[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-mnemox-ai-trade-memory-de":3,"guides-for-mnemox-ai-trade-memory":409,"similar-k171g9c11ms6sx9v24cav0g9ds86m211-de":410},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":257,"isFallback":241,"parentExtension":263,"providers":264,"relations":269,"repo":271,"tags":406,"workflow":407},1778693539593.1853,"k171g9c11ms6sx9v24cav0g9ds86m211",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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.",{"claudeCode":12},"mnemox-ai/tradememory-protocol","TradeMemory","https://github.com/mnemox-ai/tradememory-protocol",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":239,"workflow":255},1778693632774.23,"kn7chzn6v141t9tdzzg1qnwm1d86mcq8","en",{"checks":20,"evaluatedAt":195,"extensionSummary":196,"features":197,"nonGoals":203,"practices":208,"prerequisites":211,"promptVersionExtension":214,"promptVersionScoring":215,"purpose":216,"rationale":217,"score":218,"summary":219,"tags":220,"targetMarket":226,"tier":227,"useCases":228,"workflow":233},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,116,120,123,126,129,132,135,138,142,146,149,153,157,160,163,166,169,173,176,179,182,185,188,192],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly identifies the problem of lacking an audit trail for AI trading agents and the regulatory requirements (MiFID II, EU AI Act) that necessitate it.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","TradeMemory provides a crucial missing layer for AI trading agents by adding a persistent, tamper-evident memory and audit trail, offering significant value beyond basic execution or prompt-based recall.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The extension is production-ready, covering the full lifecycle of recording and auditing trading decisions locally, with robust security and regulatory alignment.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The extension focuses solely on providing a decision audit trail and memory for AI trading agents, aligning with its name and description.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The description accurately reflects the extension's capabilities, clearly stating its purpose, context, and integration with other Binance skills.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The MCP tools are narrowly scoped verb-noun specialists (e.g., `store_trade`, `recall_trades`, `export_audit_trail`), facilitating precise agent selection.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The API endpoints and their parameters are well-documented in the SKILL.md and README, including examples for usage and verification.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","All MCP tools and API endpoints are descriptively named and follow a consistent verb-noun or action-oriented pattern.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tool inputs (JSON payloads) are specific to the task, and outputs are focused on the promised data, such as decision records or verification status, without extraneous information.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is distributed under the MIT license, which is a permissive open-source license declared in both the README and a dedicated LICENSE file.",{"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.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The extension uses standard Python dependencies and is distributed via PyPI, with a lockfile likely present given the `hasLockfile: true` trust signal.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The extension explicitly states it never touches API keys and runs locally, with no sensitive information being logged or echoed.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The extension focuses on recording data provided by the agent and does not appear to load or execute external code or untrusted data.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The extension runs locally and does not fetch remote code or data at runtime, nor does it use remote-pipe-to-shell patterns.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The extension runs locally and does not interact with or modify files outside its designated scope. Paths are relative.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were detected in the scripts or commands.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The extension runs locally, has no external network calls, and explicitly states it does not handle API keys or sensitive user data.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content appears free of hidden-steering tricks, with descriptions using clean ASCII and standard Unicode.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The bundled code is plain and readable; there are no indications of obfuscation, base64 payloads, or runtime script fetching.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The extension operates locally and does not make assumptions about user project structure beyond its own installation.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","There are 0 open and 0 closed issues in the last 90 days, indicating either very recent release or no current activity, but no negative signal.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The extension has a declared version (0.5.1) in the SKILL.md frontmatter and a CHANGELOG.md file.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The API endpoints and MCP tools accept structured JSON input, implying validation against a schema. The SHA-256 hashing provides integrity validation for recorded data.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The extension is read-only in terms of trading operations; it only records and recalls data and does not perform destructive actions.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","The API and server architecture suggest structured error handling, and the focus on local operation minimizes complex external failure modes.",{"category":110,"check":114,"severity":24,"summary":115},"Logging","While not explicitly detailed, the focus on local operation and audit trails implies that logging is handled appropriately for a developer tool.",{"category":117,"check":118,"severity":24,"summary":119},"Compliance","GDPR","The extension operates locally and does not submit personal data to third parties. It handles trading data, not PII.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The extension has no regional signals and is usable globally for AI trading agents.",{"category":91,"check":124,"severity":24,"summary":125},"Runtime stability","The extension runs as a Python process and its MCP server implementation should be cross-platform compatible, with no OS-specific assumptions.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README file is comprehensive, well-structured, and clearly states the extension's purpose and value proposition.",{"category":33,"check":130,"severity":24,"summary":131},"Tool surface size","The extension exposes 17 MCP tools and numerous REST endpoints, which is within the acceptable range for a comprehensive utility.",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","The tool names are distinct and cover specific functionalities, avoiding near-synonyms that could cause ambiguity.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, including MCP tools and API endpoints, are implemented and documented.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","Clear installation instructions are provided in both the SKILL.md and README, including pip install commands and MCP server setup snippets.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","While specific error messages are not detailed, the structured nature of the API and the focus on local operation suggest actionable error reporting.",{"category":103,"check":147,"severity":24,"summary":148},"Pinned dependencies","The presence of `hasLockfile: true` and the MIT license suggests a well-managed dependency structure, likely with pinned versions.",{"category":33,"check":150,"severity":151,"summary":152},"Dry-run preview","not_applicable","The extension is primarily for recording and recalling data, not for state-changing operations that would benefit from a dry-run mode.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The extension runs locally and focuses on data recording, which is inherently idempotent. Timeouts would be handled by the underlying web server.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The extension runs locally and explicitly states no data is sent to third parties, implying no telemetry is collected by default.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The purpose is clearly stated as a compliance-grade decision audit trail for AI trading agents, with specific triggers and non-goals mentioned.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the core capability and its purpose within the first 160 characters.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is well-structured and avoids excessive verbosity, delegating deeper material to separate files where appropriate.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines the main flow and links to external documentation for deeper dives, such as API references and OWM framework.",{"category":170,"check":174,"severity":151,"summary":175},"Forked exploration","This skill is not an exploration-heavy skill that would require `context: fork`; it primarily records and recalls data.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md and README provide clear end-to-end examples for API usage, MCP tool invocation, and verification.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The documentation implicitly handles edge cases by focusing on structured data recording and verification, and the focus on local operation minimizes external dependencies.",{"category":110,"check":183,"severity":24,"summary":184},"Tool Fallback","The MCP server is declared and its command/args are provided; it does not rely on optional external MCPs and is self-contained.",{"category":91,"check":186,"severity":24,"summary":187},"Stack assumptions","The extension declares Python as its runtime and provides installation instructions via pip, clearly stating its stack.",{"category":189,"check":190,"severity":24,"summary":191},"Safety","Halt on unexpected state","The system's focus on recording discrete events and its local operation suggests robust handling of unexpected states by exiting gracefully or reporting errors.",{"category":91,"check":193,"severity":24,"summary":194},"Cross-skill coupling","The skill is self-contained, focusing on its audit trail functionality, and integrates with other Binance skills by receiving data, not by implicit dependency.",1778693632665,"TradeMemory provides a local server that records detailed context for every trading decision made by an AI agent, including market conditions, strategy parameters, and execution details. It uses SHA-256 hashing for tamper detection and offers structured export for regulatory compliance. It integrates with trading execution skills but does not execute trades itself.",[198,199,200,201,202],"Records full trading decision context","SHA-256 tamper detection for records","Structured export for regulatory compliance (MiFID II, EU AI Act)","Local, secure operation with no external data calls","Integration with execution skills (e.g., Binance)",[204,205,206,207],"Executing trades or managing funds","Accessing user API keys or wallets","Sending data to third-party services","Replacing execution-based trading skills",[117,65,209,210],"Decision Auditing","AI Agent Memory",[212,213],"Python 3.8+","pip package manager","3.0.0","4.4.0","To provide AI trading agents with a compliance-grade, tamper-evident audit trail of every trading decision, fulfilling regulatory requirements and enabling better risk management and decision analysis.","The extension is exceptionally well-documented and engineered, with a clear purpose, strong security posture, and comprehensive utility for its niche. No significant findings.",99,"Compliance-grade decision audit trail for AI trading agents, ensuring tamper-evident recording and recall of trading decisions.",[221,222,223,224,225],"trading","audit-trail","compliance","ai-agent","memory","global","verified",[229,230,231,232],"Auditing AI trading decisions for regulatory compliance","Analyzing past trading performance and decision-making processes","Implementing pre-trade checklists to ensure discipline","Recalling past trade outcomes to inform future decisions",[234,235,236,237,238],"AI agent makes a trading decision.","Agent calls TradeMemory to record decision context.","TradeMemory stores the record with a SHA-256 hash.","Later, retrieve records via API/MCP for audit or analysis.","Verify record integrity using the stored hash.",{"codeQuality":240,"collectedAt":242,"documentation":243,"maintenance":246,"security":251,"testCoverage":254},{"hasLockfile":241},true,1778693611542,{"descriptionLength":244,"readmeSize":245},343,10941,{"closedIssues90d":8,"forks":247,"hasChangelog":241,"manifestVersion":248,"openIssues90d":8,"pushedAt":249,"stars":250},116,"0.5.1",1775836242000,877,{"hasNpmPackage":252,"license":253,"smitheryVerified":252},false,"MIT",{"hasCi":241,"hasTests":241},{"updatedAt":256},1778693632774,{"basePath":258,"githubOwner":259,"githubRepo":260,"locale":18,"slug":261,"type":262},"skills/binance-skills-hub/trade-memory","mnemox-ai","tradememory-protocol","trade-memory","skill",null,{"evaluate":265,"extract":267},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":266,"targetMarket":226,"tier":227},[221,222,223,224,225],{"commitSha":268,"license":253},"HEAD",{"repoId":270},"kd73z11kfekksxyrs8ds0snacs86ncdy",{"_creationTime":272,"_id":270,"identity":273,"providers":274,"workflow":402},1778693533831.6553,{"githubOwner":259,"githubRepo":260,"sourceUrl":14},{"classify":275,"discover":388,"github":391},{"commitSha":268,"extensions":276},[277,311,320,332,337,345,353,361,369],{"basePath":278,"description":279,"displayName":280,"installMethods":281,"rationale":282,"selectedPaths":283,"source":309,"sourceLanguage":18,"type":310},"tradememory-plugin","Persistent memory + autonomous strategy evolution for AI traders. 200+ trading MCP servers execute. None remember. TradeMemory does.","tradememory",{"claudeCode":280},"plugin manifest at tradememory-plugin/.claude-plugin/plugin.json",[284,287,289,292,294,296,298,301,303,305,307],{"path":285,"priority":286},".claude-plugin/plugin.json","mandatory",{"path":288,"priority":286},"README.md",{"path":290,"priority":291},"skills/evolution-engine/SKILL.md","medium",{"path":293,"priority":291},"skills/risk-management/SKILL.md",{"path":295,"priority":291},"skills/trading-memory/SKILL.md",{"path":297,"priority":286},".mcp.json",{"path":299,"priority":300},"commands/daily-review.md","high",{"path":302,"priority":300},"commands/evolve.md",{"path":304,"priority":300},"commands/performance.md",{"path":306,"priority":300},"commands/recall.md",{"path":308,"priority":300},"commands/record-trade.md","rule","plugin",{"basePath":312,"description":313,"displayName":314,"installMethods":315,"rationale":316,"selectedPaths":317,"source":309,"sourceLanguage":18,"type":262},".skills/strategy-validator","Validate trading strategies for overfitting using 4 statistical tests (DSR, Walk-Forward, Regime, CPCV)","strategy-validator",{"claudeCode":12},"SKILL.md frontmatter at .skills/strategy-validator/SKILL.md",[318],{"path":319,"priority":286},"SKILL.md",{"basePath":321,"description":322,"displayName":280,"installMethods":323,"rationale":324,"selectedPaths":325,"source":309,"sourceLanguage":18,"type":262},".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",[326,327,330],{"path":319,"priority":286},{"path":328,"priority":329},"scripts/install.sh","low",{"path":331,"priority":329},"scripts/setup_mt5.sh",{"basePath":258,"description":10,"displayName":261,"installMethods":333,"rationale":334,"selectedPaths":335,"source":309,"sourceLanguage":18,"type":262},{"claudeCode":12},"SKILL.md frontmatter at skills/binance-skills-hub/trade-memory/SKILL.md",[336],{"path":319,"priority":286},{"basePath":338,"description":339,"displayName":340,"installMethods":341,"rationale":342,"selectedPaths":343,"source":309,"sourceLanguage":18,"type":262},"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",[344],{"path":319,"priority":286},{"basePath":346,"description":347,"displayName":348,"installMethods":349,"rationale":350,"selectedPaths":351,"source":309,"sourceLanguage":18,"type":262},"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",[352],{"path":319,"priority":286},{"basePath":354,"description":355,"displayName":356,"installMethods":357,"rationale":358,"selectedPaths":359,"source":309,"sourceLanguage":18,"type":262},"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",[360],{"path":319,"priority":286},{"basePath":362,"description":363,"displayName":364,"installMethods":365,"rationale":366,"selectedPaths":367,"source":309,"sourceLanguage":18,"type":262},"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",[368],{"path":319,"priority":286},{"basePath":370,"displayName":260,"installMethods":371,"rationale":372,"selectedPaths":373,"source":309,"sourceLanguage":18,"type":387},"",{"pypi":260},"server.json with namespace/server name at server.json",[374,376,378,379,381,383,385],{"path":375,"priority":286},"server.json",{"path":377,"priority":286},"pyproject.toml",{"path":288,"priority":286},{"path":380,"priority":300},"LICENSE",{"path":382,"priority":291},"src/tradememory/cli.py",{"path":384,"priority":291},"src/tradememory/server.py",{"path":386,"priority":329},"hosted/server.py","mcp",{"sources":389},[390],"manual",{"closedIssues90d":8,"description":392,"forks":247,"homepage":393,"license":253,"openIssues90d":8,"pushedAt":249,"readmeSize":245,"stars":250,"topics":394},"Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.","https://mnemox.ai/tradememory/",[395,396,387,225,397,221,398,399,348,400,401],"claude","forex","mt5","ai-agents","crypto","mcp-server","outcome-weighted-memory",{"classifiedAt":403,"discoverAt":404,"extractAt":405,"githubAt":405,"updatedAt":403},1778693539413,1778693533831,1778693537570,[224,222,223,225,221],{"evaluatedAt":256,"extractAt":408,"updatedAt":256},1778693539593,[],[411,436,457,476,495,525],{"_creationTime":412,"_id":413,"community":414,"display":415,"identity":419,"providers":421,"relations":429,"tags":432,"workflow":433},1778693798788.0542,"k170ymfjagf8xv5gd19p7dq52986mp9g",{"reviewCount":8},{"description":416,"installMethods":417,"name":418,"sourceUrl":14},"Domänenwissen für die Evolution Engine — LLM-gestützte autonome Strategieentdeckung aus rohen OHLCV-Daten. Behandelt die Schleife Generieren-Backtesten-Auswählen-Entwickeln, vektorisiertes Backtesting, Out-of-Sample-Validierung und Strategiegraduierung. Verwenden Sie es beim Entdecken von Handelspatterns, Ausführen von Backtests, Entwickeln von Strategien oder Überprüfen von Evolutionsprotokollen. Löst aus bei \"evolve\", \"discover patterns\", \"backtest\", \"evolution\", \"strategy generation\", \"candidate strategy\".",{"claudeCode":12},"TradeMemory Protocol",{"basePath":346,"githubOwner":259,"githubRepo":260,"locale":420,"slug":348,"type":262},"de",{"evaluate":422,"extract":428},{"promptVersionExtension":214,"promptVersionScoring":215,"score":423,"tags":424,"targetMarket":226,"tier":227},100,[221,425,225,426,223,427],"ai","audit","llm",{"commitSha":268,"license":253},{"parentExtensionId":430,"repoId":270,"translatedFrom":431},"k170vxkqee48k2xq1v55a025nh86nzn7","k171p5pgbfbm5g4k5sa3y4cj9s86m6hk",[425,426,223,427,225,221],{"evaluatedAt":434,"extractAt":408,"updatedAt":435},1778693678813,1778693798788,{"_creationTime":437,"_id":438,"community":439,"display":440,"identity":444,"providers":445,"relations":451,"tags":453,"workflow":454},1778693810753.2522,"k17e9x8nbeqn99r3hpez0fzfs186n9jv",{"reviewCount":8},{"description":441,"installMethods":442,"name":443,"sourceUrl":14},"Risikomanagement-Domänenwissen für Handelsagenten – Überwachung des affektiven Zustands, Positionsbestimmung, Drawdown-Management, Tilt-Erkennung und Verhaltensrichtlinien. Verwenden Sie dies beim Prüfen des Risikos vor Trades, beim Verwalten von Drawdowns, beim Erkennen von Verhaltensabweichungen oder beim Erzwingen von Disziplin. Löst bei \"risk\", \"drawdown\", \"tilt\", \"position size\", \"lot size\", \"confidence\", \"revenge trading\", \"overtrading\", \"discipline\" aus.",{"claudeCode":12},"Risk Management",{"basePath":354,"githubOwner":259,"githubRepo":260,"locale":420,"slug":356,"type":262},{"evaluate":446,"extract":450},{"promptVersionExtension":214,"promptVersionScoring":215,"score":423,"tags":447,"targetMarket":226,"tier":227},[221,356,224,448,449],"behavioral-analysis","finance",{"commitSha":268,"license":253},{"parentExtensionId":430,"repoId":270,"translatedFrom":452},"k17bgwvhb6h29py715de1cm9xd86msq6",[224,448,449,356,221],{"evaluatedAt":455,"extractAt":408,"updatedAt":456},1778693700524,1778693810753,{"_creationTime":458,"_id":459,"community":460,"display":461,"identity":464,"providers":465,"relations":470,"tags":472,"workflow":473},1778693819124.3687,"k177re651qqdxa2pxznqy4qzx186mgmm",{"reviewCount":8},{"description":462,"installMethods":463,"name":364,"sourceUrl":14},"Domänenwissen für die KI-Trading-Erinnerung – Outcome-Weighted Memory (OWM)-Architektur, 5 Speichertypen, Abrufbewertung und Verhaltensanalyse. Verwenden Sie dies beim Aufzeichnen von Trades, beim Abrufen ähnlicher Kontexte, bei der Leistungsanalyse oder bei der Überprüfung von Verhaltensabweichungen. Löst bei \"record trade\", \"remember trade\", \"recall\", \"similar trades\", \"performance\", \"behavioral\", \"disposition\", \"affective state\", \"confidence\" aus.",{"claudeCode":12},{"basePath":362,"githubOwner":259,"githubRepo":260,"locale":420,"slug":364,"type":262},{"evaluate":466,"extract":469},{"promptVersionExtension":214,"promptVersionScoring":215,"score":423,"tags":467,"targetMarket":226,"tier":227},[221,425,225,449,468],"python",{"commitSha":268},{"parentExtensionId":430,"repoId":270,"translatedFrom":471},"k173a67a16bpq0e29wjd85v71986nx03",[425,449,225,468,221],{"evaluatedAt":474,"extractAt":408,"updatedAt":475},1778693719816,1778693819124,{"_creationTime":477,"_id":478,"community":479,"display":480,"identity":483,"providers":484,"relations":489,"tags":491,"workflow":492},1778693793330.2832,"k177d4r606hjhrdzjy7nbxena986ncpn",{"reviewCount":8},{"description":481,"installMethods":482,"name":340,"sourceUrl":14},"Brücke zwischen Binance-Handelsereignissen und dem TradeMemory Protocol.\nJournalisiert automatisch Trades, ruft ähnliche vergangene Setups ab, erkennt Verhaltensverzerrungen\nund bietet ergebnisgewichteten Abruf für KI-Trading-Agenten.\nVerwenden Sie diese Fähigkeit nach der Ausführung von Binance-Spot-Trades, um ein persistentes Gedächtnis aufzubauen.\n",{"claudeCode":12},{"basePath":338,"githubOwner":259,"githubRepo":260,"locale":420,"slug":340,"type":262},{"evaluate":485,"extract":488},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":486,"targetMarket":226,"tier":227},[221,449,225,487,224],"journaling",{"commitSha":268},{"repoId":270,"translatedFrom":490},"k17em57x7pnqhv6x3a2s5g5wv586mjq6",[224,449,487,225,221],{"evaluatedAt":493,"extractAt":408,"updatedAt":494},1778693660212,1778693793330,{"_creationTime":496,"_id":497,"community":498,"display":499,"identity":505,"providers":508,"relations":517,"tags":520,"workflow":521},1778699508017.8022,"k17ayarn0e5prt2n3bh82hxn5n86nv51",{"reviewCount":8},{"description":500,"installMethods":501,"name":503,"sourceUrl":504},"Context Runtime für KI-Agenten — 59 MCP-Tools, 10 Lesemodi, über 95 Shell-Muster, Tree-sitter AST für 18 Sprachen. Komprimiert LLM-Kontext um bis zu 99%. Verwenden Sie es beim Lesen von Dateien, Ausführen von Shell-Befehlen, Suchen von Code oder Erkunden von Verzeichnissen. Automatische Installation, falls nicht vorhanden.",{"claudeCode":502},"yvgude/lean-ctx","lean-ctx","https://github.com/yvgude/lean-ctx",{"basePath":506,"githubOwner":507,"githubRepo":503,"locale":420,"slug":503,"type":262},"skills/lean-ctx","yvgude",{"evaluate":509,"extract":516},{"promptVersionExtension":214,"promptVersionScoring":215,"score":423,"tags":510,"targetMarket":226,"tier":227},[511,224,512,513,514,515],"context-compression","cli-tools","developer-tools","rust","code-analysis",{"commitSha":268},{"repoId":518,"translatedFrom":519},"kd7dxtfr9j3z54hs3bz0218e1n86may0","k170fxxh22hdspg4vr94whgj1986mpr9",[224,512,515,511,513,514],{"evaluatedAt":522,"extractAt":523,"updatedAt":524},1778699456179,1778699438912,1778699508017,{"_creationTime":526,"_id":527,"community":528,"display":529,"identity":535,"providers":539,"relations":547,"tags":550,"workflow":551},1778696833339.6243,"k174g80xa9zxhydbncvpf0xzy986nvx5",{"reviewCount":8},{"description":530,"installMethods":531,"name":533,"sourceUrl":534},"Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.",{"claudeCode":532},"sanjay3290/ai-skills","manus","https://github.com/sanjay3290/ai-skills",{"basePath":536,"githubOwner":537,"githubRepo":538,"locale":18,"slug":533,"type":262},"skills/manus","sanjay3290","ai-skills",{"evaluate":540,"extract":546},{"promptVersionExtension":214,"promptVersionScoring":215,"score":423,"tags":541,"targetMarket":226,"tier":227},[224,542,543,544,545],"autonomous-execution","research","automation","api-integration",{"commitSha":268},{"parentExtensionId":548,"repoId":549},"k17es37z10n1sw6t2m3f0vsydx86mnje","kd71np0fyqg23qg8w2hcfw0h0h86nkn0",[224,545,544,542,543],{"evaluatedAt":552,"extractAt":553,"updatedAt":552},1778697107270,1778696833339]