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Found 17 Skills
Create, extract, refine, review, critique, or iterate on the brand identity at `stardust/brand-profile.json` and `stardust/brand-board.html` (and `.impeccable.md`) — philosophy, logo, colors, typography, componentStyle, motifs, photography direction, voice, tone, content pillars, personas, spacing. Ingests guidelines (PDF, URL, or conversation) or iterates on an existing profile. Use when the user provides brand guidelines, when the user asks to change, refine, refactor, review, improve, polish, critique, or iterate on any aspect of brand identity, or whenever the user asks to modify a file at `stardust/brand-profile.json`, `stardust/brand-board.html`, or `.impeccable.md`.
Use when designing and building knowledge graphs from unstructured data. Invoke when user mentions entity extraction, schema design, LPG vs RDF, graph data model, ontology alignment, knowledge graph construction, or building a KG for RAG. Provides extraction pipelines, schema patterns, and data model selection guidance.
Parse ebooks, extract concepts and entities with citation traceability, classify by type/layer, and synthesize across book collections.
CLI for Limitless.ai Pendant with lifelog management, FalkorDBLite semantic graph, vector embeddings, and DAG pipelines. Use for personal memory queries, semantic search across lifelogs/chats/persons/topics, entity extraction, and knowledge graph operations. Triggers include "lifelog", "pendant", "limitless", "personal memory", "semantic search", "graph query", "extraction".
Digital archiving workflows with AI enrichment, entity extraction, and knowledge graph construction. Use when building content archives, implementing AI-powered categorization, extracting entities and relationships, or integrating multiple data sources. Covers patterns from the Jay Rosen Digital Archive project.
Process unstructured external input (meeting transcripts, conversation logs, pasted documents) into structured Basic Memory entities. Extracts entities, searches for existing matches, proposes new entities with approval, creates notes with observations and relations, and captures action items.
Use when extracting entities and relationships, building ontologies, compressing large graphs, or analyzing knowledge structures - provides structural equivalence-based compression achieving 57-95% size reduction, k-bisimulation summarization, categorical quotient constructions, and metagraph hierarchical modeling with scale-invariant properties. Supports recursive refinement through graph topology metrics including |R|/|E| ratios and automorphism analysis.
End-to-end guided workflow for creating a product launch or release announcement video. Extracts brand identity from the repo, generates a cinematic soundtrack, and produces a reveal-driven motion.dev composition rendered via Helios CLI. Use when making launch announcements, release videos, or product reveals.
Implement Named Entity Recognition to identify and classify entities in text. Use this skill when the user needs to extract people, organizations, locations, dates, or custom entities from documents — even if they say 'extract names from text', 'find companies mentioned', or 'entity extraction'.
Always-on ambient signal capture. Fires on every inbound message to detect original thinking and entity mentions. Spawn as a cheap sub-agent in parallel, never block the main response.
Knowledge graph memory orchestration - entity extraction, query parsing, deduplication, and cross-reference boosting. Use when designing memory orchestration.
Text analytics using LLM APIs — sentiment analysis, customer feedback classification, document entity extraction, multi-language support (English/Luganda/Swahili), feedback aggregation, and NLP feature implementation for PHP/Android/iOS. Sources...