[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-mnemox-ai-tradememory-en":3,"guides-for-mnemox-ai-tradememory":397,"similar-k174jrt9tnryf6b31jfh0hn3js86ny47-en":398},{"_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":393,"workflow":394},1778693539593.185,"k174jrt9tnryf6b31jfh0hn3js86ny47",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"AI trading memory with outcome-weighted recall and autonomous strategy evolution. 17 MCP tools, 1,233 tests, works with any trading platform.",{"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":228,"workflow":244},1778693611218.2073,"kn76ggkr34ase0888hqqfqp95986n41b","en",{"checks":20,"evaluatedAt":195,"extensionSummary":196,"features":197,"nonGoals":203,"promptVersionExtension":207,"promptVersionScoring":208,"purpose":209,"rationale":210,"score":211,"summary":212,"tags":213,"targetMarket":221,"tier":222,"useCases":223},[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 states the problem of AI agents lacking persistent memory and the inability to explain past trading decisions, which TradeMemory aims to solve.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","TradeMemory offers a distinct value proposition by providing a dedicated memory layer for AI trading agents, including outcome-weighted recall and autonomous strategy evolution, which goes beyond simple API wrappers or default LLM behavior.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The extension appears production-ready with comprehensive documentation, installation instructions, and a clear focus on recording and recalling trade data, covering the necessary lifecycle for its stated purpose.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on providing memory and strategy evolution for AI trading agents, a coherent and single domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately and concisely reflects the core capabilities of the extension, highlighting memory, recall, and strategy evolution.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The extension exposes a set of narrow, verb-noun specialist tools for managing trading memory and evolution, avoiding a single generalist execution tool.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","Environment variables and their purposes are clearly documented, including defaults where applicable.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names are descriptive and follow a consistent verb-noun pattern relevant to trading memory and strategy evolution.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Tool inputs and outputs are structured and appear to request/return only necessary data for their stated tasks, without excessive diagnostic dumps.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the MIT license, as indicated by the LICENSE file and README, which is a permissive open-source license.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The repository shows recent commits, with the last push being in April 2026, indicating active maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project appears to manage its dependencies via pip, and the setup scripts and installation instructions suggest a standard Python dependency management approach.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","Secrets such as API keys are handled via environment variables and are clearly marked as optional, with no indication of them being hardcoded or echoed in logs.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The extension appears to treat external data as untrusted, with no indication of executing arbitrary code from loaded data or downloading and executing remote scripts.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The extension relies on standard Python package installation 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 operates within the project directory, writing to a local SQLite database, and does not attempt to modify files outside its designated scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No evidence of detached process spawns or retry loops around denied tool calls was found in the provided code snippets.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The extension clearly states it does not make outbound network calls except for fetching market data from Binance (documented) and authenticating with the Anthropic API (optional, documented). No confidential data exfiltration is apparent.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content appears free of hidden steering tricks, HTML comments with instructions, or invisible Unicode characters.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The bundled scripts are plain Python and do not appear to use obfuscation techniques like base64 encoding or runtime script fetching.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The extension manages its own data storage locally and does not make assumptions about the user's project file layout beyond its own database.",{"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 a very new project or that the issue tracker is not actively used for discussion.",{"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 PyPI version, along with a CHANGELOG.md.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","While specific schema validation libraries aren't explicitly detailed in the provided snippets, the use of Python and Pydantic for API setup suggests a robust approach to input handling.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The extension is primarily data recording and recall; destructive operations are not a primary function and are not apparent.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","The provided scripts and documentation indicate proper error handling, with clear messages and fallbacks for non-critical issues like OS compatibility. The MT5 connection test also includes error handling.",{"category":110,"check":114,"severity":24,"summary":115},"Logging","The extension focuses on recording to its own SQLite database and does not appear to have explicit logging mechanisms for user review, which is acceptable given its nature.",{"category":117,"check":118,"severity":24,"summary":119},"Compliance","GDPR","The extension operates on trade data and strategy parameters, which may indirectly relate to personal data if used by an individual trader, but it does not explicitly handle or submit PII to third parties without approval.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The extension is designed to be global and platform-agnostic, with no specific regional or jurisdictional logic detected.",{"category":91,"check":124,"severity":24,"summary":125},"Runtime stability","The extension specifies Python 3.10+ and notes Windows-only compatibility for the MT5 Python API, providing graceful fallbacks for other OS.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README file is comprehensive, clearly stating the extension's purpose, features, and installation/setup instructions.",{"category":33,"check":130,"severity":24,"summary":131},"Tool surface size","The extension exposes 17 MCP tools, which falls within the recommended range for comprehensive functionality without being overly bloated.",{"category":40,"check":133,"severity":24,"summary":134},"Overlapping near-synonym tools","The tool names are distinct and cover specific functionalities within memory management and strategy evolution, with no significant overlap in near-synonyms.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, including the 17 MCP tools, are implemented and documented in the SKILL.md and README.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","Clear installation instructions via pip and setup for Claude Desktop/Code are provided in both the README and SKILL.md, along with verification steps.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","Error messages for setup and connection tests are clear, indicating the problem and suggesting next steps or potential causes.",{"category":103,"check":147,"severity":24,"summary":148},"Pinned dependencies","The setup scripts install dependencies using pip, and the project structure implies standard Python dependency management practices are followed.",{"category":33,"check":150,"severity":151,"summary":152},"Dry-run preview","not_applicable","The extension's core function is data recording and recall, not state-changing operations that would typically require a dry-run preview.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The extension's operations, particularly trade recording, are designed to be idempotent where applicable, and standard Python error handling with potential retries on network calls would be expected.",{"category":117,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The extension explicitly states that no data is sent to third parties except for optional LLM API calls and Binance data fetching, aligning with opt-in principles.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md and README clearly define the extension's purpose of providing AI trading memory and strategy evolution, with specific use cases and triggers outlined.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter in SKILL.md is concise and effectively summarizes the core capabilities of AI trading memory and strategy evolution.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is well-structured and under the typical line limit, delegating deeper material to separate documentation files.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Deeper material like the OWM Framework and API Reference is linked to separate documentation files, enabling progressive disclosure.",{"category":170,"check":174,"severity":151,"summary":175},"Forked exploration","This skill is not primarily an exploration or code-review tool that would necessitate context: fork.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The README and SKILL.md provide clear, end-to-end examples for recording trades, recalling memories, checking performance, and using the evolution engine.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The documentation mentions platform compatibility and environment variables, implying handling of different setups. The failure taxonomy link also suggests consideration of failure modes.",{"category":110,"check":183,"severity":24,"summary":184},"Tool Fallback","The skill lists requirements like Python and the MCP server, and provides instructions for manual setup and local server execution, indicating optionality and fallback paths.",{"category":91,"check":186,"severity":24,"summary":187},"Stack assumptions","The documentation clearly states Python 3.10+ as a requirement and notes OS-specific limitations for the MT5 API, ensuring stack assumptions are declared.",{"category":189,"check":190,"severity":24,"summary":191},"Safety","Halt on unexpected state","The installation and setup scripts include checks for Python and dependencies, and the MT5 sync setup script includes checks for OS and connection, halting on unexpected states.",{"category":91,"check":193,"severity":24,"summary":194},"Cross-skill coupling","The skill focuses on its core memory and evolution tasks and links to a related skill ('Strategy Validator') explicitly, avoiding implicit coupling.",1778693611111,"This skill provides persistent memory for AI trading agents, recording trades with outcome-weighted recall and enabling autonomous strategy evolution. It offers 17 MCP tools and includes a robust setup for integration with various trading platforms and AI agents.",[198,199,200,201,202],"Outcome-weighted memory recall","LLM-powered autonomous strategy evolution","SHA-256 tamper-proof trading decision records","Platform-agnostic trade data integration","17 comprehensive MCP tools for memory and evolution",[204,205,206],"Executing trades or managing user funds","Directly interacting with trading platforms (requires separate sync scripts)","Replacing core AI agent decision-making capabilities","3.0.0","4.4.0","To equip AI trading agents with persistent memory, enabling them to learn from past trades, recall relevant information, and autonomously evolve new trading strategies.","Excellent documentation and implementation quality across all checks. Minor info on specific validation library and logging details do not detract from overall strong showing.",99,"A high-quality, production-ready skill for AI trading memory and strategy evolution with comprehensive documentation and tooling.",[214,215,216,217,218,219,220],"trading","finance","memory","strategy","ai","python","mcp","global","verified",[224,225,226,227],"Recording and recalling trade history with outcome-weighted scoring","Discovering new trading patterns and strategies from raw price data","Analyzing behavioral patterns and agent state (confidence, drawdown)","Generating and verifying tamper-proof audit trails for regulatory compliance",{"codeQuality":229,"collectedAt":231,"documentation":232,"maintenance":235,"security":240,"testCoverage":243},{"hasLockfile":230},true,1778693588873,{"descriptionLength":233,"readmeSize":234},141,10941,{"closedIssues90d":8,"forks":236,"hasChangelog":230,"manifestVersion":237,"openIssues90d":8,"pushedAt":238,"stars":239},116,"0.5.1",1775836242000,877,{"hasNpmPackage":241,"license":242,"smitheryVerified":241},false,"MIT",{"hasCi":230,"hasTests":230},{"updatedAt":245},1778693611218,{"basePath":247,"githubOwner":248,"githubRepo":249,"locale":18,"slug":13,"type":250},".skills/tradememory","mnemox-ai","tradememory-protocol","skill",null,{"evaluate":253,"extract":255},{"promptVersionExtension":207,"promptVersionScoring":208,"score":211,"tags":254,"targetMarket":221,"tier":222},[214,215,216,217,218,219,220],{"commitSha":256},"HEAD",{"repoId":258},"kd73z11kfekksxyrs8ds0snacs86ncdy",{"_creationTime":260,"_id":258,"identity":261,"providers":262,"workflow":389},1778693533831.6553,{"githubOwner":248,"githubRepo":249,"sourceUrl":14},{"classify":263,"discover":375,"github":378},{"commitSha":256,"extensions":264},[265,298,307,317,325,333,341,349,357],{"basePath":266,"description":267,"displayName":13,"installMethods":268,"rationale":269,"selectedPaths":270,"source":296,"sourceLanguage":18,"type":297},"tradememory-plugin","Persistent memory + autonomous strategy evolution for AI traders. 200+ trading MCP servers execute. None remember. TradeMemory does.",{"claudeCode":13},"plugin manifest at tradememory-plugin/.claude-plugin/plugin.json",[271,274,276,279,281,283,285,288,290,292,294],{"path":272,"priority":273},".claude-plugin/plugin.json","mandatory",{"path":275,"priority":273},"README.md",{"path":277,"priority":278},"skills/evolution-engine/SKILL.md","medium",{"path":280,"priority":278},"skills/risk-management/SKILL.md",{"path":282,"priority":278},"skills/trading-memory/SKILL.md",{"path":284,"priority":273},".mcp.json",{"path":286,"priority":287},"commands/daily-review.md","high",{"path":289,"priority":287},"commands/evolve.md",{"path":291,"priority":287},"commands/performance.md",{"path":293,"priority":287},"commands/recall.md",{"path":295,"priority":287},"commands/record-trade.md","rule","plugin",{"basePath":299,"description":300,"displayName":301,"installMethods":302,"rationale":303,"selectedPaths":304,"source":296,"sourceLanguage":18,"type":250},".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",[305],{"path":306,"priority":273},"SKILL.md",{"basePath":247,"description":10,"displayName":13,"installMethods":308,"rationale":309,"selectedPaths":310,"source":296,"sourceLanguage":18,"type":250},{"claudeCode":12},"SKILL.md frontmatter at .skills/tradememory/SKILL.md",[311,312,315],{"path":306,"priority":273},{"path":313,"priority":314},"scripts/install.sh","low",{"path":316,"priority":314},"scripts/setup_mt5.sh",{"basePath":318,"description":319,"displayName":320,"installMethods":321,"rationale":322,"selectedPaths":323,"source":296,"sourceLanguage":18,"type":250},"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",[324],{"path":306,"priority":273},{"basePath":326,"description":327,"displayName":328,"installMethods":329,"rationale":330,"selectedPaths":331,"source":296,"sourceLanguage":18,"type":250},"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",[332],{"path":306,"priority":273},{"basePath":334,"description":335,"displayName":336,"installMethods":337,"rationale":338,"selectedPaths":339,"source":296,"sourceLanguage":18,"type":250},"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",[340],{"path":306,"priority":273},{"basePath":342,"description":343,"displayName":344,"installMethods":345,"rationale":346,"selectedPaths":347,"source":296,"sourceLanguage":18,"type":250},"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",[348],{"path":306,"priority":273},{"basePath":350,"description":351,"displayName":352,"installMethods":353,"rationale":354,"selectedPaths":355,"source":296,"sourceLanguage":18,"type":250},"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",[356],{"path":306,"priority":273},{"basePath":358,"displayName":249,"installMethods":359,"rationale":360,"selectedPaths":361,"source":296,"sourceLanguage":18,"type":220},"",{"pypi":249},"server.json with namespace/server name at server.json",[362,364,366,367,369,371,373],{"path":363,"priority":273},"server.json",{"path":365,"priority":273},"pyproject.toml",{"path":275,"priority":273},{"path":368,"priority":287},"LICENSE",{"path":370,"priority":278},"src/tradememory/cli.py",{"path":372,"priority":278},"src/tradememory/server.py",{"path":374,"priority":314},"hosted/server.py",{"sources":376},[377],"manual",{"closedIssues90d":8,"description":379,"forks":236,"homepage":380,"license":242,"openIssues90d":8,"pushedAt":238,"readmeSize":234,"stars":239,"topics":381},"Decision audit trail + persistent memory for AI trading agents. Outcome-weighted recall, SHA-256 tamper detection, 17 MCP tools.","https://mnemox.ai/tradememory/",[382,383,220,216,384,214,385,386,336,387,388],"claude","forex","mt5","ai-agents","crypto","mcp-server","outcome-weighted-memory",{"classifiedAt":390,"discoverAt":391,"extractAt":392,"githubAt":392,"updatedAt":390},1778693539413,1778693533831,1778693537570,[218,215,220,216,219,217,214],{"evaluatedAt":245,"extractAt":395,"updatedAt":396},1778693539593,1778693832326,[],[399,417,446,466,485,514],{"_creationTime":400,"_id":401,"community":402,"display":403,"identity":405,"providers":406,"relations":411,"tags":413,"workflow":414},1778693539593.1863,"k173a67a16bpq0e29wjd85v71986nx03",{"reviewCount":8},{"description":351,"installMethods":404,"name":352,"sourceUrl":14},{"claudeCode":12},{"basePath":350,"githubOwner":248,"githubRepo":249,"locale":18,"slug":352,"type":250},{"evaluate":407,"extract":410},{"promptVersionExtension":207,"promptVersionScoring":208,"score":408,"tags":409,"targetMarket":221,"tier":222},100,[214,218,216,215,219],{"commitSha":256},{"parentExtensionId":412,"repoId":258},"k170vxkqee48k2xq1v55a025nh86nzn7",[218,215,216,219,214],{"evaluatedAt":415,"extractAt":395,"updatedAt":416},1778693719816,1778693833320,{"_creationTime":418,"_id":419,"community":420,"display":421,"identity":427,"providers":432,"relations":439,"tags":442,"workflow":443},1778696691708.3274,"k170az7r02e9e2v47mpy80kx6n86nff3",{"reviewCount":8},{"description":422,"installMethods":423,"name":425,"sourceUrl":426},"Detect current market regime using npx neural-trader — bull/bear/ranging/volatile classification with recommended strategy",{"claudeCode":424},"ruvnet/ruflo","Trader Regime","https://github.com/ruvnet/ruflo",{"basePath":428,"githubOwner":429,"githubRepo":430,"locale":18,"slug":431,"type":250},"plugins/ruflo-neural-trader/skills/trader-regime","ruvnet","ruflo","trader-regime",{"evaluate":433,"extract":438},{"promptVersionExtension":207,"promptVersionScoring":208,"score":408,"tags":434,"targetMarket":221,"tier":222},[215,214,435,218,436,437],"market-analysis","typescript","cli",{"commitSha":256,"license":242},{"parentExtensionId":440,"repoId":441},"k17drge8h1fgzchr0p4jaeg33n86mwmy","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[218,437,215,435,214,436],{"evaluatedAt":444,"extractAt":445,"updatedAt":444},1778701108877,1778696691708,{"_creationTime":447,"_id":448,"community":449,"display":450,"identity":453,"providers":454,"relations":461,"tags":462,"workflow":463},1778693539593.1858,"k171p5pgbfbm5g4k5sa3y4cj9s86m6hk",{"reviewCount":8},{"description":335,"installMethods":451,"name":452,"sourceUrl":14},{"claudeCode":12},"TradeMemory Protocol",{"basePath":334,"githubOwner":248,"githubRepo":249,"locale":18,"slug":336,"type":250},{"evaluate":455,"extract":460},{"promptVersionExtension":207,"promptVersionScoring":208,"score":408,"tags":456,"targetMarket":221,"tier":222},[214,218,216,457,458,459],"audit","compliance","llm",{"commitSha":256,"license":242},{"parentExtensionId":412,"repoId":258},[218,457,458,459,216,214],{"evaluatedAt":464,"extractAt":395,"updatedAt":465},1778693678813,1778693832942,{"_creationTime":467,"_id":468,"community":469,"display":470,"identity":473,"providers":474,"relations":480,"tags":481,"workflow":482},1778693539593.186,"k17bgwvhb6h29py715de1cm9xd86msq6",{"reviewCount":8},{"description":343,"installMethods":471,"name":472,"sourceUrl":14},{"claudeCode":12},"Risk Management",{"basePath":342,"githubOwner":248,"githubRepo":249,"locale":18,"slug":344,"type":250},{"evaluate":475,"extract":479},{"promptVersionExtension":207,"promptVersionScoring":208,"score":408,"tags":476,"targetMarket":221,"tier":222},[214,344,477,478,215],"ai-agent","behavioral-analysis",{"commitSha":256,"license":242},{"parentExtensionId":412,"repoId":258},[477,478,215,344,214],{"evaluatedAt":483,"extractAt":395,"updatedAt":484},1778693700524,1778693833120,{"_creationTime":486,"_id":487,"community":488,"display":489,"identity":495,"providers":499,"relations":508,"tags":510,"workflow":511},1778688112811.7527,"k17enr6rktmxh0enswrmze6et186mq12",{"reviewCount":8},{"description":490,"installMethods":491,"name":493,"sourceUrl":494},"Model best-case, worst-case, and likely revenue scenarios with sensitivity analysis for strategic planning. Use when: building financial forecasts; presenting board scenarios; planning headcount around revenue uncertainty; modeling pricing changes impact; preparing investor updates with upside/downside ranges",{"claudeCode":492},"guia-matthieu/clawfu-skills","forecast-scenarios","https://github.com/guia-matthieu/clawfu-skills",{"basePath":496,"githubOwner":497,"githubRepo":498,"locale":18,"slug":493,"type":250},"skills/revops/forecast-scenarios","guia-matthieu","clawfu-skills",{"evaluate":500,"extract":507},{"promptVersionExtension":207,"promptVersionScoring":208,"score":408,"tags":501,"targetMarket":221,"tier":222},[215,502,503,504,217,505,506],"forecasting","revenue","planning","sensitivity-analysis","mckinsey",{"commitSha":256},{"repoId":509},"kd72qvzyvm658ya7pbyh5ey47h86md53",[215,502,506,504,503,505,217],{"evaluatedAt":512,"extractAt":513,"updatedAt":512},1778690475880,1778688112811,{"_creationTime":515,"_id":516,"community":517,"display":518,"identity":522,"providers":524,"relations":531,"tags":532,"workflow":533},1778696691708.2983,"k17c6tkghtgnr7jnsh6gf5mf9h86nk00",{"reviewCount":8},{"description":519,"installMethods":520,"name":521,"sourceUrl":426},"Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.",{"claudeCode":424},"agentdb-memory-patterns",{"basePath":523,"githubOwner":429,"githubRepo":430,"locale":18,"slug":521,"type":250},".claude/skills/agentdb-memory-patterns",{"evaluate":525,"extract":530},{"promptVersionExtension":207,"promptVersionScoring":208,"score":211,"tags":526,"targetMarket":221,"tier":222},[218,527,216,528,436,529],"agent","database","nodejs",{"commitSha":256},{"repoId":441},[527,218,528,216,529,436],{"evaluatedAt":534,"extractAt":445,"updatedAt":534},1778698807267]