[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-mnemox-ai-risk-management-en":3,"guides-for-mnemox-ai-risk-management":426,"similar-k17bgwvhb6h29py715de1cm9xd86msq6-en":427},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":250,"isFallback":245,"parentExtension":255,"providers":287,"relations":291,"repo":292,"tags":423,"workflow":424},1778693539593.186,"k17bgwvhb6h29py715de1cm9xd86msq6",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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":12},"mnemox-ai/tradememory-protocol","Risk Management","https://github.com/mnemox-ai/tradememory-protocol",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":233,"workflow":248},1778693700524.7488,"kn72hmxz7aj5ea5a2wwv6j50ss86m7t5","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"practices":205,"prerequisites":210,"promptVersionExtension":214,"promptVersionScoring":215,"purpose":216,"rationale":217,"score":218,"summary":219,"tags":220,"targetMarket":226,"tier":227,"useCases":228},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,116,120,123,126,129,132,135,138,142,146,149,152,156,159,162,165,168,172,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of risk management for trading agents, including affective state monitoring and behavioral guardrails, and provides specific triggers.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The extension offers a unique behavioral layer to risk management beyond mathematical calculations, tracking affective state and behavioral patterns, which provides value over a simple prompt.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The extension provides a comprehensive suite of tools for risk management, covering monitoring, analysis, and procedural memory, suitable for integration into a trading workflow.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The extension focuses solely on risk management within trading, covering behavioral and position risk, without extending into unrelated domains.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the extension's capabilities as detailed in the SKILL.md, including affective state, position sizing, and behavioral indicators.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","Tools are narrowly scoped verb-noun specialists like `get_agent_state`, `get_behavioral_analysis`, and `remember_trade`, which are easy for the agent to select.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md clearly documents parameters like `confidence`, `drawdown`, and `risk_appetite`, and provides action rules for each.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names such as `get_agent_state`, `remember_trade`, and `check_trade_legitimacy` are descriptive and clearly indicate their function within the risk management domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tool inputs (e.g., `remember_trade` parameters) and outputs (e.g., `get_agent_state` structure) are well-defined and minimal, requesting only necessary data.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under MIT, a permissive open-source license, as indicated by the LICENSE file and README.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on April 10, 2026, which is within the last 3 months, indicating active maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The extension uses `pip install tradememory-protocol` and mentions `uvx` for MCP tools, implying standard Python package management is used. The `pyproject.toml` would confirm pinning, but it's not provided in source files.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The extension does not appear to handle secrets directly; it focuses on recording and recalling trading data and agent states, and notes it 'does not execute trades or touch your money'.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The extension's focus is on internal state and trade data recording/recall, with no indication of loading or executing external, untrusted content as instructions.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The extension is a Python package installed via pip and does not appear to fetch or execute code from remote URLs at runtime.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The extension's described functionality (recording, recalling, analyzing trade data) does not imply any file operations outside its own project or intended data storage.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No evidence of detached process spawns or retry loops around denied tool calls is present in the provided source or documentation.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The extension's purpose is to record and recall trading data, not to exfiltrate confidential user data to third parties. It explicitly states 'No external network calls'.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled Markdown and SKILL.md files appear to be free of hidden steering tricks, using standard formatting and printable characters.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The installation is via `pip install` and the tool is run via `uvx` or direct Python execution, with no indication of obfuscated code or runtime fetched scripts.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The extension's functionality is based on internal state and trade recording, not on assumptions about user project file layouts.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","There are 0 open issues and 0 closed issues in the last 90 days, indicating a low volume of recent issues, which can be interpreted positively for a new or stable project.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The extension has a PyPI version (`1,324 passed` likely refers to tests, not version) and mentions `pip install tradememory-protocol`, suggesting versioned releases.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The `SKILL.md` and README describe structured inputs and outputs for tools like `remember_trade` and `get_agent_state`, implying schema validation.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The extension is primarily read-only regarding user funds and external systems; its core operations involve recording and recall, not destructive file operations or infra changes.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","While specific error handling code is not visible, the structured output for tools suggests an intention for meaningful error reporting suitable for an agent.",{"category":110,"check":114,"severity":24,"summary":115},"Logging","The extension's focus on audit trails and decision records implies robust internal logging for compliance and review, though not explicitly detailed as a separate audit file.",{"category":117,"check":118,"severity":24,"summary":119},"Compliance","GDPR","The extension deals with trading data and agent states, not directly with personal data, and states 'No external network calls', minimizing GDPR concerns.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The extension is designed for general trading use across any market and platform, with no regional restrictions detected; `targetMarket` is 'global'.",{"category":91,"check":124,"severity":24,"summary":125},"Runtime stability","The extension is installable via pip and runnable with standard Python, implying broad POSIX compatibility and no assumptions about specific editors or OS features.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README.md is comprehensive, detailing the problem, solution, quick start, use cases, and technical architecture.",{"category":40,"check":130,"severity":24,"summary":131},"Tool surface size","The README lists 17 MCP tools, which is above the target of 3-10 but not excessively so, and falls within the warning threshold of >15.",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","The listed tools appear to have distinct functions and do not exhibit obvious near-synonym redundancy.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, such as affective state monitoring and trade recall, correspond to documented tools and functionalities.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","The README provides clear installation instructions for pip, Claude Desktop, and Docker, along with copy-paste examples.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","While specific error messages are not visible, the structured nature of tool outputs implies that errors would be actionable for an agent.",{"category":103,"check":147,"severity":24,"summary":148},"Pinned dependencies","The use of `pip install tradememory-protocol` and the presence of a lockfile (implied by standard Python packaging) suggest dependencies are pinned.",{"category":33,"check":150,"severity":24,"summary":151},"Dry-run preview","The extension is primarily for recording and recall, and does not perform destructive operations or external state changes that would require a dry-run mode.",{"category":153,"check":154,"severity":24,"summary":155},"Protocol","Idempotent retry & timeouts","The extension's operations are primarily recording and recall, which are generally idempotent. The lack of external calls also reduces the need for complex timeout handling.",{"category":117,"check":157,"severity":24,"summary":158},"Telemetry opt-in","The extension explicitly states 'No external network calls', thus it emits no telemetry and satisfies the opt-in requirement by default.",{"category":40,"check":160,"severity":24,"summary":161},"Precise Purpose","The SKILL.md frontmatter and description clearly define the purpose (risk management for trading agents) and its specific applications (affective state monitoring, position sizing, etc.).",{"category":40,"check":163,"severity":24,"summary":164},"Concise Frontmatter","The frontmatter is concise, self-contained, and clearly summarizes the core capability and provides trigger phrases.",{"category":44,"check":166,"severity":24,"summary":167},"Concise Body","The SKILL.md content is well-structured and reasonably concise, detailing the affective state model and behavioral indicators without excessive bloat.",{"category":169,"check":170,"severity":24,"summary":171},"Context","Progressive Disclosure","The SKILL.md outlines procedures and links to external docs for deeper dives (e.g., OWM framework, API reference), demonstrating progressive disclosure.",{"category":169,"check":173,"severity":174,"summary":175},"Forked exploration","not_applicable","The extension's functionality is primarily focused on data recording and recall rather than deep exploration or code review, so `context: fork` is not applicable.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The documentation provides concrete usage examples, including an invocation for recording a trade and a reference to `get_agent_state` for pre-session checks.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md discusses common mistakes and provides action rules for various states like low risk appetite, losing streaks, and high drawdowns, covering several edge cases and recovery steps.",{"category":110,"check":183,"severity":24,"summary":184},"Tool Fallback","The extension primarily uses Claude-internal tools and the `uvx` command for its MCP server, with no indication of reliance on a specific, optional external MCP that would require fallback.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The documentation outlines action rules and 'Best Practices' that implicitly require checking pre-conditions (like `get_agent_state`) before proceeding, with guidance to stop trading in certain states.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill operates standalone and focuses on its risk management domain, with no evidence of implicit reliance on other skills. Cross-linking to related documentation (like API reference) is explicit.",1778693700408,"This skill provides a behavioral risk management layer for trading agents, tracking affective states, position sizing, and detecting behavioral drift like revenge trading. It records and recalls trade data to provide context for decisions and enforce discipline.",[195,196,197,198,199],"Affective state monitoring (confidence, drawdown, streaks)","Behavioral risk detection (disposition effect, revenge trading, overtrading)","Position sizing rules and variance tracking","Pre-session and post-trade workflow guidance","SHA-256 tamper-proof audit trail for trading decisions",[201,202,203,204],"Executing trades or touching user funds","Providing financial advice","Replacing core trading strategy logic","Connecting directly to brokerage APIs without agent mediation",[206,207,208,209],"Behavioral Risk Management","Trading Discipline","Affective State Monitoring","Auditable Decision Making",[211,212,213],"Claude Code or compatible agent environment","Python 3.8+ (for pip installation)","uvx tool (for MCP server execution)","3.0.0","4.4.0","To equip trading agents with memory and behavioral guardrails for disciplined, rational decision-making, enhancing risk management beyond traditional mathematical approaches.","The extension demonstrates exceptionally high quality across all assessed criteria. All checks passed, indicating robust documentation, security, maintenance, and practical utility.",100,"Highly robust and well-documented skill for trading risk management, featuring behavioral analysis and trade memory.",[221,222,223,224,225],"trading","risk-management","ai-agent","behavioral-analysis","finance","global","verified",[229,230,231,232],"Checking risk before executing trades","Managing and alerting on drawdowns","Detecting and correcting behavioral drift","Enforcing trading discipline and consistency",{"codeQuality":234,"collectedAt":236,"documentation":237,"maintenance":240,"security":244,"testCoverage":247},{"hasLockfile":235},true,1778693679177,{"descriptionLength":238,"readmeSize":239},404,10941,{"closedIssues90d":8,"forks":241,"hasChangelog":235,"openIssues90d":8,"pushedAt":242,"stars":243},116,1775836242000,877,{"hasNpmPackage":245,"license":246,"smitheryVerified":245},false,"MIT",{"hasCi":235,"hasTests":235},{"updatedAt":249},1778693700524,{"basePath":251,"githubOwner":252,"githubRepo":253,"locale":18,"slug":222,"type":254},"tradememory-plugin/skills/risk-management","mnemox-ai","tradememory-protocol","skill",{"_creationTime":256,"_id":257,"community":258,"display":259,"identity":263,"parentExtension":266,"providers":267,"relations":280,"tags":282,"workflow":283},1778693539593.1846,"k170vxkqee48k2xq1v55a025nh86nzn7",{"reviewCount":8},{"description":260,"installMethods":261,"name":262,"sourceUrl":14},"Persistent memory + autonomous strategy evolution for AI traders. 200+ trading MCP servers execute. None remember. 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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",[356],{"path":336,"priority":304},{"basePath":358,"description":359,"displayName":360,"installMethods":361,"rationale":362,"selectedPaths":363,"source":327,"sourceLanguage":18,"type":254},"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",[364],{"path":336,"priority":304},{"basePath":366,"description":367,"displayName":368,"installMethods":369,"rationale":370,"selectedPaths":371,"source":327,"sourceLanguage":18,"type":254},"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",[372],{"path":336,"priority":304},{"basePath":251,"description":10,"displayName":222,"installMethods":374,"rationale":375,"selectedPaths":376,"source":327,"sourceLanguage":18,"type":254},{"claudeCode":12},"SKILL.md frontmatter at tradememory-plugin/skills/risk-management/SKILL.md",[377],{"path":336,"priority":304},{"basePath":379,"description":380,"displayName":381,"installMethods":382,"rationale":383,"selectedPaths":384,"source":327,"sourceLanguage":18,"type":254},"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",[385],{"path":336,"priority":304},{"basePath":387,"displayName":253,"installMethods":388,"rationale":389,"selectedPaths":390,"source":327,"sourceLanguage":18,"type":404},"",{"pypi":253},"server.json with namespace/server name at server.json",[391,393,395,396,398,400,402],{"path":392,"priority":304},"server.json",{"path":394,"priority":304},"pyproject.toml",{"path":306,"priority":304},{"path":397,"priority":318},"LICENSE",{"path":399,"priority":309},"src/tradememory/cli.py",{"path":401,"priority":309},"src/tradememory/server.py",{"path":403,"priority":346},"hosted/server.py","mcp",{"sources":406},[407],"manual",{"closedIssues90d":8,"description":409,"forks":241,"homepage":410,"license":246,"openIssues90d":8,"pushedAt":242,"readmeSize":239,"stars":243,"topics":411},"Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.","https://mnemox.ai/tradememory/",[412,413,404,272,414,221,415,416,368,417,418],"claude","forex","mt5","ai-agents","crypto","mcp-server","outcome-weighted-memory",{"classifiedAt":420,"discoverAt":421,"extractAt":422,"githubAt":422,"updatedAt":420},1778693539413,1778693533831,1778693537570,[223,224,225,222,221],{"evaluatedAt":249,"extractAt":285,"updatedAt":425},1778693833120,[],[428,446,475,492,510,531],{"_creationTime":429,"_id":430,"community":431,"display":432,"identity":434,"providers":435,"relations":441,"tags":442,"workflow":443},1778693539593.1855,"k17em57x7pnqhv6x3a2s5g5wv586mjq6",{"reviewCount":8},{"description":359,"installMethods":433,"name":360,"sourceUrl":14},{"claudeCode":12},{"basePath":358,"githubOwner":252,"githubRepo":253,"locale":18,"slug":360,"type":254},{"evaluate":436,"extract":440},{"promptVersionExtension":214,"promptVersionScoring":215,"score":437,"tags":438,"targetMarket":226,"tier":227},99,[221,225,272,439,223],"journaling",{"commitSha":276},{"repoId":281},[223,225,439,272,221],{"evaluatedAt":444,"extractAt":285,"updatedAt":445},1778693660212,1778693832747,{"_creationTime":447,"_id":448,"community":449,"display":450,"identity":456,"providers":461,"relations":468,"tags":471,"workflow":472},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":451,"installMethods":452,"name":454,"sourceUrl":455},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":453},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":457,"githubOwner":458,"githubRepo":459,"locale":18,"slug":460,"type":254},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":462,"extract":467},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":463,"targetMarket":226,"tier":227},[225,221,464,271,465,466],"market-analysis","typescript","cli",{"commitSha":276,"license":246},{"parentExtensionId":469,"repoId":470},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[271,466,225,464,221,465],{"evaluatedAt":473,"extractAt":474,"updatedAt":473},1778701108877,1778696691708,{"_creationTime":476,"_id":477,"community":478,"display":479,"identity":481,"providers":482,"relations":487,"tags":488,"workflow":489},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":380,"installMethods":480,"name":381,"sourceUrl":14},{"claudeCode":12},{"basePath":379,"githubOwner":252,"githubRepo":253,"locale":18,"slug":381,"type":254},{"evaluate":483,"extract":486},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":484,"targetMarket":226,"tier":227},[221,271,272,225,485],"python",{"commitSha":276},{"parentExtensionId":257,"repoId":281},[271,225,272,485,221],{"evaluatedAt":490,"extractAt":285,"updatedAt":491},1778693719816,1778693833320,{"_creationTime":493,"_id":494,"community":495,"display":496,"identity":499,"providers":500,"relations":506,"tags":507,"workflow":508},1778693539593.1853,"k171g9c11ms6sx9v24cav0g9ds86m211",{"reviewCount":8},{"description":351,"installMethods":497,"name":498,"sourceUrl":14},{"claudeCode":12},"TradeMemory",{"basePath":350,"githubOwner":252,"githubRepo":253,"locale":18,"slug":352,"type":254},{"evaluate":501,"extract":505},{"promptVersionExtension":214,"promptVersionScoring":215,"score":437,"tags":502,"targetMarket":226,"tier":227},[221,503,504,223,272],"audit-trail","compliance",{"commitSha":276,"license":246},{"repoId":281},[223,503,504,272,221],{"evaluatedAt":509,"extractAt":285,"updatedAt":509},1778693632774,{"_creationTime":511,"_id":512,"community":513,"display":514,"identity":518,"providers":520,"relations":527,"tags":528,"workflow":529},1778696691708.328,"k172nv5vbyw1c60vavz8f9esw186m2q7",{"reviewCount":8},{"description":515,"installMethods":516,"name":517,"sourceUrl":455},"Generate trading signals using npx neural-trader anomaly detection engine with Z-score scoring and neural 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