{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"npm-wicked-brain","slug":"wicked-brain","name":"wicked-brain","type":"repo","url":"https://github.com/mikeparcewski/wicked-brain","page_url":"https://unfragile.ai/wicked-brain","categories":["automation","rag-knowledge"],"tags":["ai","brain","knowledge-base","skills","claude-code","gemini-cli","copilot","cursor","codex","markdown","sqlite","fts5","rag-alternative"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"npm-wicked-brain__cap_0","uri":"capability://memory.knowledge.markdown.based.skill.indexing.with.full.text.search","name":"markdown-based skill indexing with full-text search","description":"Indexes markdown files containing code skills and knowledge into a local SQLite database with FTS5 (Full-Text Search 5) enabled, enabling semantic keyword matching without vector embeddings or external infrastructure. The system parses markdown structure (headings, code blocks, metadata) and builds inverted indices for fast retrieval of skill documentation by natural language queries. No external vector DB or embedding service required — all indexing and search happens locally.","intents":["I want to build a personal knowledge base of coding patterns without managing a vector database","I need to quickly search through my documented skills and code snippets using natural language","I want to integrate my skill library directly into AI CLI tools like Claude Code or Cursor without API overhead"],"best_for":["solo developers building LLM-powered CLI agents","teams using Claude Code, Cursor, or Gemini CLI who want local knowledge augmentation","developers avoiding vector DB infrastructure (Pinecone, Weaviate, Milvus) for simplicity"],"limitations":["FTS5 keyword matching lacks semantic understanding — synonyms and conceptual similarity not captured without manual tagging","No ranking by relevance confidence — results are binary match/no-match rather than scored by relevance","Markdown parsing is basic — complex nested structures or non-standard formatting may not index correctly","Single-machine SQLite — no distributed indexing or multi-user concurrent writes without locking"],"requires":["Node.js 14+","SQLite 3.8+ (FTS5 support)","Markdown files formatted with consistent structure (headings, code blocks)"],"input_types":["markdown files","plain text queries","code snippets embedded in markdown"],"output_types":["matched markdown sections","code blocks","structured skill metadata"],"categories":["memory-knowledge","search-retrieval"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_1","uri":"capability://memory.knowledge.ai.cli.skill.injection.via.context.augmentation","name":"ai cli skill injection via context augmentation","description":"Retrieves indexed skills from the local SQLite database and injects them into the context window of AI coding CLIs (Claude Code, Cursor, Gemini CLI, GitHub Copilot) as formatted markdown or structured prompts. The system acts as a middleware layer that intercepts queries, searches the skill index, and prepends relevant documentation to the AI's input context before sending to the LLM. Supports multiple CLI integrations through adapter patterns.","intents":["I want my AI coding assistant to know about my custom coding patterns and best practices without retraining","I need to automatically inject relevant documentation into Claude Code or Cursor before each AI request","I want to augment Gemini CLI with my team's skill library without modifying the CLI itself"],"best_for":["developers using Claude Code, Cursor, or Gemini CLI who want persistent skill context","teams standardizing on specific coding patterns and wanting AI assistants to follow them","solo developers building custom LLM agents that need skill augmentation"],"limitations":["Context window limited by the underlying CLI/LLM — injecting too many skills may exceed token limits","No automatic skill relevance ranking — all matching skills injected equally, potentially diluting signal","Requires manual skill curation and markdown formatting — no automatic extraction from existing codebases","CLI integration is adapter-based — each new CLI tool requires custom integration code"],"requires":["Node.js 14+","Installed AI CLI tool (Claude Code, Cursor, Gemini CLI, or compatible)","Pre-indexed skill database from markdown files"],"input_types":["natural language query from CLI","code context from editor"],"output_types":["augmented prompt with injected skills","formatted markdown documentation","code examples from skill library"],"categories":["memory-knowledge","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_10","uri":"capability://automation.workflow.cli.command.interface.for.skill.management","name":"cli command interface for skill management","description":"Provides a command-line interface for managing the skill library (add, remove, search, list, export) without requiring programmatic API calls. Commands include `wicked-brain add <file>`, `wicked-brain search <query>`, `wicked-brain list`, `wicked-brain export`, enabling developers to manage skills from the terminal. Supports piping and scripting for automation.","intents":["I want to add and search skills from the command line without writing code","I need to integrate skill management into shell scripts or CI/CD pipelines","I want to list and manage my skills using familiar CLI tools"],"best_for":["developers comfortable with CLI tools and shell scripting","teams integrating skill management into CI/CD workflows","solo developers wanting quick command-line access to skills"],"limitations":["CLI output format is fixed — no customizable output templates","Complex queries require understanding FTS5 syntax — natural language queries may not work","No interactive CLI mode — each command requires a separate invocation","Error messages may be cryptic for non-technical users"],"requires":["Node.js 14+","npm or yarn for installation","Terminal/shell access"],"input_types":["CLI arguments","markdown files","text queries"],"output_types":["CLI output (text, JSON)","skill records","search results"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_2","uri":"capability://memory.knowledge.skill.library.management.with.markdown.versioning","name":"skill library management with markdown versioning","description":"Provides a structured system for organizing, storing, and versioning coding skills as markdown files with optional metadata (tags, difficulty, language, category). Skills are stored in a flat or hierarchical directory structure and can be edited directly in any text editor. The system tracks which skills are indexed and provides utilities to add, update, and remove skills from the index without requiring a database UI or special tooling.","intents":["I want to organize my coding patterns and best practices in a version-controllable format","I need to add new skills to my knowledge base and have them immediately available to my AI CLI","I want to maintain my skill library in Git alongside my code without proprietary tooling"],"best_for":["developers who prefer plaintext/markdown over database UIs","teams using Git for version control and wanting skills as code","solo developers building personal knowledge bases"],"limitations":["No built-in conflict resolution for concurrent skill edits — Git merge conflicts possible","Metadata extraction relies on markdown frontmatter or comment conventions — no schema validation","No access control — all skills in the library are equally accessible to all CLI integrations","Manual skill organization required — no automatic categorization or tagging"],"requires":["Node.js 14+","Text editor (any editor works — no special IDE required)","Optional: Git for version control"],"input_types":["markdown files","YAML frontmatter (optional)","code blocks"],"output_types":["indexed skill records","skill metadata","Git-compatible file structure"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_3","uri":"capability://memory.knowledge.zero.infrastructure.local.knowledge.retrieval","name":"zero-infrastructure local knowledge retrieval","description":"Executes all knowledge indexing and retrieval operations locally on the developer's machine using SQLite FTS5, eliminating the need for external services, API keys, or cloud infrastructure. The entire skill database is stored as a single SQLite file that can be backed up, versioned, or shared via Git. No network calls, no rate limits, no vendor lock-in — all operations complete in milliseconds on local hardware.","intents":["I want a knowledge base system that works offline and doesn't require API keys or cloud services","I need to ensure my skill library stays private and under my control without uploading to external services","I want instant search results without network latency or rate limiting"],"best_for":["developers working offline or in restricted network environments","teams with data privacy requirements preventing cloud storage","solo developers wanting minimal operational overhead"],"limitations":["Single-machine deployment — no built-in replication or distributed search","SQLite write locking can cause contention under high concurrent load","No automatic backups — developer responsible for database file backups","Limited to local hardware resources — very large skill libraries (100k+ documents) may slow search"],"requires":["Node.js 14+","SQLite 3.8+ with FTS5 support","Local disk space for SQLite database file"],"input_types":["markdown files","text queries"],"output_types":["search results","skill metadata","SQLite database file"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_4","uri":"capability://tool.use.integration.multi.cli.adapter.framework.for.skill.injection","name":"multi-cli adapter framework for skill injection","description":"Provides an extensible adapter pattern for integrating the skill library with multiple AI coding CLIs through standardized interfaces. Each CLI adapter handles the specific protocol, context format, and API of its target tool (Claude Code's prompt format, Cursor's context injection, Gemini CLI's request structure). New adapters can be added by implementing a simple interface without modifying core indexing logic.","intents":["I want to use the same skill library across multiple AI coding tools (Claude Code, Cursor, Gemini CLI)","I need to add support for a new AI CLI tool without forking the entire project","I want to standardize my team's skill library across different developers' preferred AI tools"],"best_for":["developers using multiple AI coding CLIs and wanting unified skill context","teams with heterogeneous tool preferences (some use Cursor, some use Claude Code)","contributors wanting to extend wicked-brain with new CLI integrations"],"limitations":["Each CLI adapter must be manually implemented — no automatic protocol detection","CLI API changes require adapter updates — breaking changes in CLI tools may break integrations","No built-in fallback if a CLI adapter fails — errors propagate to the user","Context injection format varies per CLI — skills may render differently across tools"],"requires":["Node.js 14+","Target CLI tool installed and configured","Understanding of target CLI's context/prompt API"],"input_types":["skill library metadata","CLI-specific context format"],"output_types":["CLI-formatted prompts","injected context","adapter implementations"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_5","uri":"capability://search.retrieval.query.to.skill.matching.via.fts5.keyword.expansion","name":"query-to-skill matching via fts5 keyword expansion","description":"Converts natural language queries into FTS5 search expressions by tokenizing, normalizing, and optionally expanding queries with synonyms or related terms. The system handles common query patterns (e.g., 'how do I X' → search for skill tags matching X) and applies FTS5 operators (AND, OR, phrase matching) to improve precision. No machine learning or semantic models — purely lexical matching with heuristic query expansion.","intents":["I want to search my skill library using natural language without learning FTS5 syntax","I need search results that understand common query variations (e.g., 'async patterns' vs 'promises')","I want to improve search precision by automatically expanding queries with related terms"],"best_for":["developers using wicked-brain CLI for interactive skill searches","teams wanting natural language search without semantic models","solo developers building custom query interfaces"],"limitations":["Synonym expansion requires manual configuration — no automatic synonym discovery","Query expansion heuristics are rule-based — edge cases and ambiguous queries may not expand correctly","No ranking by relevance — all matching skills returned equally","Typos and misspellings not handled — FTS5 has no fuzzy matching by default"],"requires":["Node.js 14+","SQLite 3.8+ with FTS5 support","Pre-indexed skill database"],"input_types":["natural language text query","optional query expansion configuration"],"output_types":["FTS5 search expressions","matched skill records","search result rankings"],"categories":["search-retrieval","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_6","uri":"capability://data.processing.analysis.skill.metadata.extraction.and.tagging","name":"skill metadata extraction and tagging","description":"Parses markdown skill files to extract structured metadata (title, description, tags, language, difficulty, category) from frontmatter (YAML/TOML) or markdown conventions (heading levels, code fence language tags). Metadata is indexed alongside skill content, enabling filtered searches (e.g., 'find all Python skills tagged with async'). Supports custom metadata fields through configuration.","intents":["I want to organize my skills by language, difficulty, and category for easier discovery","I need to filter skills by metadata (e.g., show only TypeScript patterns) when searching","I want to add custom metadata fields to my skills without modifying the indexing system"],"best_for":["developers with large skill libraries (50+ skills) needing organization","teams standardizing skill metadata across contributors","solo developers building searchable skill catalogs"],"limitations":["Metadata parsing relies on consistent markdown formatting — inconsistent files may lose metadata","No schema validation — invalid metadata silently ignored or causes indexing errors","Custom metadata fields require configuration changes — no dynamic field discovery","Metadata updates require re-indexing — no incremental metadata updates"],"requires":["Node.js 14+","Markdown files with YAML/TOML frontmatter or markdown conventions","Optional: metadata configuration file"],"input_types":["markdown files with frontmatter","code fence language tags","markdown heading structure"],"output_types":["structured metadata objects","indexed metadata fields","filtered search results"],"categories":["data-processing-analysis","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_7","uri":"capability://automation.workflow.batch.skill.indexing.and.incremental.updates","name":"batch skill indexing and incremental updates","description":"Indexes multiple markdown files in batch operations, computing file hashes to detect changes and only re-indexing modified files. The system maintains an index state file tracking which skills have been indexed and their modification timestamps. Supports full re-indexing and incremental updates, enabling fast skill library updates without rebuilding the entire index.","intents":["I want to quickly add new skills to my library without re-indexing everything","I need to detect which skills have changed since the last index update","I want to rebuild my skill index efficiently when skills are added, removed, or modified"],"best_for":["developers with growing skill libraries (100+ skills) where full re-indexing is slow","teams with CI/CD pipelines that need fast skill index updates","solo developers wanting responsive skill library updates"],"limitations":["File hash comparison only detects content changes — metadata-only changes may not trigger re-indexing","Index state file can become stale if skills are deleted outside the system","No distributed indexing — batch operations run sequentially on a single machine","Incremental updates assume files are only added/modified, not moved or renamed"],"requires":["Node.js 14+","SQLite 3.8+ with FTS5 support","Markdown skill files"],"input_types":["markdown file paths","directory paths for batch indexing"],"output_types":["updated SQLite index","index state file","indexing statistics"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_8","uri":"capability://memory.knowledge.skill.context.window.optimization.for.llm.injection","name":"skill context window optimization for llm injection","description":"Selects and ranks the most relevant skills from search results to fit within the LLM's context window limit, using heuristics like skill size, relevance score, and recency. The system truncates or summarizes skills that exceed token budgets and prioritizes high-relevance matches. Supports configurable context window sizes for different LLMs (Claude 3, GPT-4, Gemini) and provides fallback strategies when skills don't fit.","intents":["I want to inject the most relevant skills into my AI CLI without exceeding the context window","I need to automatically prioritize high-relevance skills when space is limited","I want to configure context limits for different LLMs (Claude vs GPT-4 vs Gemini)"],"best_for":["developers using wicked-brain with LLMs that have strict context limits","teams wanting to maximize skill relevance within fixed token budgets","solo developers building custom LLM agents with context constraints"],"limitations":["Token counting is approximate — actual token usage may vary by tokenizer","Skill truncation may lose important context — no intelligent summarization","Relevance ranking is heuristic-based — no learned ranking model","Context window limits must be manually configured per LLM — no automatic detection"],"requires":["Node.js 14+","Target LLM's context window size","Optional: token counting library (e.g., js-tiktoken for OpenAI models)"],"input_types":["search results (skill records)","context window size limit","LLM configuration"],"output_types":["ranked and truncated skills","context window usage statistics","fallback skill selections"],"categories":["memory-knowledge","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"npm-wicked-brain__cap_9","uri":"capability://automation.workflow.skill.library.export.and.sharing","name":"skill library export and sharing","description":"Exports the indexed skill library in multiple formats (JSON, CSV, markdown index) for sharing, backup, or integration with other tools. Supports selective exports (e.g., export only Python skills) and includes metadata, search indices, and skill content. Exported libraries can be re-imported into other wicked-brain instances or used as standalone documentation.","intents":["I want to back up my skill library in a portable format","I need to share my skills with teammates who use different tools","I want to export my skills as documentation or a searchable index"],"best_for":["developers wanting portable skill library backups","teams sharing skills across different tools and workflows","solo developers building public skill catalogs"],"limitations":["Export formats may lose metadata or search indices — re-imported libraries may not be fully functional","Large exports (100k+ skills) can be slow and produce large files","No built-in compression — exported files can be large without manual compression","Selective exports require filter configuration — no UI for choosing export criteria"],"requires":["Node.js 14+","SQLite database with indexed skills"],"input_types":["SQLite skill database","export format specification","optional filter criteria"],"output_types":["JSON export","CSV export","markdown index","backup files"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":33,"verified":false,"data_access_risk":"high","permissions":["Node.js 14+","SQLite 3.8+ (FTS5 support)","Markdown files formatted with consistent structure (headings, code blocks)","Installed AI CLI tool (Claude Code, Cursor, Gemini CLI, or compatible)","Pre-indexed skill database from markdown files","npm or yarn for installation","Terminal/shell access","Text editor (any editor works — no special IDE required)","Optional: Git for version control","SQLite 3.8+ with FTS5 support"],"failure_modes":["FTS5 keyword matching lacks semantic understanding — synonyms and conceptual similarity not captured without manual tagging","No ranking by relevance confidence — results are binary match/no-match rather than scored by relevance","Markdown parsing is basic — complex nested structures or non-standard formatting may not index correctly","Single-machine SQLite — no distributed indexing or multi-user concurrent writes without locking","Context window limited by the underlying CLI/LLM — injecting too many skills may exceed token limits","No automatic skill relevance ranking — all matching skills injected equally, potentially diluting signal","Requires manual skill curation and markdown formatting — no automatic extraction from existing codebases","CLI integration is adapter-based — each new CLI tool requires custom integration code","CLI output format is fixed — no customizable output templates","Complex queries require understanding FTS5 syntax — natural language queries may not work","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.14776999283321307,"quality":0.32,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.902Z","last_scraped_at":"2026-04-22T08:08:13.653Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":1502,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=wicked-brain","compare_url":"https://unfragile.ai/compare?artifact=wicked-brain"}},"signature":"rY2e9NQOEMxvyb1q1q9EjQO7piK6+U6PLm9dmlJ0RCg8q0xnWeOffSVpO7iddNOl1q+BEW12xrJATcoOig3HAA==","signedAt":"2026-06-21T05:07:54.633Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/wicked-brain","artifact":"https://unfragile.ai/wicked-brain","verify":"https://unfragile.ai/api/v1/verify?slug=wicked-brain","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}