[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"extension-skill-cdeust-cortex-explore-memory-de":3,"guides-for-cdeust-cortex-explore-memory":774,"similar-k177jwx0k8bpyh2c59vk0xzfq586nk6z-de":775},{"_creationTime":4,"_id":5,"children":6,"community":7,"display":9,"evaluation":15,"identity":243,"isFallback":225,"parentExtension":248,"providers":305,"relations":309,"repo":310,"tags":772,"workflow":773},1778683562157.8762,"k177jwx0k8bpyh2c59vk0xzfq586nk6z",[],{"reviewCount":8},0,{"description":10,"installMethods":11,"name":13,"sourceUrl":14},"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.",{"claudeCode":12},"cdeust/Cortex","cortex-explore-memory","https://github.com/cdeust/Cortex",{"_creationTime":16,"_id":17,"extensionId":5,"locale":18,"result":19,"trustSignals":223,"workflow":241},1778683686741.7454,"kn7c5yc4tzepm2ma36d8gbz1ms86n1yk","en",{"checks":20,"evaluatedAt":192,"extensionSummary":193,"features":194,"nonGoals":200,"promptVersionExtension":204,"promptVersionScoring":205,"purpose":206,"rationale":207,"score":208,"summary":209,"tags":210,"targetMarket":216,"tier":217,"useCases":218},[21,26,29,32,36,39,43,47,50,53,57,61,64,68,71,74,77,80,83,86,90,94,98,102,107,110,114,117,121,124,127,130,133,136,139,143,147,150,153,157,160,163,166,169,173,176,179,182,185,189],{"category":22,"check":23,"severity":24,"summary":25},"Practical Utility","Problem relevance","pass","The description clearly identifies the user problem of understanding memory state, gaps, and coverage, and provides specific use cases and triggers.",{"category":22,"check":27,"severity":24,"summary":28},"Unique selling proposition","The skill offers significant value beyond default behavior by implementing a complex, multi-faceted memory system based on computational neuroscience, going far beyond simple keyword retrieval or basic LLM memory.",{"category":22,"check":30,"severity":24,"summary":31},"Production readiness","The extension appears production-ready, with comprehensive documentation, clear installation instructions, and a robust set of tools covering diagnostics, coverage assessment, gap detection, validation, and narrative summaries, all designed for local execution.",{"category":33,"check":34,"severity":24,"summary":35},"Scope","Single responsibility principle","The skill focuses on the single domain of memory system exploration and diagnostics, with all tools and features contributing to this core purpose.",{"category":33,"check":37,"severity":24,"summary":38},"Description quality","The displayed description accurately reflects the skill's capabilities and use cases, providing clear triggers and boundaries.",{"category":40,"check":41,"severity":24,"summary":42},"Invocation","Scoped tools","Tools are narrowly scoped verb-noun specialists (e.g., `memory_stats`, `assess_coverage`) which facilitates precise agent selection.",{"category":44,"check":45,"severity":24,"summary":46},"Documentation","Configuration & parameter reference","All parameters for the described tools (`domain`, `query`, `directory`, `period`, `style`) are documented in the SKILL.md, with their expected types and purposes implicitly clear from the context.",{"category":33,"check":48,"severity":24,"summary":49},"Tool naming","All exposed tool names are descriptive and follow a clear verb-noun pattern (e.g., `memory_stats`, `assess_coverage`, `detect_gaps`).",{"category":33,"check":51,"severity":24,"summary":52},"Minimal I/O surface","Input parameters are explicitly defined for each tool, and the described outputs are focused on the requested diagnostic information without extraneous data.",{"category":54,"check":55,"severity":24,"summary":56},"License","License usability","The extension is licensed under MIT, a permissive open-source license, clearly stated in the README and LICENSE file.",{"category":58,"check":59,"severity":24,"summary":60},"Maintenance","Commit recency","The repository shows recent commits as of May 13, 2026, indicating active maintenance.",{"category":58,"check":62,"severity":24,"summary":63},"Dependency Management","The project includes a lockfile (`hasLockfile: true`) and uses standard package managers, suggesting good dependency management practices.",{"category":65,"check":66,"severity":24,"summary":67},"Security","Secret Management","The extension runs locally with no external secrets required for its core functionality, and documentation emphasizes local execution, mitigating secret handling risks.",{"category":65,"check":69,"severity":24,"summary":70},"Injection","The extension operates locally on local project files and internal memory structures, with no indication of loading or executing untrusted third-party data.",{"category":65,"check":72,"severity":24,"summary":73},"Transitive Supply-Chain Grenades","The skill runs 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 extension is designed to run locally and operate within its own project scope or local data stores, with no indications of attempting to modify files outside its designated area.",{"category":65,"check":78,"severity":24,"summary":79},"Sandbox escape primitives","No detached processes or retry loops around denied tool calls were detected in the provided source or documentation.",{"category":65,"check":81,"severity":24,"summary":82},"Data Exfiltration","The extension operates locally and does not submit any data to third parties; all operations are contained within the user's machine.",{"category":65,"check":84,"severity":24,"summary":85},"Hidden Text Tricks","The bundled content and documentation appear free of hidden text tricks or obfuscated instructions.",{"category":87,"check":88,"severity":24,"summary":89},"Hooks","Opaque code execution","The provided scripts and documentation do not indicate the use of obfuscation techniques like base64 payloads or runtime fetched code.",{"category":91,"check":92,"severity":24,"summary":93},"Portability","Structural Assumption","The `validate_memory` tool explicitly takes a `directory` parameter, allowing users to specify the project root, thus avoiding rigid structural assumptions.",{"category":95,"check":96,"severity":24,"summary":97},"Trust","Issues Attention","With 0 issues opened and 16 closed in the last 90 days, the maintainers show strong engagement and responsiveness.",{"category":99,"check":100,"severity":24,"summary":101},"Versioning","Release Management","The project declares a meaningful version (v3.15.0) in the README and appears to have a CHANGELOG, indicating proper release management.",{"category":103,"check":104,"severity":105,"summary":106},"Execution","Validation","not_applicable","The extension primarily uses predefined tools with structured parameters, and the documentation does not indicate complex input validation is required beyond standard parameter types.",{"category":65,"check":108,"severity":24,"summary":109},"Unguarded Destructive Operations","The skill's tools are primarily for diagnostics and analysis, not destructive operations, thus not requiring guarding.",{"category":111,"check":112,"severity":24,"summary":113},"Code Execution","Error Handling","The provided SKILL.md outlines expected outputs and failure modes with recovery steps, suggesting robust error handling.",{"category":111,"check":115,"severity":105,"summary":116},"Logging","As the extension operates locally and doesn't perform destructive actions or outbound calls, explicit local audit logging is not a primary concern.",{"category":118,"check":119,"severity":24,"summary":120},"Compliance","GDPR","The extension operates on internal memory structures and project files, not sensitive personal data, and is designed for local execution.",{"category":118,"check":122,"severity":24,"summary":123},"Target market","The extension is designed for local use on user projects and has no regional or jurisdictional limitations, making it global.",{"category":91,"check":125,"severity":24,"summary":126},"Runtime stability","The extension is designed for local execution and uses standard PostgreSQL and Python, with no specific OS or shell assumptions beyond general POSIX compatibility implied by local execution.",{"category":44,"check":128,"severity":24,"summary":129},"README","The README is extensive and clearly explains the project's purpose, features, and science.",{"category":33,"check":131,"severity":24,"summary":132},"Tool surface size","The skill exposes a manageable number of tools (5 primary tools) focused on memory exploration.",{"category":40,"check":134,"severity":24,"summary":135},"Overlapping near-synonym tools","The exposed tools have distinct names and functionalities, avoiding redundancy.",{"category":44,"check":137,"severity":24,"summary":138},"Phantom features","All advertised features, such as memory diagnostics and coverage assessment, are directly implemented by the described tools.",{"category":140,"check":141,"severity":24,"summary":142},"Install","Installation instruction","The README provides clear, copy-pasteable installation and setup instructions, including verification steps.",{"category":144,"check":145,"severity":24,"summary":146},"Errors","Actionable error messages","The SKILL.md outlines failure modes with observable symptoms and recovery steps, indicating actionable error reporting.",{"category":103,"check":148,"severity":24,"summary":149},"Pinned dependencies","The presence of a lockfile and clear Python version requirements in the README indicates pinned dependencies.",{"category":33,"check":151,"severity":105,"summary":152},"Dry-run preview","The skill's operations are analytical and diagnostic, not state-changing or outbound, making a dry-run preview not applicable.",{"category":154,"check":155,"severity":105,"summary":156},"Protocol","Idempotent retry & timeouts","The skill's operations are local and analytical, not involving remote calls or state-changing operations that would require idempotency or timeouts.",{"category":118,"check":158,"severity":24,"summary":159},"Telemetry opt-in","The extension is explicitly designed for local execution with no data leaving the user's machine, thus emitting no telemetry.",{"category":40,"check":161,"severity":24,"summary":162},"Precise Purpose","The SKILL.md clearly defines the skill's purpose (explore memory system's state) and when to use it, with specific triggers and boundaries.",{"category":40,"check":164,"severity":24,"summary":165},"Concise Frontmatter","The frontmatter is concise and self-contained, summarizing the core capability and providing specific trigger phrases.",{"category":44,"check":167,"severity":24,"summary":168},"Concise Body","The SKILL.md is reasonably concise, outlining the workflow and delegating deeper details to separate files like the science documentation.",{"category":170,"check":171,"severity":24,"summary":172},"Context","Progressive Disclosure","Deeper scientific details and extensive benchmark results are appropriately delegated to separate markdown files, adhering to progressive disclosure.",{"category":170,"check":174,"severity":105,"summary":175},"Forked exploration","This skill focuses on diagnostics and does not involve deep exploration that would require a forked context.",{"category":22,"check":177,"severity":24,"summary":178},"Usage examples","The SKILL.md provides clear code examples for each major tool, showing input and expected output format.",{"category":22,"check":180,"severity":24,"summary":181},"Edge cases","The SKILL.md mentions failure modes like stale references and potential knowledge gaps, with recovery steps suggested for some (e.g., running `validate_memory`).",{"category":111,"check":183,"severity":105,"summary":184},"Tool Fallback","The skill uses local tools and does not rely on an external MCP server, so fallbacks are not applicable.",{"category":186,"check":187,"severity":24,"summary":188},"Safety","Halt on unexpected state","The documentation implies that operations like `validate_memory` help detect and correct unexpected states (stale references), aligning with halting on unexpected pre-state.",{"category":91,"check":190,"severity":24,"summary":191},"Cross-skill coupling","The skill is self-contained and focused on memory diagnostics, not implicitly relying on or directly coordinating with other specific skills.",1778683686632,"This skill provides tools to explore the state of an LLM's memory system, including diagnostics, knowledge coverage assessment, gap detection, and file reference validation. It operates locally and uses computational neuroscience principles for memory management.",[195,196,197,198,199],"Explore memory system diagnostics","Assess knowledge coverage for topics/projects","Detect knowledge gaps (isolated entities, sparse domains)","Validate memory against existing file paths","Generate narrative summaries of memory content",[201,202,203],"Modifying or directly editing memories (focus is on diagnostics)","Providing general LLM conversational capabilities","Managing external project dependencies or build systems","3.0.0","4.4.0","To provide users with deep insights into their LLM's memory system, allowing them to understand knowledge coverage, identify gaps, and diagnose system health before or during complex tasks.","The skill is exceptionally well-documented, technically sound, and focused on a clear utility. The comprehensive documentation, adherence to best practices, and strong community engagement (high issue closure rate) support a verified tier.",98,"A high-quality skill for exploring and diagnosing the state of an LLM's memory system.",[211,212,213,214,215],"memory","diagnostics","knowledge-management","code-analysis","llm-memory","global","verified",[219,220,221,222],"When starting a new project phase to understand existing knowledge.","After a long break to re-orient with stored information.","To diagnose memory health and identify areas needing updates.","Before undertaking complex tasks to ensure sufficient context is stored.",{"codeQuality":224,"collectedAt":226,"documentation":227,"maintenance":230,"popularity":235,"security":237,"testCoverage":240},{"hasLockfile":225},true,1778683671668,{"descriptionLength":228,"readmeSize":229},383,36381,{"closedIssues90d":231,"forks":232,"hasChangelog":225,"openIssues90d":8,"pushedAt":233,"stars":234},16,8,1778675198000,33,{"npmDownloads":236},14,{"hasNpmPackage":225,"license":238,"smitheryVerified":239},"NOASSERTION",false,{"hasCi":225,"hasTests":225},{"updatedAt":242},1778683686741,{"basePath":244,"githubOwner":245,"githubRepo":246,"locale":18,"slug":13,"type":247},"skills/cortex-explore-memory","cdeust","Cortex","skill",{"_creationTime":249,"_id":250,"community":251,"display":252,"identity":255,"parentExtension":258,"providers":292,"relations":301,"tags":302,"workflow":303},1778683562157.8752,"k1739s9t9kj9bmjq1z4byk17g986mv7x",{"reviewCount":8},{"description":253,"installMethods":254,"name":246,"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":246},{"basePath":256,"githubOwner":245,"githubRepo":246,"locale":18,"slug":246,"type":257},"","plugin",{"_creationTime":259,"_id":260,"community":261,"display":262,"identity":266,"providers":268,"relations":286,"tags":288,"workflow":289},1778683562157.875,"k174pnm5ch9ab6fr1etef2f2b586m74b",{"reviewCount":8},{"description":263,"installMethods":264,"name":265,"sourceUrl":14},"Persistent memory and cognitive profiling plugins for Claude Code",{"claudeCode":12},"cortex-plugins",{"basePath":256,"githubOwner":245,"githubRepo":246,"locale":18,"slug":246,"type":267},"marketplace",{"evaluate":269,"extract":280},{"promptVersionExtension":270,"promptVersionScoring":205,"score":271,"tags":272,"targetMarket":216,"tier":217},"3.1.0",100,[211,273,274,275,276,277,278,279],"cognitive-profiling","mcp","claude-code","knowledge-graph","codebase-analysis","postgresql","pgvector",{"commitSha":281,"marketplace":282,"plugin":284},"HEAD",{"name":265,"pluginCount":283},1,{"mcpCount":8,"provider":285,"skillCount":8},"classify",{"repoId":287},"kd79gxpemvkr09a7zsb3h8kmah86nvgf",[275,277,273,276,274,211,279,278],{"evaluatedAt":290,"extractAt":291,"updatedAt":290},1778683583007,1778683562157,{"evaluate":293,"extract":298},{"promptVersionExtension":204,"promptVersionScoring":205,"score":294,"tags":295,"targetMarket":216,"tier":217},99,[211,296,276,273,278,279,297],"persistence","developer-tools",{"commitSha":281,"license":299,"plugin":300},"MIT",{"mcpCount":8,"provider":285,"skillCount":236},{"parentExtensionId":260,"repoId":287},[273,297,276,211,296,279,278],{"evaluatedAt":304,"extractAt":291,"updatedAt":304},1778683602463,{"evaluate":306,"extract":308},{"promptVersionExtension":204,"promptVersionScoring":205,"score":208,"tags":307,"targetMarket":216,"tier":217},[211,212,213,214,215],{"commitSha":281},{"parentExtensionId":250,"repoId":287},{"_creationTime":311,"_id":287,"identity":312,"providers":313,"workflow":767},1778683544930.988,{"githubOwner":245,"githubRepo":246,"sourceUrl":14},{"classify":314,"discover":740,"extract":743,"github":744,"npm":766},{"commitSha":281,"extensions":315},[316,329,342,351,359,367,372,380,388,396,404,412,420,428,436,444,452],{"basePath":256,"description":263,"displayName":265,"installMethods":317,"rationale":318,"selectedPaths":319,"source":328,"sourceLanguage":18,"type":267},{"claudeCode":12},"marketplace.json at .claude-plugin/marketplace.json",[320,323,325],{"path":321,"priority":322},".claude-plugin/marketplace.json","mandatory",{"path":324,"priority":322},"README.md",{"path":326,"priority":327},"LICENSE","high","rule",{"basePath":256,"description":253,"displayName":330,"installMethods":331,"rationale":332,"selectedPaths":333,"source":328,"sourceLanguage":18,"type":257},"cortex",{"claudeCode":246},"inline plugin source from marketplace.json at /",[334,335,336,338,340],{"path":324,"priority":322},{"path":326,"priority":327},{"path":337,"priority":322},".mcp.json",{"path":339,"priority":327},"agents/cortex-wiki-groomer.md",{"path":341,"priority":327},"commands/methodology.md",{"basePath":343,"description":344,"displayName":345,"installMethods":346,"rationale":347,"selectedPaths":348,"source":328,"sourceLanguage":18,"type":247},"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",[349],{"path":350,"priority":322},"SKILL.md",{"basePath":352,"description":353,"displayName":354,"installMethods":355,"rationale":356,"selectedPaths":357,"source":328,"sourceLanguage":18,"type":247},"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",[358],{"path":350,"priority":322},{"basePath":360,"description":361,"displayName":362,"installMethods":363,"rationale":364,"selectedPaths":365,"source":328,"sourceLanguage":18,"type":247},"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",[366],{"path":350,"priority":322},{"basePath":244,"description":10,"displayName":13,"installMethods":368,"rationale":369,"selectedPaths":370,"source":328,"sourceLanguage":18,"type":247},{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-explore-memory/SKILL.md",[371],{"path":350,"priority":322},{"basePath":373,"description":374,"displayName":375,"installMethods":376,"rationale":377,"selectedPaths":378,"source":328,"sourceLanguage":18,"type":247},"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",[379],{"path":350,"priority":322},{"basePath":381,"description":382,"displayName":383,"installMethods":384,"rationale":385,"selectedPaths":386,"source":328,"sourceLanguage":18,"type":247},"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",[387],{"path":350,"priority":322},{"basePath":389,"description":390,"displayName":391,"installMethods":392,"rationale":393,"selectedPaths":394,"source":328,"sourceLanguage":18,"type":247},"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",[395],{"path":350,"priority":322},{"basePath":397,"description":398,"displayName":399,"installMethods":400,"rationale":401,"selectedPaths":402,"source":328,"sourceLanguage":18,"type":247},"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",[403],{"path":350,"priority":322},{"basePath":405,"description":406,"displayName":407,"installMethods":408,"rationale":409,"selectedPaths":410,"source":328,"sourceLanguage":18,"type":247},"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",[411],{"path":350,"priority":322},{"basePath":413,"description":414,"displayName":415,"installMethods":416,"rationale":417,"selectedPaths":418,"source":328,"sourceLanguage":18,"type":247},"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",[419],{"path":350,"priority":322},{"basePath":421,"description":422,"displayName":423,"installMethods":424,"rationale":425,"selectedPaths":426,"source":328,"sourceLanguage":18,"type":247},"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",[427],{"path":350,"priority":322},{"basePath":429,"description":430,"displayName":431,"installMethods":432,"rationale":433,"selectedPaths":434,"source":328,"sourceLanguage":18,"type":247},"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",[435],{"path":350,"priority":322},{"basePath":437,"description":438,"displayName":439,"installMethods":440,"rationale":441,"selectedPaths":442,"source":328,"sourceLanguage":18,"type":247},"skills/cortex-visualize","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.","cortex-visualize",{"claudeCode":12},"SKILL.md frontmatter at skills/cortex-visualize/SKILL.md",[443],{"path":350,"priority":322},{"basePath":445,"description":446,"displayName":447,"installMethods":448,"rationale":449,"selectedPaths":450,"source":328,"sourceLanguage":18,"type":247},"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",[451],{"path":350,"priority":322},{"basePath":256,"description":453,"displayName":454,"installMethods":455,"license":299,"rationale":456,"selectedPaths":457,"source":328,"sourceLanguage":18,"type":274},"Persistent memory and cognitive profiling for Claude Code","neuro-cortex-memory",{"pypi":454},"pyproject.toml with mcp/fastmcp dependency + scripts at 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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",[748,749,750,751,275,752,753,754,755,756,757,758,759,760,761,762,215,763,764,765],"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","anthropic","artificial-intelligence","claude",{"downloads":236},{"classifiedAt":768,"discoverAt":769,"extractAt":770,"githubAt":770,"npmAt":771,"updatedAt":768},1778683561790,1778683544931,1778683554398,1778683559402,[214,212,213,215,211],{"evaluatedAt":242,"extractAt":291,"updatedAt":242},[],[776,804,832,858,888,918],{"_creationTime":777,"_id":778,"community":779,"display":780,"identity":786,"providers":790,"relations":797,"tags":800,"workflow":801},1778692488329.0144,"k1724vjyyetw8qcfh8anb4c3tn86ngy7",{"reviewCount":8},{"description":781,"installMethods":782,"name":784,"sourceUrl":785},"Extract domain knowledge from existing project sources and generate domain rules. Also handles vault sync and domain listing.",{"claudeCode":783},"luiseiman/claude-kit","domain-extract","https://github.com/luiseiman/claude-kit",{"basePath":787,"githubOwner":788,"githubRepo":789,"locale":18,"slug":784,"type":247},"skills/domain-extract","luiseiman","claude-kit",{"evaluate":791,"extract":796},{"promptVersionExtension":204,"promptVersionScoring":205,"score":271,"tags":792,"targetMarket":216,"tier":217},[793,213,794,214,795],"domain-extraction","rule-generation","automation",{"commitSha":281},{"parentExtensionId":798,"repoId":799},"k17650xadq8363kzchr4w0gmf186nq24","kd79wqc8an5wh20cc2znr8tyb586mxwx",[795,214,793,213,794],{"evaluatedAt":802,"extractAt":803,"updatedAt":802},1778693090713,1778692488329,{"_creationTime":805,"_id":806,"community":807,"display":808,"identity":814,"providers":818,"relations":825,"tags":828,"workflow":829},1778696595410.5657,"k17bk9m02r7jkbzzqapbzfvq8h86m6qn",{"reviewCount":8},{"description":809,"installMethods":810,"name":812,"sourceUrl":813},"Wire Commands, Agents, and Skills together for complex features. Use when building features that need research, planning, and implementation phases.",{"claudeCode":811},"rohitg00/pro-workflow","orchestrate","https://github.com/rohitg00/pro-workflow",{"basePath":815,"githubOwner":816,"githubRepo":817,"locale":18,"slug":812,"type":247},"skills/orchestrate","rohitg00","pro-workflow",{"evaluate":819,"extract":824},{"promptVersionExtension":204,"promptVersionScoring":205,"score":271,"tags":820,"targetMarket":216,"tier":217},[821,822,823,211,213],"llm-ops","agent","workflow",{"commitSha":281},{"parentExtensionId":826,"repoId":827},"k17fxtjcfh5gvxdrhv2dmgn1t986mdhv","kd7am4e918eq98hrd9s31jm4vs86nn0b",[822,213,821,211,823],{"evaluatedAt":830,"extractAt":831,"updatedAt":830},1778696881233,1778696595410,{"_creationTime":833,"_id":834,"community":835,"display":836,"identity":842,"providers":846,"relations":852,"tags":854,"workflow":855},1778696691708.3027,"k174mp6hf33cptbna2p91t2ts586n4ad",{"reviewCount":8},{"description":837,"installMethods":838,"name":840,"sourceUrl":841},"AgentDB memory system with HNSW vector search. Provides 150x-12,500x faster pattern retrieval, persistent storage, and semantic search capabilities for learning and knowledge management. Use when: need to store successful patterns, searching for similar solutions, semantic lookup of past work, learning from previous tasks, sharing knowledge between agents, building knowledge base. Skip when: no learning needed, ephemeral one-off tasks, external data sources available, read-only exploration.\n",{"claudeCode":839},"ruvnet/ruflo","memory-management","https://github.com/ruvnet/ruflo",{"basePath":843,"githubOwner":844,"githubRepo":845,"locale":18,"slug":840,"type":247},".agents/skills/memory-management","ruvnet","ruflo",{"evaluate":847,"extract":851},{"promptVersionExtension":204,"promptVersionScoring":205,"score":294,"tags":848,"targetMarket":216,"tier":217},[211,759,213,849,850],"agentdb","hnsw",{"commitSha":281},{"repoId":853},"kd7ed28gj8n0y3msk5dzrp05zs86nqtc",[849,850,213,211,759],{"evaluatedAt":856,"extractAt":857,"updatedAt":856},1778699160670,1778696691708,{"_creationTime":859,"_id":860,"community":861,"display":862,"identity":868,"providers":873,"relations":881,"tags":884,"workflow":885},1778695548458.377,"k17esa27yncbsd0bz8hcg2crg986mjqk",{"reviewCount":8},{"description":863,"installMethods":864,"name":866,"sourceUrl":867},"Extract the conceptual essence of a repository as skills, agents, and teams — the project's roles, procedures, and coordination patterns expressed as agentskills.io-standard definitions. Reads an arbitrary codebase and produces generalized definitions that capture WHAT the project does and WHO operates it, without replicating HOW it does it. Use when onboarding to a new codebase and wanting to understand its conceptual architecture, when bootstrapping an agentic system from an existing project, when studying a project's organizational DNA for cross-pollination, or when creating a skill/agent/team library inspired by a reference implementation.\n",{"claudeCode":865},"pjt222/agent-almanac","Metal","https://github.com/pjt222/agent-almanac",{"basePath":869,"githubOwner":870,"githubRepo":871,"locale":18,"slug":872,"type":247},"skills/metal","pjt222","agent-almanac","metal",{"evaluate":874,"extract":880},{"promptVersionExtension":204,"promptVersionScoring":205,"score":271,"tags":875,"targetMarket":216,"tier":217},[214,876,877,878,879],"conceptualization","agent-definition","repository-structure","software-architecture",{"commitSha":281,"license":299},{"parentExtensionId":882,"repoId":883},"k170h0janaa9kwn7cfgfz2ykss86mmh9","kd7aryv63z61j39n2td1aeqkvh86mh12",[877,214,876,878,879],{"evaluatedAt":886,"extractAt":887,"updatedAt":886},1778699463464,1778695548458,{"_creationTime":889,"_id":890,"community":891,"display":892,"identity":898,"providers":902,"relations":910,"tags":913,"workflow":914},1778699508017.8022,"k17ayarn0e5prt2n3bh82hxn5n86nv51",{"reviewCount":8},{"description":893,"installMethods":894,"name":896,"sourceUrl":897},"Context Runtime für KI-Agenten — 59 MCP-Tools, 10 Lesemodi, über 95 Shell-Muster, Tree-sitter AST für 18 Sprachen. 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Automatische Installation, falls nicht vorhanden.",{"claudeCode":895},"yvgude/lean-ctx","lean-ctx","https://github.com/yvgude/lean-ctx",{"basePath":899,"githubOwner":900,"githubRepo":896,"locale":901,"slug":896,"type":247},"skills/lean-ctx","yvgude","de",{"evaluate":903,"extract":909},{"promptVersionExtension":204,"promptVersionScoring":205,"score":271,"tags":904,"targetMarket":216,"tier":217},[905,906,907,297,908,214],"context-compression","ai-agent","cli-tools","rust",{"commitSha":281},{"repoId":911,"translatedFrom":912},"kd7dxtfr9j3z54hs3bz0218e1n86may0","k170fxxh22hdspg4vr94whgj1986mpr9",[906,907,214,905,297,908],{"evaluatedAt":915,"extractAt":916,"updatedAt":917},1778699456179,1778699438912,1778699508017,{"_creationTime":919,"_id":920,"community":921,"display":922,"identity":928,"providers":933,"relations":942,"tags":945,"workflow":946},1778698405469.0413,"k17e4wbf2c3x45d7e730x7eje986mc5g",{"reviewCount":8},{"description":923,"installMethods":924,"name":926,"sourceUrl":927},"Ordnet eine Codebasis in Feature-gruppierte Flussdiagramme ein, identifiziert doppelte Belange über Features hinweg und schlägt eine einheitliche Architektur vor. Wird verwendet, wenn nach \"dem idealen Pfad\" gefragt wird, duplizierte Systeme vereinheitlicht oder die Architektur vor einem Refactoring auditiert werden soll. Gibt ein vorgeschlagenes einheitliches Flussdiagramm sowie Prompts zum Erstellen eines Plans pro System aus.",{"claudeCode":925},"thedotmack/claude-mem","Pathfinder","https://github.com/thedotmack/claude-mem",{"basePath":929,"githubOwner":930,"githubRepo":931,"locale":901,"slug":932,"type":247},"plugin/skills/pathfinder","thedotmack","claude-mem","pathfinder",{"evaluate":934,"extract":940},{"promptVersionExtension":204,"promptVersionScoring":205,"score":271,"tags":935,"targetMarket":216,"tier":217},[214,936,937,938,939],"architecture","documentation","refactoring","flowchart",{"commitSha":281,"license":941},"Apache-2.0",{"repoId":943,"translatedFrom":944},"kd70jnxgm695az2wtf37zbqdj986mp7k","k176pxdjxvnyex7jv6abt3myd586n5vv",[936,214,937,939,938],{"evaluatedAt":947,"extractAt":948,"updatedAt":949},1778698228002,1778698056313,1778698405469]