[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-Whatsonyourmind-oraclaw-pathfind-en":3,"guides-for-Whatsonyourmind-oraclaw-pathfind":434,"similar-k173hqx1847hrmdc2dhpm1f6n586md97-en":435},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":251,"isFallback":246,"parentExtension":257,"providers":258,"relations":263,"repo":265,"tags":430,"workflow":431},1778698837670.8005,"k173hqx1847hrmdc2dhpm1f6n586md97",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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.",{"claudeCode":12},"Whatsonyourmind/oraclaw","OraClaw Pathfind","https://github.com/Whatsonyourmind/oraclaw",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":232,"workflow":249},1778699008622.949,"kn7dthhcbxpam70fzxveb4vky586nsbj","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"practices":204,"prerequisites":208,"promptVersionExtension":211,"promptVersionScoring":212,"purpose":213,"rationale":214,"score":215,"summary":216,"tags":217,"targetMarket":225,"tier":226,"useCases":227},[21,26,29,32,36,39,43,47,50,53,57,61,65,69,72,75,78,81,84,87,91,95,99,103,107,110,113,116,120,123,126,129,132,135,138,142,146,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly states the problem of finding optimal paths through workflows and dependencies for AI agents, specifically mentioning task sequencing and A* pathfinding.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers deterministic optimization and algorithms for AI agents, providing mathematically correct answers that LLMs cannot, which is a significant value beyond simple prompting.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides a well-defined tool for pathfinding and task sequencing, covering the core lifecycle of planning and analysis. The documentation also includes pricing and usage examples, indicating readiness for real workflows.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses solely on pathfinding and task sequencing using A* and related algorithms, fitting within a single, coherent domain.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately and concisely reflects the skill's capabilities as described in the SKILL.md, covering A* pathfinding, task sequencing, and cost/time/risk breakdown.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes a single, well-scoped tool `plan_pathfind` with a clear, structured input schema for defining nodes and edges.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The SKILL.md clearly documents the input schema for `plan_pathfind`, including the structure of nodes and edges, and explains the available heuristics and rules.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","The single exposed tool `plan_pathfind` is descriptive and clearly indicates its function.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The `plan_pathfind` tool accepts a structured JSON input that precisely defines the graph and parameters, and its output is documented as the optimal path and cost breakdown.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The project explicitly states MIT license and provides a LICENSE file.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The last commit was on May 2, 2026, which is within the last 3 months.",{"category":58,"check":62,"severity":63,"summary":64},"Dependency Management","not_applicable","The skill itself does not appear to directly manage third-party dependencies in a way that requires updates or merging beyond its own core logic.",{"category":66,"check":67,"severity":24,"summary":68},"Security","Secret Management","The skill requires an `ORACLAW_API_KEY` but it's handled via environment variables, and there's no indication of secrets being echoed to stdout/stderr.",{"category":66,"check":70,"severity":24,"summary":71},"Injection","The skill operates on structured graph data and algorithms; there's no indication of loading or executing untrusted external code or data.",{"category":66,"check":73,"severity":24,"summary":74},"Transitive Supply-Chain Grenades","The skill relies on its own bundled logic and algorithms, not fetching remote code or data at runtime.",{"category":66,"check":76,"severity":24,"summary":77},"Sandbox Isolation","The skill's operation is confined to algorithmic computation and does not involve file system modifications outside its defined scope.",{"category":66,"check":79,"severity":24,"summary":80},"Sandbox escape primitives","No evidence of detached processes, retry loops on denied calls, or other sandbox escape primitives was found in the provided code and documentation.",{"category":66,"check":82,"severity":24,"summary":83},"Data Exfiltration","The skill processes graph data for pathfinding and does not involve submitting confidential data to third parties.",{"category":66,"check":85,"severity":24,"summary":86},"Hidden Text Tricks","The bundled content and documentation appear free of hidden-steering tricks, control characters, or obfuscated text.",{"category":88,"check":89,"severity":24,"summary":90},"Hooks","Opaque code execution","The skill's logic appears to be in plain, readable source code, with no evidence of obfuscation, base64 payloads, or runtime fetched scripts.",{"category":92,"check":93,"severity":24,"summary":94},"Portability","Structural Assumption","The skill operates on data structures provided as input and does not make assumptions about the user's project file layout.",{"category":96,"check":97,"severity":24,"summary":98},"Trust","Issues Attention","There are 0 open issues and 44 closed issues in the last 90 days, indicating active maintenance and responsiveness.",{"category":100,"check":101,"severity":24,"summary":102},"Versioning","Release Management","The SKILL.md frontmatter declares a `version: 1.0.0`, which is a meaningful semantic version.",{"category":104,"check":105,"severity":24,"summary":106},"Code Execution","Validation","The tool's input schema, as described in SKILL.md, implies validation of the graph structure (nodes, edges, costs) before processing.",{"category":66,"check":108,"severity":63,"summary":109},"Unguarded Destructive Operations","The skill is purely analytical and performs no destructive operations.",{"category":104,"check":111,"severity":24,"summary":112},"Error Handling","The SKILL.md outlines behavior for non-existent paths (empty path with Infinity cost), implying error handling for invalid graph states.",{"category":104,"check":114,"severity":63,"summary":115},"Logging","The skill is analytical and does not perform destructive actions or outbound calls that would require audit logging.",{"category":117,"check":118,"severity":63,"summary":119},"Compliance","GDPR","The skill processes graph data and has no interaction with personal data.",{"category":117,"check":121,"severity":24,"summary":122},"Target market","The skill's functionality is algorithm-based and has no regional or jurisdictional limitations, making it globally applicable.",{"category":92,"check":124,"severity":24,"summary":125},"Runtime stability","The skill operates on abstract data structures and algorithms, making it portable across different runtime environments without OS or shell-specific assumptions.",{"category":44,"check":127,"severity":24,"summary":128},"README","The README provides a comprehensive overview of OraClaw's purpose, tools, and market position, complementing the SKILL.md.",{"category":33,"check":130,"severity":63,"summary":131},"Tool surface size","This extension exposes only a single tool, `plan_pathfind`.",{"category":40,"check":133,"severity":63,"summary":134},"Overlapping near-synonym tools","With only one tool exposed, there are no overlapping near-synonym tools.",{"category":44,"check":136,"severity":24,"summary":137},"Phantom features","All advertised features, such as A* pathfinding and K-shortest paths, are implemented and described in the SKILL.md.",{"category":139,"check":140,"severity":24,"summary":141},"Install","Installation instruction","The README provides clear installation instructions for the MCP server and includes copy-pasteable JSON configuration and example agent prompts.",{"category":143,"check":144,"severity":24,"summary":145},"Errors","Actionable error messages","The SKILL.md specifies that if no path exists, an empty path with Infinity cost is returned, indicating defined behavior for a failure mode.",{"category":147,"check":148,"severity":63,"summary":149},"Execution","Pinned dependencies","The skill itself does not appear to have bundled scripts with direct dependencies that would require pinning.",{"category":33,"check":151,"severity":63,"summary":152},"Dry-run preview","The skill is analytical and does not perform state-changing operations, thus a dry-run preview is not applicable.",{"category":154,"check":155,"severity":63,"summary":156},"Protocol","Idempotent retry & timeouts","The skill's operation is a stateless computation based on provided graph data, making retries and timeouts not directly applicable to its core logic.",{"category":117,"check":158,"severity":63,"summary":159},"Telemetry opt-in","There is no indication that this skill emits telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md clearly defines the purpose as A* pathfinding and task sequencing for AI agents, operating on task dependency graphs, and specifies when to use it.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and self-contained, summarizing the core capability of A* pathfinding and task sequencing.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is concise, detailing the tool, heuristics, rules, and pricing without unnecessary verbosity.",{"category":170,"check":171,"severity":63,"summary":172},"Context","Progressive Disclosure","The skill is straightforward and does not involve lengthy procedures or bulk third-party material that would necessitate progressive disclosure.",{"category":170,"check":174,"severity":63,"summary":175},"Forked exploration","This skill is a direct computation and does not involve deep exploration or code review, so `context: fork` is not applicable.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides a clear, copy-pasteable JSON example of the input structure for the `plan_pathfind` tool.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md documents edge cases such as non-existent paths returning Infinity cost and specifies rules for edge costs.",{"category":104,"check":183,"severity":63,"summary":184},"Tool Fallback","The skill does not depend on external MCP servers or tools; it is self-contained.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The documentation specifies behavior for an invalid graph state (no path found), implying a controlled halt with a report.",{"category":92,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and does not rely on or implicitly couple with other skills.",1778699008503,"This skill provides A* pathfinding and task sequencing capabilities for AI agents, enabling optimal route finding through workflows, dependencies, and decision trees. It supports K-shortest paths via Yen's algorithm and offers cost, time, and risk breakdowns.",[195,196,197,198,199],"A* pathfinding for optimal routes","K-shortest paths via Yen's algorithm","Cost, time, and risk breakdown","Configurable heuristics (cost, time, risk, weighted, zero)","Task sequencing and workflow navigation",[201,202,203],"LLM-based reasoning or hallucination","Real-time execution of tasks","Complex simulation beyond path analysis",[205,206,207],"Algorithm selection","Optimization","Task planning",[209,210],"ORACLAW_API_KEY environment variable (for premium features/higher rate limits)","Access to the OraClaw API or MCP Server","3.0.0","4.4.0","To equip AI agents with deterministic mathematical tools for finding optimal paths and sequencing tasks, overcoming LLM limitations in algorithmic reasoning.","The skill is exceptionally well-documented and robust, with no significant findings across any category. The only minor points not applicable are due to the self-contained, analytical nature of the tool.",99,"A robust and well-documented skill for optimal pathfinding and task sequencing using A* algorithms.",[218,219,220,221,222,223,224],"pathfinding","routing","workflow","dependencies","critical-path","task-sequencing","astar","global","verified",[228,229,230,231],"Finding the fastest/cheapest/safest path through a task dependency graph","Sequencing tasks optimally considering time, cost, and risk","Navigating complex workflows with multiple routes to completion","Planning project execution order with dependency constraints",{"codeQuality":233,"collectedAt":235,"documentation":236,"maintenance":239,"security":245,"testCoverage":248},{"hasLockfile":234},true,1778698992989,{"descriptionLength":237,"readmeSize":238},188,9472,{"closedIssues90d":240,"forks":241,"hasChangelog":234,"manifestVersion":242,"openIssues90d":8,"pushedAt":243,"stars":244},44,2,"1.0.0",1777714123000,8,{"hasNpmPackage":246,"license":247,"smitheryVerified":246},false,"MIT",{"hasCi":234,"hasTests":234},{"updatedAt":250},1778699008623,{"basePath":252,"githubOwner":253,"githubRepo":254,"locale":18,"slug":255,"type":256},"mission-control/packages/clawhub-skills/oraclaw-pathfind","Whatsonyourmind","oraclaw","oraclaw-pathfind","skill",null,{"evaluate":259,"extract":261},{"promptVersionExtension":211,"promptVersionScoring":212,"score":215,"tags":260,"targetMarket":225,"tier":226},[218,219,220,221,222,223,224],{"commitSha":262,"license":247},"HEAD",{"repoId":264},"kd76fmxm1ng903s4fmj0p7hxxs86ndkg",{"_creationTime":266,"_id":264,"identity":267,"providers":268,"workflow":426},1778698831609.0093,{"githubOwner":253,"githubRepo":254,"sourceUrl":14},{"classify":269,"discover":400,"github":403},{"commitSha":262,"extensions":270},[271,282,290,298,306,314,322,330,338,346,354,359,367,375,383],{"basePath":272,"description":273,"displayName":274,"installMethods":275,"rationale":276,"selectedPaths":277,"source":281,"sourceLanguage":18,"type":256},"mission-control/packages/clawhub-skills/oraclaw-anomaly","Anomaly detection for AI agents. 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",[278],{"path":279,"priority":280},"SKILL.md","mandatory","rule",{"basePath":283,"description":284,"displayName":285,"installMethods":286,"rationale":287,"selectedPaths":288,"source":281,"sourceLanguage":18,"type":256},"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",[289],{"path":279,"priority":280},{"basePath":291,"description":292,"displayName":293,"installMethods":294,"rationale":295,"selectedPaths":296,"source":281,"sourceLanguage":18,"type":256},"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",[297],{"path":279,"priority":280},{"basePath":299,"description":300,"displayName":301,"installMethods":302,"rationale":303,"selectedPaths":304,"source":281,"sourceLanguage":18,"type":256},"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",[305],{"path":279,"priority":280},{"basePath":307,"description":308,"displayName":309,"installMethods":310,"rationale":311,"selectedPaths":312,"source":281,"sourceLanguage":18,"type":256},"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",[313],{"path":279,"priority":280},{"basePath":315,"description":316,"displayName":317,"installMethods":318,"rationale":319,"selectedPaths":320,"source":281,"sourceLanguage":18,"type":256},"mission-control/packages/clawhub-skills/oraclaw-decide","Decision intelligence for AI agents. Analyze options, map decision dependencies with PageRank, detect when information sources conflict, and find the choices that matter most.","oraclaw-decide",{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-decide/SKILL.md",[321],{"path":279,"priority":280},{"basePath":323,"description":324,"displayName":325,"installMethods":326,"rationale":327,"selectedPaths":328,"source":281,"sourceLanguage":18,"type":256},"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",[329],{"path":279,"priority":280},{"basePath":331,"description":332,"displayName":333,"installMethods":334,"rationale":335,"selectedPaths":336,"source":281,"sourceLanguage":18,"type":256},"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",[337],{"path":279,"priority":280},{"basePath":339,"description":340,"displayName":341,"installMethods":342,"rationale":343,"selectedPaths":344,"source":281,"sourceLanguage":18,"type":256},"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",[345],{"path":279,"priority":280},{"basePath":347,"description":348,"displayName":349,"installMethods":350,"rationale":351,"selectedPaths":352,"source":281,"sourceLanguage":18,"type":256},"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",[353],{"path":279,"priority":280},{"basePath":252,"description":10,"displayName":255,"installMethods":355,"rationale":356,"selectedPaths":357,"source":281,"sourceLanguage":18,"type":256},{"claudeCode":12},"SKILL.md frontmatter at mission-control/packages/clawhub-skills/oraclaw-pathfind/SKILL.md",[358],{"path":279,"priority":280},{"basePath":360,"description":361,"displayName":362,"installMethods":363,"rationale":364,"selectedPaths":365,"source":281,"sourceLanguage":18,"type":256},"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",[366],{"path":279,"priority":280},{"basePath":368,"description":369,"displayName":370,"installMethods":371,"rationale":372,"selectedPaths":373,"source":281,"sourceLanguage":18,"type":256},"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",[374],{"path":279,"priority":280},{"basePath":376,"description":377,"displayName":378,"installMethods":379,"rationale":380,"selectedPaths":381,"source":281,"sourceLanguage":18,"type":256},"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",[382],{"path":279,"priority":280},{"basePath":384,"description":385,"displayName":386,"installMethods":387,"license":247,"rationale":388,"selectedPaths":389,"source":281,"sourceLanguage":18,"type":399},"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":386},"server.json with namespace/server name at mission-control/packages/mcp-server/server.json",[390,392,394,396],{"path":391,"priority":280},"server.json",{"path":393,"priority":280},"package.json",{"path":395,"priority":280},"README.md",{"path":397,"priority":398},"src/index.ts","low","mcp",{"sources":401},[402],"manual",{"closedIssues90d":240,"description":404,"forks":241,"homepage":405,"license":247,"openIssues90d":8,"pushedAt":243,"readmeSize":238,"stars":244,"topics":406},"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. Field-validated in 12+ OSS projects.","https://web-olive-one-89.vercel.app",[407,408,409,410,411,412,413,399,414,415,416,417,418,419,420,421,422,423,424,425],"ai-agents","algorithms","api","bandits","decision-intelligence","fastify","machine-learning","optimization","typescript","agent-tools","anthropic","claude-mcp","contextual-bandit","deterministic-tools","linear-programming","llm-tools","model-context-protocol","monte-carlo","pagerank",{"classifiedAt":427,"discoverAt":428,"extractAt":429,"githubAt":429,"updatedAt":427},1778698837409,1778698831609,1778698835357,[224,222,221,218,219,223,220],{"evaluatedAt":250,"extractAt":432,"updatedAt":433},1778698837670,1778699188404,[],[436,464,493,523,553,579],{"_creationTime":437,"_id":438,"community":439,"display":440,"identity":446,"providers":450,"relations":458,"tags":460,"workflow":461},1778697652123.886,"k174rav3ndhd0xydpyp2k4nn8586nbvw",{"reviewCount":8},{"description":441,"installMethods":442,"name":444,"sourceUrl":445},"Route plain-language requests for Pi, Claude Code, Cursor, Copilot, OpenClaw ACP, OpenCode, Gemini CLI, Qwen, Kiro, Kimi, iFlow, Factory Droid, Kilocode, or explicit ACP harness work into either OpenClaw ACP runtime sessions or direct acpx-driven sessions (\"telephone game\" flow). For coding-agent thread requests, read this skill first, then use only `sessions_spawn` for thread creation. Codex chat binding defaults to the native Codex app-server plugin unless ACP is explicit or background spawn needs ACP.",{"claudeCode":443},"steipete/clawdis","acp-router","https://github.com/steipete/clawdis",{"basePath":447,"githubOwner":448,"githubRepo":449,"locale":18,"slug":444,"type":256},"extensions/acpx/skills/acp-router","steipete","clawdis",{"evaluate":451,"extract":457},{"promptVersionExtension":211,"promptVersionScoring":212,"score":452,"tags":453,"targetMarket":225,"tier":226},100,[219,454,455,220,456],"acp","coding-assistants","automation",{"commitSha":262},{"repoId":459},"kd738npxg9yh3xf3vddzy9fyfh86nhng",[454,456,455,219,220],{"evaluatedAt":462,"extractAt":463,"updatedAt":462},1778698053003,1778697652123,{"_creationTime":465,"_id":466,"community":467,"display":468,"identity":474,"providers":478,"relations":486,"tags":489,"workflow":490},1778696691708.3306,"k172evhhmbzzyp7g0t2caf4hfh86nsp9",{"reviewCount":8},{"description":469,"installMethods":470,"name":472,"sourceUrl":473},"First-run setup for ruvector@0.2.25 — installs ONNX/Brain/SONA add-ons, registers the MCP server, and verifies the install via `doctor`",{"claudeCode":471},"ruvnet/ruflo","vector-setup","https://github.com/ruvnet/ruflo",{"basePath":475,"githubOwner":476,"githubRepo":477,"locale":18,"slug":472,"type":256},"plugins/ruflo-ruvector/skills/vector-setup","ruvnet","ruflo",{"evaluate":479,"extract":485},{"promptVersionExtension":211,"promptVersionScoring":212,"score":452,"tags":480,"targetMarket":225,"tier":226},[481,482,483,484,221],"setup","installation","ruvector","npm",{"commitSha":262},{"parentExtensionId":487,"repoId":488},"k17710fw96s8hs1y3j2cye3aa586n523","kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[221,482,484,483,481],{"evaluatedAt":491,"extractAt":492,"updatedAt":491},1778701365160,1778696691708,{"_creationTime":494,"_id":495,"community":496,"display":497,"identity":503,"providers":507,"relations":517,"tags":519,"workflow":520},1778696052276.0203,"k17bgxxgryq8edg32egypsvqtn86m1h7",{"reviewCount":8},{"description":498,"installMethods":499,"name":501,"sourceUrl":502},"Detect and untangle circular dependencies. Runs madge/skott (TS), pycycle (Py), or compiler-only checks (Go/Rust). Auto-fixes leaf-extractable cycles; reports core cycles for human review. Use when the user asks to find circular imports, fix dependency cycles, or untangle module graph. Example queries — \"find circular imports\", \"fix dependency cycles\", \"untangle our module graph\", \"why is madge complaining\".",{"claudeCode":500},"raintree-technology/claude-starter","cleanup-cycles","https://github.com/raintree-technology/claude-starter",{"basePath":504,"githubOwner":505,"githubRepo":506,"locale":18,"slug":501,"type":256},"templates/.claude/skills/cleanup-cycles","raintree-technology","claude-starter",{"evaluate":508,"extract":516},{"promptVersionExtension":211,"promptVersionScoring":212,"score":452,"tags":509,"targetMarket":225,"tier":226},[510,221,511,512,415,513,514,515],"code-quality","javascript","python","go","rust","refactoring",{"commitSha":262},{"repoId":518},"kd78ywakatnz4sjfx781sy14vh86mtty",[510,221,513,511,512,515,514,415],{"evaluatedAt":521,"extractAt":522,"updatedAt":521},1778696977114,1778696052276,{"_creationTime":524,"_id":525,"community":526,"display":527,"identity":533,"providers":537,"relations":546,"tags":549,"workflow":550},1778695548458.3328,"k17cyw0d6mk1vdgew2xmncx1f186npdm",{"reviewCount":8},{"description":528,"installMethods":529,"name":531,"sourceUrl":532},"Audit project dependencies for version staleness, security vulnerabilities, and compatibility issues. Covers lock file analysis, upgrade path planning, and breaking change assessment. Use before a release to ensure dependencies are current and secure, during periodic maintenance reviews, after receiving a security advisory, when upgrading to a new language version, before submitting to CRAN or npm, or when inheriting a project to assess its dependency health.\n",{"claudeCode":530},"pjt222/agent-almanac","audit-dependency-versions","https://github.com/pjt222/agent-almanac",{"basePath":534,"githubOwner":535,"githubRepo":536,"locale":18,"slug":531,"type":256},"skills/audit-dependency-versions","pjt222","agent-almanac",{"evaluate":538,"extract":545},{"promptVersionExtension":211,"promptVersionScoring":212,"score":452,"tags":539,"targetMarket":225,"tier":226},[221,540,541,542,543,544],"auditing","security","upgrades","versioning","maintenance",{"commitSha":262},{"parentExtensionId":547,"repoId":548},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[540,221,544,541,542,543],{"evaluatedAt":551,"extractAt":552,"updatedAt":551},1778696062378,1778695548458,{"_creationTime":554,"_id":555,"community":556,"display":557,"identity":562,"providers":566,"relations":572,"tags":575,"workflow":576},1778699234184.6174,"k174zww66m804nhr89ttra7r6d86nwyg",{"reviewCount":8},{"description":558,"installMethods":559,"name":481,"sourceUrl":561},"Use first for install/update routing — sends setup, doctor, or MCP requests to the correct OMC setup flow",{"claudeCode":560},"Yeachan-Heo/oh-my-claudecode","https://github.com/Yeachan-Heo/oh-my-claudecode",{"basePath":563,"githubOwner":564,"githubRepo":565,"locale":18,"slug":481,"type":256},"skills/setup","Yeachan-Heo","oh-my-claudecode",{"evaluate":567,"extract":571},{"promptVersionExtension":211,"promptVersionScoring":212,"score":452,"tags":568,"targetMarket":225,"tier":226},[481,219,569,570,399],"configuration","cli",{"commitSha":262},{"parentExtensionId":573,"repoId":574},"k17brg5egdw1jbncj1j4wfv3fh86n639","kd74zv63fryf9prygtq7gf4es986n22y",[570,569,399,219,481],{"evaluatedAt":577,"extractAt":578,"updatedAt":577},1778699724286,1778699234184,{"_creationTime":580,"_id":581,"community":582,"display":583,"identity":589,"providers":593,"relations":601,"tags":604,"workflow":605},1778675056600.2566,"k1749wefszncghc6rgh3g0cdks86mem5",{"reviewCount":8},{"description":584,"installMethods":585,"name":587,"sourceUrl":588},"Deprecated redirect skill that routes legacy 'content creator' requests to the correct specialist. Use when a user invokes 'content creator', asks to write a blog post, article, guide, or brand voice analysis (routes to content-production), or asks to plan content, build a topic cluster, or create a content calendar (routes to content-strategy). Does not handle requests directly — identifies user intent and redirects to content-production for writing/SEO/brand-voice tasks or content-strategy for planning tasks.",{"claudeCode":586},"alirezarezvani/claude-skills","content-creator","https://github.com/alirezarezvani/claude-skills",{"basePath":590,"githubOwner":591,"githubRepo":592,"locale":18,"slug":587,"type":256},"marketing-skill/skills/content-creator","alirezarezvani","claude-skills",{"evaluate":594,"extract":600},{"promptVersionExtension":211,"promptVersionScoring":212,"score":452,"tags":595,"targetMarket":225,"tier":226},[596,597,598,599,219],"marketing","content","redirect","deprecation",{"commitSha":262},{"parentExtensionId":602,"repoId":603},"k170sws65f0ebecn36z3q8c2z186m477","kd7ff9s1w43mfyy1n7hf87816186m6px",[597,599,596,598,219],{"evaluatedAt":606,"extractAt":607,"updatedAt":606},1778684296105,1778675056600]