data to third parties.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content and documentation appear free of hidden text tricks or malicious Unicode characters.",{"category":66,"check":88,"severity":24,"summary":89},"Opaque code execution","The skill is distributed via npm packages and an API, with no evidence of obfuscated code or runtime execution of downloaded scripts.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill relies on API calls or npm packages and does not make assumptions about user project file structures.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","With 0 issues opened and 44 closed in the last 90 days, the maintainers are highly responsive.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The `manifestVersion` is declared as 1.0.0, and a CHANGELOG.md is present, indicating clear versioning.",{"category":103,"check":104,"severity":24,"summary":105},"Code Execution","Validation","The skill's tools operate on structured financial data, and the API/SDK approach implies input validation.",{"category":66,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The skill is primarily analytical and does not perform destructive operations.",{"category":103,"check":110,"severity":24,"summary":111},"Error Handling","The API and SDK design suggests structured error handling, returning specific codes and messages rather than raw exceptions.",{"category":103,"check":113,"severity":114,"summary":115},"Logging","not_applicable","As the skill relies on an external API and npm packages, there is no direct audit log to evaluate within the skill's own code.",{"category":117,"check":118,"severity":114,"summary":119},"Compliance","GDPR","The skill processes financial data and does not inherently handle personal data.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The skill's financial risk assessment capabilities are globally applicable, and no regional restrictions are indicated.",{"category":91,"check":124,"severity":24,"summary":125},"Runtime stability","The skill utilizes standard web APIs and npm packages, ensuring cross-platform compatibility.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README is comprehensive, detailing the purpose, features, implementation, and usage of the OraClaw tools.",{"category":33,"check":130,"severity":24,"summary":131},"Tool surface size","The skill exposes a focused set of 17 tools, well within the recommended range.",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","The tool names are distinct and cover specific areas of risk assessment and optimization, avoiding near-synonym redundancy.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, including the 17 MCP tools and SDK packages, are implemented and documented.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","Clear installation instructions are provided for the MCP server, REST API, and npm SDK, along with copy-pasteable examples.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","The API and SDK approach implies structured error messages with codes and potential remediation steps.",{"category":147,"check":148,"severity":24,"summary":149},"Execution","Pinned dependencies","The skill is distributed via npm, indicating dependencies are managed and likely pinned through package-lock.json.",{"category":33,"check":151,"severity":114,"summary":152},"Dry-run preview","The skill is analytical and does not perform state-changing operations that would require a dry-run mode.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The API-based design and focus on deterministic calculations suggest that operations are likely idempotent and have timeouts implemented by the service.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The project documentation does not mention telemetry, implying it is either not collected or strictly opt-in.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md clearly states the purpose (risk assessment engine) and use cases (VaR, CVaR, stress testing) for AI agents.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the skill's core capability and target audience.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is well-structured and appropriately delegates deeper material to external files or examples.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines procedures and links to external resources where applicable, demonstrating progressive disclosure.",{"category":170,"check":174,"severity":114,"summary":175},"Forked exploration","The skill's function is direct calculation and analysis, not deep exploration requiring a forked context.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md and README provide clear, ready-to-use examples for portfolio VaR calculation, Monte Carlo simulation, and anomaly detection.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md mentions rules for distributions and iterations, implicitly handling some edge cases; further documentation covers pricing tiers.",{"category":103,"check":183,"severity":114,"summary":184},"Tool Fallback","The skill relies on its own provided MCP server or API, not external, optional tools with fallbacks.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The use of an API and structured inputs suggests that invalid states would result in clear errors and halts.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and does not appear to rely on other specific skills being loaded concurrently.",1778699022885,"OraClaw provides a suite of deterministic tools for financial risk assessment, including Monte Carlo simulations, VaR, CVaR, and stress testing. It is accessible via an API, npm SDK, and an MCP server.",[195,196,197,198,199,200],"Value at Risk (VaR) calculation","Conditional Value at Risk (CVaR) calculation","Monte Carlo simulation for scenario analysis","Stress testing of financial assumptions","Multi-factor risk scoring","Convergence analysis of risk indicators",[202,203,204],"Performing basic arithmetic or probability calculations outside of financial risk contexts","Providing financial advice or investment recommendations","Replacing core LLM reasoning capabilities","3.0.0","4.4.0","To equip AI agents with precise, mathematically sound risk assessment capabilities, enabling them to make data-driven decisions in financial contexts.","The skill is exceptionally well-documented, robustly implemented via API and npm packages, and demonstrates significant real-world adoption. Minor info-level finding on configuration detail.",98,"A high-quality, production-ready risk assessment engine for AI agents.",[212,213,214,215,216,217,218,219],"risk-assessment","finance","monte-carlo","quantitative-analysis","trading","portfolio-management","cvar","var","global","verified",[223,224,225,226],"Calculating VaR for portfolios or individual positions","Running stress tests on financial models","Quantifying worst-case scenarios with confidence intervals","Assessing credit risk and default probability",{"codeQuality":228,"collectedAt":230,"documentation":231,"maintenance":234,"security":240,"testCoverage":243},{"hasLockfile":229},true,1778699008989,{"descriptionLength":232,"readmeSize":233},198,9472,{"closedIssues90d":235,"forks":236,"hasChangelog":229,"manifestVersion":237,"openIssues90d":8,"pushedAt":238,"stars":239},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":241,"license":242,"smitheryVerified":241},false,"MIT",{"hasCi":229,"hasTests":229},{"updatedAt":245},1778699023223,{"basePath":247,"githubOwner":248,"githubRepo":249,"locale":18,"slug":13,"type":250},"mission-control/packages/clawhub-skills/oraclaw-risk","Whatsonyourmind","oraclaw","skill",null,{"evaluate":253,"extract":255},{"promptVersionExtension":205,"promptVersionScoring":206,"score":209,"tags":254,"targetMarket":220,"tier":221},[212,213,214,215,216,217,218,219],{"commitSha":256},"HEAD",{"repoId":258},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",{"_creationTime":260,"_id":258,"identity":261,"providers":262,"workflow":419},1778698831609.0093,{"githubOwner":248,"githubRepo":249,"sourceUrl":14},{"classify":263,"discover":394,"github":397},{"commitSha":256,"extensions":264},[265,276,284,292,300,308,316,324,332,340,348,356,361,369,377],{"basePath":266,"description":267,"displayName":268,"installMethods":269,"rationale":270,"selectedPaths":271,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-anomaly","Anomaly detection for AI agents. Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.","oraclaw-anomaly",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-anomaly/SKILL.md",[272],{"path":273,"priority":274},"SKILL.md","mandatory","rule",{"basePath":277,"description":278,"displayName":279,"installMethods":280,"rationale":281,"selectedPaths":282,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-bandit","A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request","oraclaw-bandit",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-bandit/SKILL.md",[283],{"path":273,"priority":274},{"basePath":285,"description":286,"displayName":287,"installMethods":288,"rationale":289,"selectedPaths":290,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-bayesian","Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.","oraclaw-bayesian",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-bayesian/SKILL.md",[291],{"path":273,"priority":274},{"basePath":293,"description":294,"displayName":295,"installMethods":296,"rationale":297,"selectedPaths":298,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-calibrate","Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.","oraclaw-calibrate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-calibrate/SKILL.md",[299],{"path":273,"priority":274},{"basePath":301,"description":302,"displayName":303,"installMethods":304,"rationale":305,"selectedPaths":306,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-cmaes","CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.","oraclaw-cmaes",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-cmaes/SKILL.md",[307],{"path":273,"priority":274},{"basePath":309,"description":310,"displayName":311,"installMethods":312,"rationale":313,"selectedPaths":314,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-decide","Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.","oraclaw-decide",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-decide/SKILL.md",[315],{"path":273,"priority":274},{"basePath":317,"description":318,"displayName":319,"installMethods":320,"rationale":321,"selectedPaths":322,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-ensemble","Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.","oraclaw-ensemble",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-ensemble/SKILL.md",[323],{"path":273,"priority":274},{"basePath":325,"description":326,"displayName":327,"installMethods":328,"rationale":329,"selectedPaths":330,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-evolve","Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem with multiple competing goals. Handles nonlinear search spaces that LP solvers cannot.","oraclaw-evolve",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-evolve/SKILL.md",[331],{"path":273,"priority":274},{"basePath":333,"description":334,"displayName":335,"installMethods":336,"rationale":337,"selectedPaths":338,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-forecast","Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.","oraclaw-forecast",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-forecast/SKILL.md",[339],{"path":273,"priority":274},{"basePath":341,"description":342,"displayName":343,"installMethods":344,"rationale":345,"selectedPaths":346,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-graph","Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things.","oraclaw-graph",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-graph/SKILL.md",[347],{"path":273,"priority":274},{"basePath":349,"description":350,"displayName":351,"installMethods":352,"rationale":353,"selectedPaths":354,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-pathfind","A* pathfinding and task sequencing for AI agents. Find the optimal path through workflows, dependencies, and decision trees. K-shortest paths via Yen's algorithm. Cost/time/risk breakdown.","oraclaw-pathfind",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-pathfind/SKILL.md",[355],{"path":273,"priority":274},{"basePath":247,"description":10,"displayName":13,"installMethods":357,"rationale":358,"selectedPaths":359,"source":275,"sourceLanguage":18,"type":250},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-risk/SKILL.md",[360],{"path":273,"priority":274},{"basePath":362,"description":363,"displayName":364,"installMethods":365,"rationale":366,"selectedPaths":367,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-simulate","Monte Carlo simulation for AI agents. Run thousands of probabilistic scenarios to model risk, forecast revenue, estimate project timelines, and quantify uncertainty. Supports 6 distribution types.","oraclaw-simulate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-simulate/SKILL.md",[368],{"path":273,"priority":274},{"basePath":370,"description":371,"displayName":372,"installMethods":373,"rationale":374,"selectedPaths":375,"source":275,"sourceLanguage":18,"type":250},"mission-control/packages/clawhub-skills/oraclaw-solver","Industrial-grade scheduling and resource optimization for AI agents. Solve task scheduling with energy matching, budget allocation, and any LP/MIP constraint problem in milliseconds.","oraclaw-solver",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-solver/SKILL.md",[376],{"path":273,"priority":274},{"basePath":378,"description":379,"displayName":380,"installMethods":381,"license":242,"rationale":382,"selectedPaths":383,"source":275,"sourceLanguage":18,"type":393},"mission-control/packages/mcp-server","OraClaw Decision Intelligence — 17 MCP tools for AI agents (6 premium API-key tools + 11 free). Full input/output schemas + MCP behavior annotations on every tool. Optimization (bandit/CMA-ES/genetic/LP-MIP), simulation (Monte Carlo/scenarios), prediction (ARIMA/Holt-Winters/Bayesian/ensemble), scoring (convergence/calibration), graph analytics, anomaly detection, pathfinding, scheduling.","@oraclaw/mcp-server",{"npm":380},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[384,386,388,390],{"path":385,"priority":274},"server.json",{"path":387,"priority":274},"package.json",{"path":389,"priority":274},"README.md",{"path":391,"priority":392},"src/index.ts","low","mcp",{"sources":395},[396],"manual",{"closedIssues90d":235,"description":398,"forks":236,"homepage":399,"license":242,"openIssues90d":8,"pushedAt":238,"readmeSize":233,"stars":239,"topics":400},"Deterministic decision-intelligence MCP server for AI agents — 17 tools, 21 algorithms (LinUCB, HiGHS LP/MIP, PageRank, Monte Carlo, CMA-ES, conformal). Sub-25ms. Zero LLM cost. AAA on Glama. Field-validated in 12+ OSS projects.","https://web-olive-one-89.vercel.app",[401,402,403,404,405,406,407,393,408,409,410,411,412,413,414,415,416,417,214,418],"ai-agents","algorithms","api","bandits","decision-intelligence","fastify","machine-learning","optimization","typescript","agent-tools","anthropic","claude-mcp","contextual-bandit","deterministic-tools","linear-programming","llm-tools","model-context-protocol","pagerank",{"classifiedAt":420,"discoverAt":421,"extractAt":422,"githubAt":422,"updatedAt":420},1778698837409,1778698831609,1778698835357,[218,213,214,217,215,212,216,219],{"evaluatedAt":245,"extractAt":425,"updatedAt":426},1778698837670,1778699188583,[],[429,459,487,509,530,550],{"_creationTime":430,"_id":431,"community":432,"display":433,"identity":439,"providers":444,"relations":452,"tags":455,"workflow":456},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":434,"installMethods":435,"name":437,"sourceUrl":438},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":436},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":440,"githubOwner":441,"githubRepo":442,"locale":18,"slug":443,"type":250},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":445,"extract":451},{"promptVersionExtension":205,"promptVersionScoring":206,"score":446,"tags":447,"targetMarket":220,"tier":221},100,[213,216,448,449,409,450],"market-analysis","ai","cli",{"commitSha":256,"license":242},{"parentExtensionId":453,"repoId":454},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[449,450,213,448,216,409],{"evaluatedAt":457,"extractAt":458,"updatedAt":457},1778701108877,1778696691708,{"_creationTime":460,"_id":461,"community":462,"display":463,"identity":469,"providers":473,"relations":479,"tags":482,"workflow":483},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":464,"installMethods":465,"name":467,"sourceUrl":468},"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\".",{"claudeCode":466},"mnemox-ai/tradememory-protocol","trading-memory","https://github.com/mnemox-ai/tradememory-protocol",{"basePath":470,"githubOwner":471,"githubRepo":472,"locale":18,"slug":467,"type":250},"tradememory-plugin/skills/trading-memory","mnemox-ai","tradememory-protocol",{"evaluate":474,"extract":478},{"promptVersionExtension":205,"promptVersionScoring":206,"score":446,"tags":475,"targetMarket":220,"tier":221},[216,449,476,213,477],"memory","python",{"commitSha":256},{"parentExtensionId":480,"repoId":481},"k170vxkqee48k2xq1v55a025nh86nzn7","kd73z11kfekksxyrs8ds0snacs86ncdy",[449,213,476,477,216],{"evaluatedAt":484,"extractAt":485,"updatedAt":486},1778693719816,1778693539593,1778693833320,{"_creationTime":488,"_id":489,"community":490,"display":491,"identity":495,"providers":498,"relations":504,"tags":505,"workflow":506},1778693539593.186,"k17bgwvhb6h29py715de1cm9xd86msq6",{"reviewCount":8},{"description":492,"installMethods":493,"name":494,"sourceUrl":468},"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\".",{"claudeCode":466},"Risk Management",{"basePath":496,"githubOwner":471,"githubRepo":472,"locale":18,"slug":497,"type":250},"tradememory-plugin/skills/risk-management","risk-management",{"evaluate":499,"extract":503},{"promptVersionExtension":205,"promptVersionScoring":206,"score":446,"tags":500,"targetMarket":220,"tier":221},[216,497,501,502,213],"ai-agent","behavioral-analysis",{"commitSha":256,"license":242},{"parentExtensionId":480,"repoId":481},[501,502,213,497,216],{"evaluatedAt":507,"extractAt":485,"updatedAt":508},1778693700524,1778693833120,{"_creationTime":510,"_id":511,"community":512,"display":513,"identity":517,"providers":519,"relations":526,"tags":527,"workflow":528},1778696691708.328,"k172nv5vbyw1c60vavz8f9esw186m2q7",{"reviewCount":8},{"description":514,"installMethods":515,"name":516,"sourceUrl":438},"Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural prediction",{"claudeCode":436},"trader-signal",{"basePath":518,"githubOwner":441,"githubRepo":442,"locale":18,"slug":516,"type":250},"plugins/ruflo-neural-trader/skills/trader-signal",{"evaluate":520,"extract":525},{"promptVersionExtension":205,"promptVersionScoring":206,"score":521,"tags":522,"targetMarket":220,"tier":221},99,[216,213,523,407,524],"anomaly-detection","prediction",{"commitSha":256},{"parentExtensionId":453,"repoId":454},[523,213,407,524,216],{"evaluatedAt":529,"extractAt":458,"updatedAt":529},1778701148958,{"_creationTime":531,"_id":532,"community":533,"display":534,"identity":538,"providers":540,"relations":545,"tags":546,"workflow":547},1778693539593.1855,"k17em57x7pnqhv6x3a2s5g5wv586mjq6",{"reviewCount":8},{"description":535,"installMethods":536,"name":537,"sourceUrl":468},"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",{"claudeCode":466},"tradememory-bridge",{"basePath":539,"githubOwner":471,"githubRepo":472,"locale":18,"slug":537,"type":250},"skills/tradememory-bridge",{"evaluate":541,"extract":544},{"promptVersionExtension":205,"promptVersionScoring":206,"score":521,"tags":542,"targetMarket":220,"tier":221},[216,213,476,543,501],"journaling",{"commitSha":256},{"repoId":481},[501,213,543,476,216],{"evaluatedAt":548,"extractAt":485,"updatedAt":549},1778693660212,1778693832747,{"_creationTime":551,"_id":552,"community":553,"display":554,"identity":558,"providers":560,"relations":565,"tags":566,"workflow":567},1778693539593.185,"k174jrt9tnryf6b31jfh0hn3js86ny47",{"reviewCount":8},{"description":555,"installMethods":556,"name":557,"sourceUrl":468},"AI trading memory with outcome-weighted recall and autonomous strategy evolution. 17 MCP tools, 1,233 tests, works with any trading platform.",{"claudeCode":466},"tradememory",{"basePath":559,"githubOwner":471,"githubRepo":472,"locale":18,"slug":557,"type":250},".skills/tradememory",{"evaluate":561,"extract":564},{"promptVersionExtension":205,"promptVersionScoring":206,"score":521,"tags":562,"targetMarket":220,"tier":221},[216,213,476,563,449,477,393],"strategy",{"commitSha":256},{"repoId":481},[449,213,393,476,477,563,216],{"evaluatedAt":568,"extractAt":485,"updatedAt":569},1778693611218,1778693832326]