[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-decide-en":3,"guides-for-Whatsonyourmind-oraclaw-decide":426,"similar-k17fe7ybjme5s1n10mmg3emmns86nr26-en":427},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":245,"isFallback":240,"parentExtension":250,"providers":251,"relations":256,"repo":258,"tags":422,"workflow":423},1778698837670.7993,"k17fe7ybjme5s1n10mmg3emmns86nr26",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.",{"claudeCode":12},"Whatsonyourmind/oraclaw","oraclaw-decide","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":226,"workflow":243},1778698934136.3132,"kn73pasfam1achagcpvcy2qf0d86ncg9","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":219,"tier":220,"useCases":221},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,106,109,113,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,188,192],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of decision intelligence for AI agents, including specific tasks like analyzing options, mapping dependencies, and detecting conflicts.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers deterministic optimization, simulation, and analysis tools (bandits, PageRank, convergence scoring) that go beyond standard LLM capabilities for mathematical tasks.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides a comprehensive set of tools for decision analysis, with clear documentation and examples, indicating readiness for real-world workflows.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on decision intelligence and related analytical tools, maintaining a coherent domain without unrelated capabilities.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities for decision intelligence, analysis, and finding impactful choices.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","Tools are narrowly scoped verb-noun specialists (e.g., optimize_bandit, analyze_decision_graph) rather than generalist execution commands.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md provides clear documentation for tools, including parameters for graph analysis and prerequisites like ORACLAW_API_KEY.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names like `optimize_bandit` and `analyze_decision_graph` are descriptive and adhere to the verb-noun convention.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Input schemas for tools like `analyze_decision_graph` are well-defined and request only necessary data, with structured outputs.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is distributed under the MIT license, clearly indicated in the README and LICENSE file.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The latest commit was on May 2, 2026, which is within the last 90 days, indicating active maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project uses npm and lists its SDK packages, implying standard dependency management practices, and has a lockfile.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The skill requires an ORACLAW_API_KEY, documented as an environment variable, and there is no indication of secrets being echoed.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The extension appears to handle data as input for algorithms, with no indication of executing untrusted code or instructions from external data.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","All code and dependencies appear to be committed to the repository, with no runtime fetching of external code or instructions.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The tools operate on data inputs and return structured outputs, with no indication of modifying files outside the intended scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","There are no indications of detached processes or retry loops around denied tool calls in the provided source.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The extension processes analytical data and does not submit confidential user data to third parties. Outbound calls are to documented services like Render.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content and descriptions are free of hidden steering tricks, control characters, or unusual Unicode.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The code appears to be plain JavaScript/TypeScript with no obfuscation, base64 payloads, or runtime script fetching.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill defines its own tools and relies on standard environment variables, not specific project directory structures.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","With 0 issues opened and 44 closed in the last 90 days, the maintainers have a high closure rate and actively manage issues.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","A meaningful `version: 1.0.0` is declared in the SKILL.md frontmatter, and a CHANGELOG.md is present.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The tools expect structured input (JSON) and return structured output, implying internal validation and sanitization.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The skill is analytical and read-only, performing no destructive operations.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","The tools are designed to return structured JSON, implying robust error handling with clear codes and messages.",{"category":110,"check":114,"severity":115,"summary":116},"Logging","not_applicable","The skill operates as an API and MCP server, with logging handled by the calling agent or server infrastructure, not within the skill's bundle itself.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The skill performs mathematical analysis on provided data and does not inherently operate on or submit personal data.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The extension uses standard algorithms and data formats, with no regional restrictions detected; targetMarket is global.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The skill relies on standard web APIs and environment variables, making it portable across different runtime environments.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README is comprehensive, detailing the project's purpose, implementations, and how to get started.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The skill exposes 3 core tools in SKILL.md and a catalog of 17 MCP tools, which is a reasonable number.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The exposed tools cover distinct functionalities (optimize_bandit, analyze_decision_graph, score_convergence) without significant overlap.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features, including the MCP tools and API access, have corresponding implementations and documentation.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","Clear instructions are provided for setting up the MCP server, REST API, and npm SDK, including example invocations.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The tools return structured JSON, implying that errors will be categorized with codes and potentially hints for remediation.",{"category":103,"check":148,"severity":24,"summary":149},"Pinned dependencies","The project has a lockfile (`hasLockfile: true`) and lists specific SDK packages, indicating pinned dependencies.",{"category":33,"check":151,"severity":115,"summary":152},"Dry-run preview","The skill is analytical and does not perform state-changing operations or outbound data sends that would require a dry-run mode.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The tools are designed for stateless API calls, implying they are idempotent and should have timeouts implemented at the API level.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The extension does not appear to emit telemetry; if it did, the opt-in nature is implied by the lack of any mention of opt-out telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md clearly defines the purpose as decision intelligence for AI agents, listing specific use cases and tools.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and self-contained, providing a clear summary of the core capability and triggering phrases.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is reasonably concise, deferring bulk material to the README and other linked resources.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The SKILL.md outlines procedures and links to external resources like the README for more detailed information, rather than embedding all content.",{"category":170,"check":174,"severity":115,"summary":175},"Forked exploration","This skill is not designed for deep exploration or code review that would require forked context; 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Z-score, IQR, and streaming detection. Find outliers in data instantly. Sub-millisecond response. Works on single values or full datasets.","oraclaw-anomaly",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-anomaly/SKILL.md",[271],{"path":272,"priority":273},"SKILL.md","mandatory","rule",{"basePath":276,"description":277,"displayName":278,"installMethods":279,"rationale":280,"selectedPaths":281,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-bandit","A/B testing and feature optimization for AI agents. Pick the best option automatically using Multi-Armed Bandits and Contextual Bandits (LinUCB). No data warehouse needed — works from request","oraclaw-bandit",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-bandit/SKILL.md",[282],{"path":272,"priority":273},{"basePath":284,"description":285,"displayName":286,"installMethods":287,"rationale":288,"selectedPaths":289,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-bayesian","Bayesian inference engine for AI agents. Update beliefs with new evidence. Prior + evidence = posterior. Multi-factor prediction with calibration tracking.","oraclaw-bayesian",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-bayesian/SKILL.md",[290],{"path":272,"priority":273},{"basePath":292,"description":293,"displayName":294,"installMethods":295,"rationale":296,"selectedPaths":297,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-calibrate","Prediction quality scoring for AI agents. Brier score, log score, and multi-source convergence analysis. Know if your forecasts are accurate and if your data sources agree.","oraclaw-calibrate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-calibrate/SKILL.md",[298],{"path":272,"priority":273},{"basePath":300,"description":301,"displayName":302,"installMethods":303,"rationale":304,"selectedPaths":305,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-cmaes","CMA-ES continuous optimization for AI agents. State-of-the-art derivative-free optimizer. 10-100x more sample-efficient than genetic algorithms on continuous problems. Hyperparameter tuning, portfolio optimization, parameter calibration.","oraclaw-cmaes",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-cmaes/SKILL.md",[306],{"path":272,"priority":273},{"basePath":246,"description":10,"displayName":13,"installMethods":308,"rationale":309,"selectedPaths":310,"source":274,"sourceLanguage":18,"type":249},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-decide/SKILL.md",[311],{"path":272,"priority":273},{"basePath":313,"description":314,"displayName":315,"installMethods":316,"rationale":317,"selectedPaths":318,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-ensemble","Multi-model consensus for AI agents. Combine predictions from multiple LLMs, models, or sources into a mathematically optimal consensus. Auto-weights by historical accuracy.","oraclaw-ensemble",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-ensemble/SKILL.md",[319],{"path":272,"priority":273},{"basePath":321,"description":322,"displayName":323,"installMethods":324,"rationale":325,"selectedPaths":326,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-evolve","Genetic Algorithm optimizer for AI agents. Multi-objective Pareto optimization for portfolio weights, pricing, hyperparameters, marketing mix — any problem with multiple competing goals. Handles nonlinear search spaces that LP solvers cannot.","oraclaw-evolve",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-evolve/SKILL.md",[327],{"path":272,"priority":273},{"basePath":329,"description":330,"displayName":331,"installMethods":332,"rationale":333,"selectedPaths":334,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-forecast","Time series forecasting for AI agents. ARIMA and Holt-Winters predictions with confidence intervals. Predict revenue, traffic, prices, or any sequential data. Sub-5ms inference.","oraclaw-forecast",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-forecast/SKILL.md",[335],{"path":272,"priority":273},{"basePath":337,"description":338,"displayName":339,"installMethods":340,"rationale":341,"selectedPaths":342,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-graph","Network intelligence for AI agents. PageRank, community detection (Louvain), critical path, and bottleneck analysis for any graph of connected things.","oraclaw-graph",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-graph/SKILL.md",[343],{"path":272,"priority":273},{"basePath":345,"description":346,"displayName":347,"installMethods":348,"rationale":349,"selectedPaths":350,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-pathfind","A* pathfinding and task sequencing for AI agents. Find the optimal path through workflows, dependencies, and decision trees. K-shortest paths via Yen's algorithm. Cost/time/risk breakdown.","oraclaw-pathfind",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-pathfind/SKILL.md",[351],{"path":272,"priority":273},{"basePath":353,"description":354,"displayName":355,"installMethods":356,"rationale":357,"selectedPaths":358,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-risk","Risk assessment engine for AI agents. Value at Risk (VaR), CVaR, stress testing, and multi-factor risk scoring. Monte Carlo powered. Built for trading agents, lending agents, and portfolio managers.","oraclaw-risk",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-risk/SKILL.md",[359],{"path":272,"priority":273},{"basePath":361,"description":362,"displayName":363,"installMethods":364,"rationale":365,"selectedPaths":366,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-simulate","Monte Carlo simulation for AI agents. Run thousands of probabilistic scenarios to model risk, forecast revenue, estimate project timelines, and quantify uncertainty. Supports 6 distribution types.","oraclaw-simulate",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-simulate/SKILL.md",[367],{"path":272,"priority":273},{"basePath":369,"description":370,"displayName":371,"installMethods":372,"rationale":373,"selectedPaths":374,"source":274,"sourceLanguage":18,"type":249},"mission-control/packages/clawhub-skills/oraclaw-solver","Industrial-grade scheduling and resource optimization for AI agents. Solve task scheduling with energy matching, budget allocation, and any LP/MIP constraint problem in milliseconds.","oraclaw-solver",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-solver/SKILL.md",[375],{"path":272,"priority":273},{"basePath":377,"description":378,"displayName":379,"installMethods":380,"license":241,"rationale":381,"selectedPaths":382,"source":274,"sourceLanguage":18,"type":392},"mission-control/packages/mcp-server","OraClaw Decision Intelligence — 17 MCP tools for AI agents (6 premium API-key tools + 11 free). Full input/output schemas + MCP behavior annotations on every tool. Optimization (bandit/CMA-ES/genetic/LP-MIP), simulation (Monte Carlo/scenarios), prediction (ARIMA/Holt-Winters/Bayesian/ensemble), scoring (convergence/calibration), graph analytics, anomaly detection, pathfinding, scheduling.","@oraclaw/mcp-server",{"npm":379},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[383,385,387,389],{"path":384,"priority":273},"server.json",{"path":386,"priority":273},"package.json",{"path":388,"priority":273},"README.md",{"path":390,"priority":391},"src/index.ts","low","mcp",{"sources":394},[395],"manual",{"closedIssues90d":234,"description":397,"forks":235,"homepage":398,"license":241,"openIssues90d":8,"pushedAt":237,"readmeSize":232,"stars":238,"topics":399},"Deterministic decision-intelligence MCP server for AI agents — 17 tools, 21 algorithms (LinUCB, HiGHS LP/MIP, PageRank, Monte Carlo, CMA-ES, conformal). Sub-25ms. Zero LLM cost. AAA on Glama. 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