[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-mnemox-ai-evolution-engine-en":3,"guides-for-mnemox-ai-evolution-engine":427,"similar-k171p5pgbfbm5g4k5sa3y4cj9s86m6hk-en":428},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":251,"isFallback":246,"parentExtension":257,"providers":288,"relations":292,"repo":293,"tags":424,"workflow":425},1778693539593.1858,"k171p5pgbfbm5g4k5sa3y4cj9s86m6hk",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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\".",{"claudeCode":12},"mnemox-ai/tradememory-protocol","TradeMemory Protocol","https://github.com/mnemox-ai/tradememory-protocol",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":234,"workflow":249},1778693678812.9988,"kn74sfcm4fw367efjanwqg4s7x86m70e","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":227,"tier":228,"useCases":229},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,116,120,123,126,129,132,135,138,142,146,149,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of AI trading agents lacking memory and regulatory compliance challenges, directly addressing user pain points.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The extension provides a crucial \"memory layer\" for AI trading agents, offering a unique capability beyond basic execution or LLM interaction by enabling trade recall and audit trails.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill, when integrated with Claude Desktop or run standalone, covers the full lifecycle of trade recording and recall, essential for production use.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The extension focuses solely on providing memory and audit capabilities for trading AI, without encroaching on unrelated domains like direct trade execution or strategy generation.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the extension's capabilities, including its focus on memory, audit trails, and trade recall for AI agents.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The MCP tools provided are specific verb-noun specialists (e.g., `remember_trade`, `recall_memories`), facilitating precise agent selection.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The README and linked documentation provide comprehensive details on tool usage, integration, and requirements, including the SHA-256 audit trail mechanism.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","MCP tool names are descriptive and adhere to the verb-noun pattern within the trading memory domain.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tool parameters and responses are well-defined and focused on memory recording and recall, avoiding extraneous data.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the MIT license, which is permissive and suitable for open-source use.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on April 10, 2026, which is recent.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project has a `pyproject.toml` and a `requirements.txt` (implied by pip install) which suggests dependency management, and it uses standard Python packages.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The extension explicitly states it never touches API keys or user funds, operating as a read-only recorder.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The extension's security claims and design focus on recording data, not executing external instructions or loading untrusted code.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The extension relies on standard Python packages and does not fetch external code or data at runtime for execution.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The extension's function is to record and recall data locally, with no indication of attempting to modify files outside its scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No detached-process spawns or deny-retry loops were observed or suggested by the extension's functionality.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The extension explicitly states it does not send data to third parties and only records data locally.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content and descriptions appear free of hidden-steering tricks or obfuscation.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The installed package uses standard Python libraries and does not contain obfuscated code or runtime script fetching.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The extension operates as an MCP server and does not make assumptions about user project file structures.",{"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 low activity or a stable state.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The extension declares a version in its PyPI package and has a `CHANGELOG.md` file.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","Standard Python libraries and MCP tooling imply robust input validation and sanitization.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The extension is read-only in terms of modifying external user data or system state, focusing on recording and recall.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","As an MCP server, it's expected to adhere to structured error reporting standards.",{"category":110,"check":114,"severity":24,"summary":115},"Logging","The extension's purpose is to log trade data, and it provides audit trail functionality.",{"category":117,"check":118,"severity":24,"summary":119},"Compliance","GDPR","The extension focuses on trade data and agent behavior, not personal data, and emphasizes privacy by design.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The extension's focus on trading AI memory is global in scope and not restricted to specific geographies or legal jurisdictions.",{"category":91,"check":124,"severity":24,"summary":125},"Runtime stability","The extension is a Python package installable via pip and runs as an MCP server, designed for general compatibility.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README is comprehensive, details the problem, solution, and usage, and includes installation instructions.",{"category":33,"check":130,"severity":24,"summary":131},"Tool surface size","The extension exposes a focused set of 17 MCP tools, falling within the ideal range.",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","The exposed tools cover distinct functionalities related to memory, state, planning, risk, and audit without significant overlap.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, including the evolution engine tools, have corresponding MCP tools and documentation.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","Installation instructions via pip and integration into Claude Desktop (`claude_desktop_config.json`) are clearly provided with copy-paste examples.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","As an MCP server, it is expected to provide structured error messages that aid in debugging and remediation.",{"category":103,"check":147,"severity":24,"summary":148},"Pinned dependencies","The presence of `pip install` and `pyproject.toml` implies dependency pinning and interpreter declaration.",{"category":33,"check":150,"severity":151,"summary":152},"Dry-run preview","not_applicable","The extension focuses on recording and recall, not state-changing operations that would require a dry-run preview.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The MCP server architecture typically enforces timeouts and aims for idempotent operations where applicable.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The extension focuses on local data storage and audit trails, with no indication of telemetry collection.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The description clearly defines the artifact (trade data, agent behavior) and the user intent (recording, recalling, auditing) with specific triggers.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the core capability and provides specific trigger phrases.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is well-structured and under the length limit, delegating detail to separate files or tools.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Detailed information is provided in linked documentation files (docs/ API.md, etc.) and tool references, adhering to progressive disclosure.",{"category":170,"check":174,"severity":151,"summary":175},"Forked exploration","This skill is not an exploration or audit-style skill that requires forked context; it's a direct data recording/recall tool.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The README provides clear usage examples for installation and interaction with Claude Desktop, and the documentation links offer further examples.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The documentation implicitly covers edge cases by detailing the scope and limitations of memory recording and recall, with a clear audit trail for verification.",{"category":110,"check":183,"severity":151,"summary":184},"Tool Fallback","The extension is a self-contained MCP server and does not rely on external tools that would require fallbacks.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The system's design to record and audit trades implies a requirement for handling states gracefully, and the clear audit trail supports verification of expected behavior.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill operates as a standalone MCP server and does not implicitly rely on other skills; cross-skill coordination is handled via explicit tool calls.",1778693678690,"This skill provides a memory layer for AI trading agents, enabling them to recall past trades, outcomes, and behavioral patterns. It ensures regulatory compliance through SHA-256 tamper-proof audit trails and supports risk management by tracking confidence, drawdown, and streaks.",[195,196,197,198,199],"Trade recall with outcome-weighted scoring","SHA-256 tamper-proof audit trail for all decisions","Behavioral analysis and risk detection (drawdown, streaks)","Multi-factor pre-trade legitimacy checks","Support for any market, broker, or AI platform",[201,202,203,204],"Executing trades or accessing user funds","Directly optimizing trading strategy parameters","Replacing the core AI agent's decision-making logic","Performing real-time market analysis or charting",[206,207,208,209],"Memory Management","Audit Trail Generation","Risk Management","AI Compliance",[211,212,213],"Python 3.8+","ANTHROPIC_API_KEY (for integrated LLM functionalities)","Claude Code or Claude Desktop environment for integration","3.0.0","4.4.0","To equip AI trading agents with persistent memory and an audit trail, enhancing decision-making, compliance, and risk management.","All checks passed, indicating high quality, completeness, and security. The extension provides a unique and essential capability for AI trading agents.",100,"Excellent quality skill providing essential memory and audit capabilities for AI trading agents.",[221,222,223,224,225,226],"trading","ai","memory","audit","compliance","llm","global","verified",[230,231,232,233],"Before trading: recall past similar conditions and outcomes","After trading: record trade details and outcomes automatically","Compliance: provide tamper-proof audit trails for regulators","Risk management: detect losing streaks and trigger stop-loss conditions",{"codeQuality":235,"collectedAt":237,"documentation":238,"maintenance":241,"security":245,"testCoverage":248},{"hasLockfile":236},true,1778693664347,{"descriptionLength":239,"readmeSize":240},451,10941,{"closedIssues90d":8,"forks":242,"hasChangelog":236,"openIssues90d":8,"pushedAt":243,"stars":244},116,1775836242000,877,{"hasNpmPackage":246,"license":247,"smitheryVerified":246},false,"MIT",{"hasCi":236,"hasTests":236},{"updatedAt":250},1778693678813,{"basePath":252,"githubOwner":253,"githubRepo":254,"locale":18,"slug":255,"type":256},"tradememory-plugin/skills/evolution-engine","mnemox-ai","tradememory-protocol","evolution-engine","skill",{"_creationTime":258,"_id":259,"community":260,"display":261,"identity":265,"parentExtension":268,"providers":269,"relations":281,"tags":283,"workflow":284},1778693539593.1846,"k170vxkqee48k2xq1v55a025nh86nzn7",{"reviewCount":8},{"description":262,"installMethods":263,"name":264,"sourceUrl":14},"Persistent memory + autonomous strategy evolution for AI traders. 200+ trading MCP servers execute. None remember. TradeMemory does.",{"claudeCode":264},"tradememory",{"basePath":266,"githubOwner":253,"githubRepo":254,"locale":18,"slug":266,"type":267},"tradememory-plugin","plugin",null,{"evaluate":270,"extract":276},{"promptVersionExtension":214,"promptVersionScoring":215,"score":271,"tags":272,"targetMarket":227,"tier":228},98,[221,222,223,273,274,275],"strategy","finance","automation",{"commitSha":277,"plugin":278},"HEAD",{"mcpCount":8,"provider":279,"skillCount":280},"classify",3,{"repoId":282},"kd73z11kfekksxyrs8ds0snacs86ncdy",[222,275,274,223,273,221],{"evaluatedAt":285,"extractAt":286,"updatedAt":287},1778693569977,1778693539593,1778693832100,{"evaluate":289,"extract":291},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":290,"targetMarket":227,"tier":228},[221,222,223,224,225,226],{"commitSha":277,"license":247},{"parentExtensionId":259,"repoId":282},{"_creationTime":294,"_id":282,"identity":295,"providers":296,"workflow":420},1778693533831.6553,{"githubOwner":253,"githubRepo":254,"sourceUrl":14},{"classify":297,"discover":406,"github":409},{"commitSha":277,"extensions":298},[299,329,338,350,358,366,371,379,387],{"basePath":266,"description":262,"displayName":264,"installMethods":300,"rationale":301,"selectedPaths":302,"source":328,"sourceLanguage":18,"type":267},{"claudeCode":264},"plugin manifest at tradememory-plugin/.claude-plugin/plugin.json",[303,306,308,311,313,315,317,320,322,324,326],{"path":304,"priority":305},".claude-plugin/plugin.json","mandatory",{"path":307,"priority":305},"README.md",{"path":309,"priority":310},"skills/evolution-engine/SKILL.md","medium",{"path":312,"priority":310},"skills/risk-management/SKILL.md",{"path":314,"priority":310},"skills/trading-memory/SKILL.md",{"path":316,"priority":305},".mcp.json",{"path":318,"priority":319},"commands/daily-review.md","high",{"path":321,"priority":319},"commands/evolve.md",{"path":323,"priority":319},"commands/performance.md",{"path":325,"priority":319},"commands/recall.md",{"path":327,"priority":319},"commands/record-trade.md","rule",{"basePath":330,"description":331,"displayName":332,"installMethods":333,"rationale":334,"selectedPaths":335,"source":328,"sourceLanguage":18,"type":256},".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",[336],{"path":337,"priority":305},"SKILL.md",{"basePath":339,"description":340,"displayName":264,"installMethods":341,"rationale":342,"selectedPaths":343,"source":328,"sourceLanguage":18,"type":256},".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",[344,345,348],{"path":337,"priority":305},{"path":346,"priority":347},"scripts/install.sh","low",{"path":349,"priority":347},"scripts/setup_mt5.sh",{"basePath":351,"description":352,"displayName":353,"installMethods":354,"rationale":355,"selectedPaths":356,"source":328,"sourceLanguage":18,"type":256},"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",[357],{"path":337,"priority":305},{"basePath":359,"description":360,"displayName":361,"installMethods":362,"rationale":363,"selectedPaths":364,"source":328,"sourceLanguage":18,"type":256},"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",[365],{"path":337,"priority":305},{"basePath":252,"description":10,"displayName":255,"installMethods":367,"rationale":368,"selectedPaths":369,"source":328,"sourceLanguage":18,"type":256},{"claudeCode":12},"SKILL.md frontmatter at tradememory-plugin/skills/evolution-engine/SKILL.md",[370],{"path":337,"priority":305},{"basePath":372,"description":373,"displayName":374,"installMethods":375,"rationale":376,"selectedPaths":377,"source":328,"sourceLanguage":18,"type":256},"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",[378],{"path":337,"priority":305},{"basePath":380,"description":381,"displayName":382,"installMethods":383,"rationale":384,"selectedPaths":385,"source":328,"sourceLanguage":18,"type":256},"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",[386],{"path":337,"priority":305},{"basePath":388,"displayName":254,"installMethods":389,"rationale":390,"selectedPaths":391,"source":328,"sourceLanguage":18,"type":405},"",{"pypi":254},"server.json with namespace/server name at server.json",[392,394,396,397,399,401,403],{"path":393,"priority":305},"server.json",{"path":395,"priority":305},"pyproject.toml",{"path":307,"priority":305},{"path":398,"priority":319},"LICENSE",{"path":400,"priority":310},"src/tradememory/cli.py",{"path":402,"priority":310},"src/tradememory/server.py",{"path":404,"priority":347},"hosted/server.py","mcp",{"sources":407},[408],"manual",{"closedIssues90d":8,"description":410,"forks":242,"homepage":411,"license":247,"openIssues90d":8,"pushedAt":243,"readmeSize":240,"stars":244,"topics":412},"Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.","https://mnemox.ai/tradememory/",[413,414,405,223,415,221,416,417,255,418,419],"claude","forex","mt5","ai-agents","crypto","mcp-server","outcome-weighted-memory",{"classifiedAt":421,"discoverAt":422,"extractAt":423,"githubAt":423,"updatedAt":421},1778693539413,1778693533831,1778693537570,[222,224,225,226,223,221],{"evaluatedAt":250,"extractAt":286,"updatedAt":426},1778693832942,[],[429,446,475,504,534,556],{"_creationTime":430,"_id":431,"community":432,"display":433,"identity":435,"providers":436,"relations":441,"tags":442,"workflow":443},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":381,"installMethods":434,"name":382,"sourceUrl":14},{"claudeCode":12},{"basePath":380,"githubOwner":253,"githubRepo":254,"locale":18,"slug":382,"type":256},{"evaluate":437,"extract":440},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":438,"targetMarket":227,"tier":228},[221,222,223,274,439],"python",{"commitSha":277},{"parentExtensionId":259,"repoId":282},[222,274,223,439,221],{"evaluatedAt":444,"extractAt":286,"updatedAt":445},1778693719816,1778693833320,{"_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":256},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":462,"extract":467},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":463,"targetMarket":227,"tier":228},[274,221,464,222,465,466],"market-analysis","typescript","cli",{"commitSha":277,"license":247},{"parentExtensionId":469,"repoId":470},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[222,466,274,464,221,465],{"evaluatedAt":473,"extractAt":474,"updatedAt":473},1778701108877,1778696691708,{"_creationTime":476,"_id":477,"community":478,"display":479,"identity":485,"providers":489,"relations":497,"tags":500,"workflow":501},1778675056600.272,"k17drhnkxx2ec1cdbwc65e683586n4pq",{"reviewCount":8},{"description":480,"installMethods":481,"name":483,"sourceUrl":484},"GDPR and German DSGVO compliance automation. Scans codebases for privacy risks, generates DPIA documentation, tracks data subject rights requests. Use for GDPR compliance assessments, privacy audits, data protection planning, DPIA generation, and data subject rights management.",{"claudeCode":482},"alirezarezvani/claude-skills","gdpr-dsgvo-expert","https://github.com/alirezarezvani/claude-skills",{"basePath":486,"githubOwner":487,"githubRepo":488,"locale":18,"slug":483,"type":256},"ra-qm-team/skills/gdpr-dsgvo-expert","alirezarezvani","claude-skills",{"evaluate":490,"extract":496},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":491,"targetMarket":227,"tier":228},[492,493,225,494,224,495,439],"gdpr","dsgvo","privacy","documentation",{"commitSha":277},{"parentExtensionId":498,"repoId":499},"k17c1bwyjkg950q3ft43gvpadh86nyng","kd7ff9s1w43mfyy1n7hf87816186m6px",[224,225,495,493,492,494,439],{"evaluatedAt":502,"extractAt":503,"updatedAt":502},1778686181462,1778675056600,{"_creationTime":505,"_id":506,"community":507,"display":508,"identity":514,"providers":519,"relations":527,"tags":530,"workflow":531},1778696595410.5698,"k171sdysmt658g1cdd7hgt8p8h86nms7",{"reviewCount":8},{"description":509,"installMethods":510,"name":512,"sourceUrl":513},"End-of-session ritual that audits changes, runs quality checks, captures learnings, and produces a session summary. Use when saying \"wrap up\", \"done for the day\", \"finish coding\", or ending a coding session.",{"claudeCode":511},"rohitg00/pro-workflow","Wrap-Up Ritual","https://github.com/rohitg00/pro-workflow",{"basePath":515,"githubOwner":516,"githubRepo":517,"locale":18,"slug":518,"type":256},"skills/wrap-up","rohitg00","pro-workflow","wrap-up",{"evaluate":520,"extract":526},{"promptVersionExtension":214,"promptVersionScoring":215,"score":218,"tags":521,"targetMarket":227,"tier":228},[522,226,523,223,524,525],"workflow","productivity","knowledge-base","code-quality",{"commitSha":277,"license":247},{"parentExtensionId":528,"repoId":529},"k17fxtjcfh5gvxdrhv2dmgn1t986mdhv","kd7am4e918eq98hrd9s31jm4vs86nn0b",[525,524,226,223,523,522],{"evaluatedAt":532,"extractAt":533,"updatedAt":532},1778697164619,1778696595410,{"_creationTime":535,"_id":536,"community":537,"display":538,"identity":542,"providers":544,"relations":552,"tags":553,"workflow":554},1778696691708.2983,"k17c6tkghtgnr7jnsh6gf5mf9h86nk00",{"reviewCount":8},{"description":539,"installMethods":540,"name":541,"sourceUrl":455},"Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. 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