[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-cdeust-cortex-visualize-en":3,"guides-for-cdeust-cortex-visualize":777,"similar-k17dw7vyzvyez1x9f35d6trcd986makc-en":778},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":247,"isFallback":243,"parentExtension":252,"providers":307,"relations":311,"repo":312,"tags":775,"workflow":776},1778683562157.8784,"k17dw7vyzvyez1x9f35d6trcd986makc",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"Launch the interactive unified neural graph visualization. Use when the user says 'show visualization', 'show me the graph', 'visualize memories', 'show memory map', 'open neural graph', or when a visual overview of the memory system or cognitive profile would be helpful.",{"claudeCode":12},"cdeust/Cortex","cortex-visualize","https://github.com/cdeust/Cortex",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":227,"workflow":245},1778683915838.4697,"kn78bd9h19e0me3pjyw0qhpjy186m439","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":220,"tier":221,"useCases":222},[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 needing a visual overview of the memory system or cognitive profile and provides specific user intents for its use.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers a unique interactive neural graph visualization with features like domain separation, emotional tagging, and quality scoring, which goes beyond standard LLM capabilities.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The skill provides a complete lifecycle for visualization, including launching the interactive graph, programmatic data retrieval, and clear usage tips, making it ready for workflow integration.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The extension focuses solely on visualizing the neural graph and related data, with a clear scope around visualization and data retrieval.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's functionality and provides clear usage triggers and purpose.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","The skill exposes narrow, verb-noun specialized tools like `cortex:open_visualization` and `cortex:get_methodology_graph`.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","The skill's documentation clearly outlines the parameters for its tools, such as the optional 'domain' parameter, and provides usage examples.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","Tool names like `open_visualization` and `get_methodology_graph` are descriptive and follow kebab-case conventions.",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","The `open_visualization` tool takes a structured input with an optional domain parameter, and `get_methodology_graph` also takes a structured input, with documented outputs.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under the MIT license, which is a permissive open-source license, as indicated by the LICENSE file and README.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The repository shows recent commits, with the last commit dated 2026-05-13, indicating active maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The repository indicates the use of Python 3.10+ and includes a lockfile (`hasLockfile: true`), suggesting good dependency management practices.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The skill appears to run locally with no obvious use of secrets that would be exposed.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The skill's operations are confined to local execution and data visualization, with no indication of loading untrusted external data as instructions.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The skill operates locally and does not fetch external code or data at runtime, mitigating supply-chain risks.",{"category":65,"check":75,"severity":24,"summary":76},"Sandbox Isolation","The skill's operations are confined to local visualization and data retrieval, with no indication of modifying files outside its designated scope.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No detached process spawns or retry loops around denied tool calls are apparent in the skill's execution model.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The skill's function is purely local visualization and data retrieval, with no outbound calls that could exfiltrate data.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content appears free of hidden-steering tricks and uses clean printable ASCII and expected Unicode.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The skill's code is not obfuscated and appears to be plain, readable source.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The skill's local execution model avoids assumptions about user project structure outside its own operational domain.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","There are 0 open issues and 16 closed issues in the last 90 days, indicating active maintenance and responsiveness.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The project declares a meaningful semver version (3.15.0) in its README, indicating proper release management.",{"category":103,"check":104,"severity":24,"summary":105},"Execution","Validation","The skill's tools accept structured input, implying schema validation for parameters like 'domain'.",{"category":65,"check":107,"severity":24,"summary":108},"Unguarded Destructive Operations","The skill is read-only in nature, focused on visualization and data retrieval, and does not perform destructive operations.",{"category":110,"check":111,"severity":24,"summary":112},"Code Execution","Error Handling","The skill's operations are local and focused; while not explicitly detailed, the nature of the tool suggests standard error handling.",{"category":110,"check":114,"severity":115,"summary":116},"Logging","not_applicable","The skill is focused on visualization and data retrieval, not destructive actions or outbound calls, making structured logging less critical.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The skill operates locally and does not handle personal data, thus avoiding GDPR compliance concerns.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The skill operates locally and its functionality is not geographically restricted, making its target market global.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The skill is designed for local execution and does not appear to make assumptions about specific editors, shells, or operating systems beyond standard Python/PostgreSQL environments.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README file is comprehensive and clearly states the extension's purpose and capabilities.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The extension exposes two main tools (`open_visualization` and `get_methodology_graph`), which is within the ideal range.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The two exposed tools have distinct functionalities and do not appear to be near-synonyms.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features related to visualization and data retrieval are implemented and accessible via the tools.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","The README provides clear installation instructions using `claude plugin marketplace add` and `claude plugin install`, along with verification steps.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","While specific error messages are not detailed, the local execution and structured tools suggest that errors would be actionable.",{"category":103,"check":148,"severity":24,"summary":149},"Pinned dependencies","The project specifies Python 3.10+ and the presence of a lockfile indicates pinned dependencies.",{"category":33,"check":151,"severity":115,"summary":152},"Dry-run preview","The skill is primarily read-only for visualization and data retrieval, and does not perform state-changing operations or send data outward.",{"category":154,"check":155,"severity":24,"summary":156},"Protocol","Idempotent retry & timeouts","The skill's operations are local and do not involve remote calls or state-changing mutations, making timeouts and idempotency less critical.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The skill operates locally and there is no indication of telemetry being emitted, thus fulfilling the opt-in requirement by default.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The description clearly states the purpose of launching an interactive neural graph visualization and provides specific usage scenarios and non-goals.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and effectively summarizes the core capability and provides trigger phrases.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md body is well-structured and focuses on core functionality, deferring deeper material to linked files.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","The skill defers detailed information to linked documents within the repository, such as `docs/papers/science.md`, adhering to progressive disclosure.",{"category":170,"check":174,"severity":115,"summary":175},"Forked exploration","The skill's primary function is visualization and data retrieval, not deep exploration or multi-file inspection, so `context: fork` is not applicable.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides clear examples for launching the visualization and retrieving graph data, including code snippets and expected outcomes.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The skill's documentation mentions specific tips like auto-shutdown after idle and consistent bookmark URLs, implicitly handling some operational edge cases.",{"category":110,"check":183,"severity":115,"summary":184},"Tool Fallback","The skill does not rely on external MCP servers or tools that would require a fallback mechanism.",{"category":91,"check":186,"severity":24,"summary":187},"Stack assumptions","The documentation clearly states the Python 3.10+ requirement and the local execution environment (PostgreSQL + pgvector).",{"category":189,"check":190,"severity":24,"summary":191},"Safety","Halt on unexpected state","The skill's local and read-only nature minimizes the risk of unexpected pre-state issues, and its clear operational boundaries would likely lead to graceful failure if issues arise.",{"category":91,"check":193,"severity":24,"summary":194},"Cross-skill coupling","The skill operates independently and does not implicitly rely on other skills, with clear documentation for related projects.",1778683915707,"This skill launches an interactive, browser-based neural graph visualization of methodology profiles, memories, and knowledge graph connections. It supports programmatic data retrieval and offers features like 2D force-directed layout, domain separation, and emotional tagging. The visualization runs locally on `127.0.0.1:3458` and auto-shuts down after 10 minutes of inactivity.",[198,199,200,201,202],"Launch interactive unified neural graph visualization","Visualize methodology profiles, memories, and knowledge graph connections","Supports 2D force-directed graph with domain separation","Includes color coding by domain, heat, memory type, and emotion","Provides programmatic graph data retrieval for custom analysis",[204,205,206],"Modifying the neural graph or memory system","Providing AI responses or conversational capabilities","Running operations outside the local machine environment","3.0.0","4.4.0","To provide users with a visual overview of their AI's memory system, cognitive profile, and knowledge graph connections through an interactive and feature-rich neural graph visualization.","The extension demonstrates exceptional quality across all evaluated aspects, with a clear purpose, robust documentation, strong security posture, and active maintenance. No significant findings were identified.",99,"A high-quality skill for interactive neural graph visualization with strong documentation and security.",[214,215,216,217,218,219],"visualization","graph","memory","neural-network","knowledge-graph","data-retrieval","global","verified",[223,224,225,226],"When the user wants a visual overview of the AI's memory system or cognitive profile","When exploring the AI's knowledge graph","When needing to present or screenshot the AI's current state","For programmatic analysis or custom visualization of graph data",{"codeQuality":228,"collectedAt":230,"documentation":231,"maintenance":234,"popularity":239,"security":241,"testCoverage":244},{"hasLockfile":229},true,1778683901014,{"descriptionLength":232,"readmeSize":233},272,36381,{"closedIssues90d":235,"forks":236,"hasChangelog":229,"openIssues90d":8,"pushedAt":237,"stars":238},16,8,1778675198000,33,{"npmDownloads":240},14,{"hasNpmPackage":229,"license":242,"smitheryVerified":243},"NOASSERTION",false,{"hasCi":229,"hasTests":229},{"updatedAt":246},1778683915838,{"basePath":248,"githubOwner":249,"githubRepo":250,"locale":18,"slug":13,"type":251},"skills/cortex-visualize","cdeust","Cortex","skill",{"_creationTime":253,"_id":254,"community":255,"display":256,"identity":259,"parentExtension":262,"providers":295,"relations":303,"tags":304,"workflow":305},1778683562157.8752,"k1739s9t9kj9bmjq1z4byk17g986mv7x",{"reviewCount":8},{"description":257,"installMethods":258,"name":250,"sourceUrl":14},"Persistent memory and cognitive profiling for Claude Code — thermodynamic memory with heat/decay, intent-aware retrieval, biological plasticity, codebase intelligence, and cognitive profiling. 47 MCP tools with enriched schemas. PostgreSQL + pgvector in CLI mode; automatic SQLite fallback in Cowork/sandboxed mode. Curated wiki (ADRs, specs, lessons) with audit-artefact filtering. Consolidate is set-based SQL batched — decay/plasticity/pruning run 100-500× faster on large stores. Workflow graph with caller-qualified CALLS chains rendering full method-to-method dependencies (native tree-sitter, no AP required). Side panel humanized for non-technical users. Ingests codebase analysis (ai-automatised-pipeline) and PRDs (prd-spec-generator) into wiki + memory + knowledge graph. Docker image available.",{"claudeCode":250},{"basePath":260,"githubOwner":249,"githubRepo":250,"locale":18,"slug":250,"type":261},"","plugin",{"_creationTime":263,"_id":264,"community":265,"display":266,"identity":270,"providers":272,"relations":289,"tags":291,"workflow":292},1778683562157.875,"k174pnm5ch9ab6fr1etef2f2b586m74b",{"reviewCount":8},{"description":267,"installMethods":268,"name":269,"sourceUrl":14},"Persistent memory and cognitive profiling plugins for Claude Code",{"claudeCode":12},"cortex-plugins",{"basePath":260,"githubOwner":249,"githubRepo":250,"locale":18,"slug":250,"type":271},"marketplace",{"evaluate":273,"extract":283},{"promptVersionExtension":274,"promptVersionScoring":208,"score":275,"tags":276,"targetMarket":220,"tier":221},"3.1.0",100,[216,277,278,279,218,280,281,282],"cognitive-profiling","mcp","claude-code","codebase-analysis","postgresql","pgvector",{"commitSha":284,"marketplace":285,"plugin":287},"HEAD",{"name":269,"pluginCount":286},1,{"mcpCount":8,"provider":288,"skillCount":8},"classify",{"repoId":290},"kd79gxpemvkr09a7zsb3h8kmah86nvgf",[279,280,277,218,278,216,282,281],{"evaluatedAt":293,"extractAt":294,"updatedAt":293},1778683583007,1778683562157,{"evaluate":296,"extract":300},{"promptVersionExtension":207,"promptVersionScoring":208,"score":211,"tags":297,"targetMarket":220,"tier":221},[216,298,218,277,281,282,299],"persistence","developer-tools",{"commitSha":284,"license":301,"plugin":302},"MIT",{"mcpCount":8,"provider":288,"skillCount":240},{"parentExtensionId":264,"repoId":290},[277,299,218,216,298,282,281],{"evaluatedAt":306,"extractAt":294,"updatedAt":306},1778683602463,{"evaluate":308,"extract":310},{"promptVersionExtension":207,"promptVersionScoring":208,"score":211,"tags":309,"targetMarket":220,"tier":221},[214,215,216,217,218,219],{"commitSha":284},{"parentExtensionId":254,"repoId":290},{"_creationTime":313,"_id":290,"identity":314,"providers":315,"workflow":770},1778683544930.988,{"githubOwner":249,"githubRepo":250,"sourceUrl":14},{"classify":316,"discover":742,"extract":745,"github":746,"npm":769},{"commitSha":284,"extensions":317},[318,331,344,353,361,369,377,385,393,401,409,417,425,433,441,446,454],{"basePath":260,"description":267,"displayName":269,"installMethods":319,"rationale":320,"selectedPaths":321,"source":330,"sourceLanguage":18,"type":271},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[322,325,327],{"path":323,"priority":324},".claude-plugin/marketplace.json","mandatory",{"path":326,"priority":324},"README.md",{"path":328,"priority":329},"LICENSE","high","rule",{"basePath":260,"description":257,"displayName":332,"installMethods":333,"rationale":334,"selectedPaths":335,"source":330,"sourceLanguage":18,"type":261},"cortex",{"claudeCode":250},"inline plugin source from marketplace.json at /",[336,337,338,340,342],{"path":326,"priority":324},{"path":328,"priority":329},{"path":339,"priority":324},".mcp.json",{"path":341,"priority":329},"agents/cortex-wiki-groomer.md",{"path":343,"priority":329},"commands/methodology.md",{"basePath":345,"description":346,"displayName":347,"installMethods":348,"rationale":349,"selectedPaths":350,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-automate","Set up automation — prospective memory triggers, neuro-symbolic rules, and CLAUDE.md sync. Use when the user says 'remind me when', 'trigger when', 'create a rule', 'auto-remember', 'sync to CLAUDE.md', 'push insights', 'set up trigger', 'when I open this file', 'when this keyword appears', or when you want to automate memory behavior based on conditions.","cortex-automate",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-automate/SKILL.md",[351],{"path":352,"priority":324},"SKILL.md",{"basePath":354,"description":355,"displayName":356,"installMethods":357,"rationale":358,"selectedPaths":359,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-consolidate","Run memory maintenance — decay old memories, compress stale content, consolidate episodic memories into semantic knowledge, and run sleep-like replay. Use when the user says 'clean up memories', 'consolidate', 'run maintenance', 'compress old memories', 'memory cleanup', or periodically to keep the memory system healthy. Also use after importing many memories or at the end of a long session.","cortex-consolidate",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-consolidate/SKILL.md",[360],{"path":352,"priority":324},{"basePath":362,"description":363,"displayName":364,"installMethods":365,"rationale":366,"selectedPaths":367,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-debug-memory","Debug and fix memory system issues — validate memories, rate quality, manage protection, forget bad memories, and restore from checkpoints. Use when the user says 'fix memory', 'bad memory', 'wrong memory', 'delete this', 'protect this', 'this memory is wrong', 'memory quality', 'rate this memory', 'restore checkpoint', 'undo', or when memories are returning incorrect or stale results.","cortex-debug-memory",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-debug-memory/SKILL.md",[368],{"path":352,"priority":324},{"basePath":370,"description":371,"displayName":372,"installMethods":373,"rationale":374,"selectedPaths":375,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-explore-memory","Explore the memory system's state, find gaps in knowledge, assess coverage, and get diagnostic information. Use when the user asks 'what does my memory look like', 'show me memory stats', 'what am I missing', 'how good is my knowledge', 'memory health', 'show coverage', 'find gaps', 'what topics are weak', or when you need to understand the state of stored knowledge before a task.","cortex-explore-memory",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-explore-memory/SKILL.md",[376],{"path":352,"priority":324},{"basePath":378,"description":379,"displayName":380,"installMethods":381,"rationale":382,"selectedPaths":383,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-import","Import memories from other AI memory systems into Cortex. Supports claude-mem (SQLite), Claude Desktop sessions, ChatGPT web export (JSON), Gemini Takeout (JSON), Cursor conversations, and Claude Code JSONL. Use when the user says 'import from claude-mem', 'migrate memories', 'import ChatGPT history', 'import from Gemini', 'transfer memories', or when Cortex detects another memory system is installed.","cortex-import",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-import/SKILL.md",[384],{"path":352,"priority":324},{"basePath":386,"description":387,"displayName":388,"installMethods":389,"rationale":390,"selectedPaths":391,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-navigate-knowledge","Navigate the knowledge graph — trace entity relationships, explore causal chains, drill into memory clusters, and traverse co-access paths. Use when the user asks 'how are these related', 'what connects X to Y', 'show me the knowledge graph', 'trace the relationship', 'what caused X', 'drill down into', 'explore connections', or when you need to understand the web of relationships between concepts, entities, and memories.","cortex-navigate-knowledge",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-navigate-knowledge/SKILL.md",[392],{"path":352,"priority":324},{"basePath":394,"description":395,"displayName":396,"installMethods":397,"rationale":398,"selectedPaths":399,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-profile","View and manage your cognitive profile — how you think, work patterns, blind spots, and cross-domain connections. Use when the user says 'show my profile', 'how do I work', 'what are my patterns', 'cognitive style', 'blind spots', 'methodology', or at the start of a session to load context. Also use 'rebuild profile' to rescan all session history, or 'list domains' to see all tracked project domains.","cortex-profile",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-profile/SKILL.md",[400],{"path":352,"priority":324},{"basePath":402,"description":403,"displayName":404,"installMethods":405,"rationale":406,"selectedPaths":407,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-recall","Search and retrieve memories from Cortex persistent memory. Use when the user asks 'what did we decide about X', 'do you remember', 'what was the fix for', 'find that thing about', 'search memories', 'what do we know about', 'have we seen this before', or when you need context about past decisions, patterns, bugs, or architecture choices. Also use proactively when working on something that likely has relevant historical context.","cortex-recall",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-recall/SKILL.md",[408],{"path":352,"priority":324},{"basePath":410,"description":411,"displayName":412,"installMethods":413,"rationale":414,"selectedPaths":415,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-recall-global","Search and retrieve global memories — knowledge that applies across all projects. Use when the user asks 'what are our coding standards', 'what conventions do we follow', 'what's our infrastructure setup', 'do we have a rule about', 'what applies to all projects', 'shared knowledge', 'global rules', or when you need cross-project context like architecture decisions, server configs, or team policies.","cortex-recall-global",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-recall-global/SKILL.md",[416],{"path":352,"priority":324},{"basePath":418,"description":419,"displayName":420,"installMethods":421,"rationale":422,"selectedPaths":423,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-remember","Store important decisions, patterns, errors, lessons, and context into Cortex persistent memory. Use when the user says 'remember this', 'save this', 'store this for later', 'note this down', 'don't forget', 'this is important', 'bookmark this', or when a significant decision, bug fix, architecture choice, or lesson learned occurs during a session. Also use after resolving tricky bugs, making technology choices, or discovering important patterns.","cortex-remember",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-remember/SKILL.md",[424],{"path":352,"priority":324},{"basePath":426,"description":427,"displayName":428,"installMethods":429,"rationale":430,"selectedPaths":431,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-remember-global","Store a global memory that is visible across all projects. Use when the user shares architecture rules, coding conventions, infrastructure facts, security policies, team agreements, or any knowledge that applies beyond a single project. Triggers on 'remember this everywhere', 'this applies to all projects', 'global rule', 'shared convention', 'infrastructure note', 'cross-project', or when the content is clearly universal (clean architecture, SOLID, deployment configs, server addresses).","cortex-remember-global",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-remember-global/SKILL.md",[432],{"path":352,"priority":324},{"basePath":434,"description":435,"displayName":436,"installMethods":437,"rationale":438,"selectedPaths":439,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-setup-project","Bootstrap Cortex for a new project or import existing session history. Use when the user says 'set up Cortex', 'seed this project', 'import my history', 'backfill memories', 'bootstrap memory', 'initialize Cortex for this project', or when starting to use Cortex on an existing codebase that already has Claude Code conversation history.","cortex-setup-project",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-setup-project/SKILL.md",[440],{"path":352,"priority":324},{"basePath":248,"description":10,"displayName":13,"installMethods":442,"rationale":443,"selectedPaths":444,"source":330,"sourceLanguage":18,"type":251},{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-visualize/SKILL.md",[445],{"path":352,"priority":324},{"basePath":447,"description":448,"displayName":449,"installMethods":450,"rationale":451,"selectedPaths":452,"source":330,"sourceLanguage":18,"type":251},"skills/cortex-wiki-author","Author first-class wiki pages (ADRs, specs, file docs, notes) that live alongside Cortex memory. Use when the user says 'this is an ADR', 'document this decision', 'write an ADR', 'add a spec', 'spec this out', 'document this file', 'add a note about', 'link these pages', 'bookmark this as a spec', or when finalizing a design decision that should persist as a human-readable document.","cortex-wiki-author",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-wiki-author/SKILL.md",[453],{"path":352,"priority":324},{"basePath":260,"description":455,"displayName":456,"installMethods":457,"license":301,"rationale":458,"selectedPaths":459,"source":330,"sourceLanguage":18,"type":278},"Persistent memory and cognitive profiling for Claude Code","neuro-cortex-memory",{"pypi":456},"pyproject.toml with mcp/fastmcp dependency + scripts at pyproject.toml",[460,462,464,465,466,469,472,474,476,478,480,482,484,486,488,490,492,494,496,498,500,502,504,506,508,510,512,514,516,518,520,522,524,526,528,530,532,534,536,538,540,542,544,546,548,550,552,554,556,558,560,562,564,566,568,570,572,574,576,578,580,582,584,586,588,590,592,594,596,598,600,602,604,606,608,610,612,614,616,618,620,622,624,626,628,630,632,634,636,638,640,642,644,646,648,650,652,654,656,658,660,662,664,666,668,670,672,674,676,678,680,682,684,686,688,690,692,694,696,698,700,702,704,706,708,710,712,714,716,718,720,722,724,726,728,730,732,734,736,738,740],{"path":461,"priority":324},"package.json",{"path":463,"priority":324},"pyproject.toml",{"path":326,"priority":324},{"path":328,"priority":329},{"path":467,"priority":468},"mcp_server/doctor.py","medium",{"path":470,"priority":471},"mcp_server/__main__.py","low",{"path":473,"priority":471},"mcp_server/handlers/__init__.py",{"path":475,"priority":471},"mcp_server/handlers/_telemetry_wrap.py",{"path":477,"priority":471},"mcp_server/handlers/_tool_meta.py",{"path":479,"priority":471},"mcp_server/handlers/add_rule.py",{"path":481,"priority":471},"mcp_server/handlers/admission.py",{"path":483,"priority":471},"mcp_server/handlers/anchor.py",{"path":485,"priority":471},"mcp_server/handlers/assess_coverage.py",{"path":487,"priority":471},"mcp_server/handlers/backfill_helpers.py",{"path":489,"priority":471},"mcp_server/handlers/backfill_memories.py",{"path":491,"priority":471},"mcp_server/handlers/change_impact.py",{"path":493,"priority":471},"mcp_server/handlers/checkpoint.py",{"path":495,"priority":471},"mcp_server/handlers/codebase_analyze.py",{"path":497,"priority":471},"mcp_server/handlers/codebase_analyze_helpers.py",{"path":499,"priority":471},"mcp_server/handlers/consolidate.py",{"path":501,"priority":471},"mcp_server/handlers/consolidation/__init__.py",{"path":503,"priority":471},"mcp_server/handlers/consolidation/cascade.py",{"path":505,"priority":471},"mcp_server/handlers/consolidation/cls.py",{"path":507,"priority":471},"mcp_server/handlers/consolidation/compression.py",{"path":509,"priority":471},"mcp_server/handlers/consolidation/decay.py",{"path":511,"priority":471},"mcp_server/handlers/consolidation/homeostatic.py",{"path":513,"priority":471},"mcp_server/handlers/consolidation/memify.py",{"path":515,"priority":471},"mcp_server/handlers/consolidation/plasticity.py",{"path":517,"priority":471},"mcp_server/handlers/consolidation/pruning.py",{"path":519,"priority":471},"mcp_server/handlers/consolidation/sleep.py",{"path":521,"priority":471},"mcp_server/handlers/consolidation/transfer.py",{"path":523,"priority":471},"mcp_server/handlers/create_trigger.py",{"path":525,"priority":471},"mcp_server/handlers/detect_domain.py",{"path":527,"priority":471},"mcp_server/handlers/detect_gaps.py",{"path":529,"priority":471},"mcp_server/handlers/drill_down.py",{"path":531,"priority":471},"mcp_server/handlers/explore_features.py",{"path":533,"priority":471},"mcp_server/handlers/forget.py",{"path":535,"priority":471},"mcp_server/handlers/get_causal_chain.py",{"path":537,"priority":471},"mcp_server/handlers/get_methodology_graph.py",{"path":539,"priority":471},"mcp_server/handlers/get_project_story.py",{"path":541,"priority":471},"mcp_server/handlers/get_rules.py",{"path":543,"priority":471},"mcp_server/handlers/get_telemetry.py",{"path":545,"priority":471},"mcp_server/handlers/import_sessions.py",{"path":547,"priority":471},"mcp_server/handlers/ingest_codebase.py",{"path":549,"priority":471},"mcp_server/handlers/ingest_codebase_cypher.py",{"path":551,"priority":471},"mcp_server/handlers/ingest_codebase_graph.py",{"path":553,"priority":471},"mcp_server/handlers/ingest_codebase_pages.py",{"path":555,"priority":471},"mcp_server/handlers/ingest_codebase_schema.py",{"path":557,"priority":471},"mcp_server/handlers/ingest_codebase_writers.py",{"path":559,"priority":471},"mcp_server/handlers/ingest_helpers.py",{"path":561,"priority":471},"mcp_server/handlers/ingest_prd.py",{"path":563,"priority":471},"mcp_server/handlers/latency_class.py",{"path":565,"priority":471},"mcp_server/handlers/list_domains.py",{"path":567,"priority":471},"mcp_server/handlers/memories_facets.py",{"path":569,"priority":471},"mcp_server/handlers/memories_page.py",{"path":571,"priority":471},"mcp_server/handlers/memory_stats.py",{"path":573,"priority":471},"mcp_server/handlers/narrative.py",{"path":575,"priority":471},"mcp_server/handlers/navigate_memory.py",{"path":577,"priority":471},"mcp_server/handlers/open_visualization.py",{"path":579,"priority":471},"mcp_server/handlers/quadtree_handler.py",{"path":581,"priority":471},"mcp_server/handlers/query_methodology.py",{"path":583,"priority":471},"mcp_server/handlers/query_workflow_graph.py",{"path":585,"priority":471},"mcp_server/handlers/rate_memory.py",{"path":587,"priority":471},"mcp_server/handlers/rebuild_profiles.py",{"path":589,"priority":471},"mcp_server/handlers/recall.py",{"path":591,"priority":471},"mcp_server/handlers/recall_helpers.py",{"path":593,"priority":471},"mcp_server/handlers/recall_hierarchical.py",{"path":595,"priority":471},"mcp_server/handlers/recompute_layout.py",{"path":597,"priority":471},"mcp_server/handlers/record_session_end.py",{"path":599,"priority":471},"mcp_server/handlers/remember.py",{"path":601,"priority":471},"mcp_server/handlers/remember_helpers.py",{"path":603,"priority":471},"mcp_server/handlers/remember_response.py",{"path":605,"priority":471},"mcp_server/handlers/seed_project.py",{"path":607,"priority":471},"mcp_server/handlers/seed_project_constants.py",{"path":609,"priority":471},"mcp_server/handlers/seed_project_stages.py",{"path":611,"priority":471},"mcp_server/handlers/sync_instructions.py",{"path":613,"priority":471},"mcp_server/handlers/tile_handler.py",{"path":615,"priority":471},"mcp_server/handlers/unified_search.py",{"path":617,"priority":471},"mcp_server/handlers/validate_memory.py",{"path":619,"priority":471},"mcp_server/handlers/wiki_adr.py",{"path":621,"priority":471},"mcp_server/handlers/wiki_api.py",{"path":623,"priority":471},"mcp_server/handlers/wiki_compile.py",{"path":625,"priority":471},"mcp_server/handlers/wiki_consolidate.py",{"path":627,"priority":471},"mcp_server/handlers/wiki_curate.py",{"path":629,"priority":471},"mcp_server/handlers/wiki_emerge.py",{"path":631,"priority":471},"mcp_server/handlers/wiki_export.py",{"path":633,"priority":471},"mcp_server/handlers/wiki_extract.py",{"path":635,"priority":471},"mcp_server/handlers/wiki_link.py",{"path":637,"priority":471},"mcp_server/handlers/wiki_list.py",{"path":639,"priority":471},"mcp_server/handlers/wiki_migrate.py",{"path":641,"priority":471},"mcp_server/handlers/wiki_pipeline.py",{"path":643,"priority":471},"mcp_server/handlers/wiki_purge.py",{"path":645,"priority":471},"mcp_server/handlers/wiki_read.py",{"path":647,"priority":471},"mcp_server/handlers/wiki_refine.py",{"path":649,"priority":471},"mcp_server/handlers/wiki_reindex.py",{"path":651,"priority":471},"mcp_server/handlers/wiki_rename.py",{"path":653,"priority":471},"mcp_server/handlers/wiki_resolve.py",{"path":655,"priority":471},"mcp_server/handlers/wiki_seed_codebase.py",{"path":657,"priority":471},"mcp_server/handlers/wiki_synthesize.py",{"path":659,"priority":471},"mcp_server/handlers/wiki_verify.py",{"path":661,"priority":471},"mcp_server/handlers/wiki_view.py",{"path":663,"priority":471},"mcp_server/handlers/wiki_write.py",{"path":665,"priority":471},"mcp_server/handlers/workflow_graph.py",{"path":667,"priority":471},"tests_py/handlers/__init__.py",{"path":669,"priority":471},"tests_py/handlers/test_a3_homeostatic_scalar.py",{"path":671,"priority":471},"tests_py/handlers/test_admission.py",{"path":673,"priority":471},"tests_py/handlers/test_backfill_discover_files_issue15.py",{"path":675,"priority":471},"tests_py/handlers/test_backfill_heat.py",{"path":677,"priority":471},"tests_py/handlers/test_beam_anticheat.py",{"path":679,"priority":471},"tests_py/handlers/test_checkpoint.py",{"path":681,"priority":471},"tests_py/handlers/test_cls_diagnostics.py",{"path":683,"priority":471},"tests_py/handlers/test_codebase_analyze_rglob.py",{"path":685,"priority":471},"tests_py/handlers/test_consolidate.py",{"path":687,"priority":471},"tests_py/handlers/test_consolidate_telemetry.py",{"path":689,"priority":471},"tests_py/handlers/test_detect_domain.py",{"path":691,"priority":471},"tests_py/handlers/test_explore_features.py",{"path":693,"priority":471},"tests_py/handlers/test_get_methodology_graph.py",{"path":695,"priority":471},"tests_py/handlers/test_get_telemetry.py",{"path":697,"priority":471},"tests_py/handlers/test_import_sessions.py",{"path":699,"priority":471},"tests_py/handlers/test_import_sessions_stream.py",{"path":701,"priority":471},"tests_py/handlers/test_ingest_codebase.py",{"path":703,"priority":471},"tests_py/handlers/test_ingest_prd.py",{"path":705,"priority":471},"tests_py/handlers/test_latency_class.py",{"path":707,"priority":471},"tests_py/handlers/test_list_domains.py",{"path":709,"priority":471},"tests_py/handlers/test_memify_diagnostics.py",{"path":711,"priority":471},"tests_py/handlers/test_memory_stats.py",{"path":713,"priority":471},"tests_py/handlers/test_open_visualization.py",{"path":715,"priority":471},"tests_py/handlers/test_query_methodology.py",{"path":717,"priority":471},"tests_py/handlers/test_query_workflow_graph.py",{"path":719,"priority":471},"tests_py/handlers/test_rebuild_profiles.py",{"path":721,"priority":471},"tests_py/handlers/test_recall.py",{"path":723,"priority":471},"tests_py/handlers/test_recall_enhancements.py",{"path":725,"priority":471},"tests_py/handlers/test_recall_hierarchical_bounded.py",{"path":727,"priority":471},"tests_py/handlers/test_recall_low_signal_filter.py",{"path":729,"priority":471},"tests_py/handlers/test_record_session_end.py",{"path":731,"priority":471},"tests_py/handlers/test_registry.py",{"path":733,"priority":471},"tests_py/handlers/test_remember.py",{"path":735,"priority":471},"tests_py/handlers/test_seed_project.py",{"path":737,"priority":471},"tests_py/handlers/test_wiki_redirect_handlers.py",{"path":739,"priority":471},"tests_py/handlers/test_wiki_seed_codebase.py",{"path":741,"priority":471},"tests_py/handlers/test_wiki_sync_errors.py",{"sources":743},[744],"manual",{"npmPackage":456},{"closedIssues90d":235,"description":747,"forks":236,"homepage":748,"license":242,"openIssues90d":8,"pushedAt":237,"readmeSize":233,"stars":238,"topics":749},"Persistent memory for Claude Code — 41 neuroscience papers, 26 biological mechanisms with paper-bearing per-mechanism ablation evidence (E1 v3). LongMemEval R@10 98.4% / MRR 0.9124 (n=500). LoCoMo R@10 94.2% / MRR 0.8278 (n=1986). BEAM-10M +33.4% over flat retrieval. PostgreSQL + pgvector. Verified via 31-row two-benchmark ablation campaign.","https://ai-architect.tools",[750,751,752,753,279,754,755,756,757,758,759,760,761,762,763,764,765,766,767,768],"mcp-server","model-context-protocol","agent-memory-system","causal-inference","claude-code-plugin","cognitive-architecture","cognitive-science","neuroscience","persistent-memory","predictive-coding","retrieval-augmented-generation","vector-search","hopfield-network","long-term-memory","episodic-memory","llm-memory","anthropic","artificial-intelligence","claude",{"downloads":240},{"classifiedAt":771,"discoverAt":772,"extractAt":773,"githubAt":773,"npmAt":774,"updatedAt":771},1778683561790,1778683544931,1778683554398,1778683559402,[219,215,218,216,217,214],{"evaluatedAt":246,"extractAt":294,"updatedAt":246},[],[779,809,839,866,896,918],{"_creationTime":780,"_id":781,"community":782,"display":783,"identity":789,"providers":792,"relations":801,"tags":804,"workflow":805},1778699170774.1592,"k172e8vt4zcz50bb0vfp6ptb1n86mf90",{"reviewCount":8},{"description":784,"installMethods":785,"name":787,"sourceUrl":788},"Use when the user needs X (Twitter) data or confirmation-gated X actions through Xquik: tweet search, user lookup, follower extraction, media download, monitoring, webhooks, MCP, SDKs, posting, likes, DMs, and profile updates. Requires a Xquik API key. Never ask for X login material.",{"claudeCode":786},"Xquik-dev/x-twitter-scraper","x-twitter-scraper","https://github.com/Xquik-dev/x-twitter-scraper",{"basePath":790,"githubOwner":791,"githubRepo":787,"locale":18,"slug":787,"type":251},"skills/x-twitter-scraper","Xquik-dev",{"evaluate":793,"extract":800},{"promptVersionExtension":207,"promptVersionScoring":208,"score":275,"tags":794,"targetMarket":220,"tier":221},[795,796,797,219,798,278,799],"twitter","x","api","automation","sdk",{"commitSha":284},{"parentExtensionId":802,"repoId":803},"k17axvhmvwp90strpqcd5b0h7986m80d","kd783enpnwhry153ka0z65ear186mjbh",[797,798,219,278,799,795,796],{"evaluatedAt":806,"extractAt":807,"updatedAt":808},1778699230863,1778699170774,1778699296021,{"_creationTime":810,"_id":811,"community":812,"display":813,"identity":819,"providers":825,"relations":832,"tags":835,"workflow":836},1778696505500.0078,"k174n9sd7wv9knh3b8rv7vv2wh86me74",{"reviewCount":8},{"description":814,"installMethods":815,"name":817,"sourceUrl":818},"Search and retrieve content from Reddit. Get posts, comments, subreddit info, and user profiles via the public JSON API. Use when user mentions Reddit, a subreddit, or r/ links.",{"claudeCode":816},"ReScienceLab/opc-skills","Reddit","https://github.com/ReScienceLab/opc-skills",{"basePath":820,"githubOwner":821,"githubRepo":822,"locale":823,"slug":824,"type":251},"skills/reddit","ReScienceLab","opc-skills","fr","reddit",{"evaluate":826,"extract":830},{"promptVersionExtension":207,"promptVersionScoring":208,"score":275,"tags":827,"targetMarket":220,"tier":221},[824,797,219,828,829],"social-media","information-gathering",{"commitSha":284,"license":831},"Apache-2.0",{"parentExtensionId":833,"repoId":834},"k17b55rp7ccqw91566yq0ax2as86n6rk","kd7fj56h5kejcgm6hcjmzn79xd86m7wa",[797,219,829,824,828],{"evaluatedAt":837,"extractAt":838,"updatedAt":837},1778696852717,1778696505500,{"_creationTime":840,"_id":841,"community":842,"display":843,"identity":849,"providers":853,"relations":860,"tags":862,"workflow":863},1778675145461.862,"k17743wxmacrq9ja7re0fbesf586nn02",{"reviewCount":8},{"description":844,"installMethods":845,"name":847,"sourceUrl":848},"Query ClinicalTrials.gov via API v2. Search trials by condition, drug, location, status, or phase. Retrieve trial details by NCT ID, export data, for clinical research and patient matching. Part of the AlterLab Academic Skills suite.",{"claudeCode":846},"AlterLab-IEU/AlterLab-Academic-Skills","alterlab-clinicaltrials","https://github.com/AlterLab-IEU/AlterLab-Academic-Skills",{"basePath":850,"githubOwner":851,"githubRepo":852,"locale":18,"slug":847,"type":251},"skills/databases/alterlab-clinicaltrials","AlterLab-IEU","AlterLab-Academic-Skills",{"evaluate":854,"extract":859},{"promptVersionExtension":207,"promptVersionScoring":208,"score":275,"tags":855,"targetMarket":220,"tier":221},[856,857,797,219,858],"clinical-trials","medical-research","python",{"commitSha":284},{"repoId":861},"kd7fqvj70pvyn4r3q9kctpnd7d86mfqd",[797,856,219,857,858],{"evaluatedAt":864,"extractAt":865,"updatedAt":864},1778677173231,1778675145461,{"_creationTime":867,"_id":868,"community":869,"display":870,"identity":876,"providers":881,"relations":889,"tags":891,"workflow":892},1778692549705.5178,"k174cf2tsjpdqmm0p10s8ybq5h86ngnx",{"reviewCount":8},{"description":871,"installMethods":872,"name":874,"sourceUrl":875},"Analyze a Karpathy-pattern LLM wiki knowledge base and generate an interactive knowledge graph with entity extraction, implicit relationships, and topic clustering.",{"claudeCode":873},"Lum1104/Understand-Anything","Understand Knowledge","https://github.com/Lum1104/Understand-Anything",{"basePath":877,"githubOwner":878,"githubRepo":879,"locale":18,"slug":880,"type":251},"understand-anything-plugin/skills/understand-knowledge","Lum1104","Understand-Anything","understand-knowledge",{"evaluate":882,"extract":888},{"promptVersionExtension":207,"promptVersionScoring":208,"score":275,"tags":883,"targetMarket":220,"tier":221},[218,884,885,886,887,858],"llm","wiki","entity-extraction","topic-clustering",{"commitSha":284,"license":301},{"repoId":890},"kd78egfytykkxxbpr6k3t7wsph86n83x",[886,218,884,858,887,885],{"evaluatedAt":893,"extractAt":894,"updatedAt":895},1778692723004,1778692549705,1778692826052,{"_creationTime":897,"_id":898,"community":899,"display":900,"identity":904,"providers":906,"relations":913,"tags":914,"workflow":915},1778692549705.5173,"k177vkkb19azmt7j1vw5x8bzcn86nwhe",{"reviewCount":8},{"description":901,"installMethods":902,"name":903,"sourceUrl":875},"Extract business domain knowledge from a codebase and generate an interactive domain flow graph. Works standalone (lightweight scan) or derives from an existing /understand knowledge graph.",{"claudeCode":873},"understand-domain",{"basePath":905,"githubOwner":878,"githubRepo":879,"locale":18,"slug":903,"type":251},"understand-anything-plugin/skills/understand-domain",{"evaluate":907,"extract":912},{"promptVersionExtension":207,"promptVersionScoring":208,"score":275,"tags":908,"targetMarket":220,"tier":221},[909,218,299,910,911],"code-analysis","code-visualization","domain-modeling",{"commitSha":284},{"repoId":890},[909,910,299,911,218],{"evaluatedAt":916,"extractAt":894,"updatedAt":917},1778692688178,1778692825689,{"_creationTime":919,"_id":920,"community":921,"display":922,"identity":926,"providers":928,"relations":933,"tags":934,"workflow":935},1778692549705.5168,"k175km8dk28xhp6eprw09sfsax86n9cs",{"reviewCount":8},{"description":923,"installMethods":924,"name":925,"sourceUrl":875},"Launch the interactive web dashboard to visualize a codebase's knowledge graph",{"claudeCode":873},"understand-dashboard",{"basePath":927,"githubOwner":878,"githubRepo":879,"locale":18,"slug":925,"type":251},"understand-anything-plugin/skills/understand-dashboard",{"evaluate":929,"extract":932},{"promptVersionExtension":207,"promptVersionScoring":208,"score":275,"tags":930,"targetMarket":220,"tier":221},[910,218,299,280,931],"dashboard",{"commitSha":284},{"repoId":890},[910,280,931,299,218],{"evaluatedAt":936,"extractAt":894,"updatedAt":937},1778692645619,1778692825282]