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Found 84 Skills
You must use this when merging findings from multiple studies into a coherent narrative with grounded evidence.
PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank, pg_trgm, trigram, fuzzy search, LIKE, ILIKE, autocomplete, typo tolerance, fuzzystrmatch
Use when developing business strategy (market entry, product launch, geographic expansion, M&A, turnaround), conducting competitive analysis (profiling competitors, assessing competitive threats, Porter's 5 Forces, identifying differentiation), applying strategic frameworks (Good Strategy kernel with diagnosis/guiding policy/coherent actions, SWOT, Blue Ocean Strategy, Playing to Win where-to-play/how-to-win, Value Chain Analysis, BCG Matrix), making strategic decisions under constraints (build vs buy, pricing strategy, market positioning, business model choices), planning strategic initiatives (annual planning, OKRs, roadmaps), evaluating competitive positioning (moats, sustainable advantages, differentiation vs cost leadership), or when user mentions "strategy", "competitive analysis", "Porter's 5 Forces", "SWOT", "market positioning", "strategic planning", "competitive landscape", or "strategic frameworks".
SmartACE (Agentic Context Engineering) workflow engine with MCP-B (Master Client Bridge) and AMUM-QCI-ETHIC module. Dual database architecture using DuckDB (analytics) + SurrealDB (graph). Uses Blender 5.0 (bpy) and UE5 Remote Control. Use when (1) MCP-B agent-to-agent communication (INQC protocol), (2) AMUM 3→6→9 progressive alignment, (3) QCI quantum coherence states, (4) ETHIC principles enforcement (Marcel/Anthropic/EU AI Act), (5) SurrealDB graph relationships, (6) DuckDB SQL workflows, (7) ML inference with infera/vss, (8) Blender 5.0 headless processing, (9) UE5 scene control, (10) DuckLake time travel.
Senior embedded software engineer specializing in firmware and driver development for ARM Cortex-M microcontrollers (Teensy, STM32, nRF52, SAMD). Decades of experience writing reliable, optimized, and maintainable embedded code with deep expertise in memory barriers, DMA/cache coherency, interrupt-driven I/O, and peripheral drivers.
Critical analysis of research papers, academic manuscripts, preprints, and technical studies — evaluating methodology, claims-evidence alignment, contribution significance, and intellectual honesty. Produces coherent analytical responses (not checklists) that distinguish genuine weaknesses from standard field limitations. Governs intellectual posture: collegial reader, not adversarial reviewer. Triggers on: "critique this paper", "review this research", "what do you think of this paper", "analyze this study", "evaluate the methodology", "is this paper sound", "assess this research", "strengths and weaknesses of this paper", "does the evidence support the claims". Use this skill when the user provides a research paper, preprint, or technical study and asks for critical evaluation of its scientific merit, methodology, or contribution — not formatting, citation hygiene, or submission readiness (use manuscript-review for those).
Strategy for creating efficient short-form video prompts. Use when creating filler shots, atmospheric scenes, or quick video clips that don't require full Production Brief methodology. Covers when to go short vs long, format+style upfront rule, and two approaches (Descriptive vs Directive) for compact yet coherent results.
Research and compile the latest AI news from across the industry. Use this skill when asked to find AI news, get AI updates, research what's happening in AI, check for AI announcements, or gather intelligence on AI companies. Triggers include requests for "AI news", "latest AI developments", "what's new in AI", "AI industry updates", or news about specific AI companies (OpenAI, Anthropic, Google, Microsoft, Meta, Amazon, Nvidia, xAI, Mistral, Cohere, Apple, Salesforce).
Eight-axis judgment code review for the current diff — Correctness, Simplification, Tests, Documentation, Style, Intent, Design/API, Performance (+ Coherence on metadata changes). Five-phase pipeline scope → deterministic tool battery (npx/uvx-preferred, zero-install for the JS + Python majority) → 8 parallel LLM axis reviewers → Haiku validators on sub-80 findings (verbatim rubric, ≥80 threshold) → synthesis with no-silent-drop + Conventional Comments JSONL. Every report closes with "What I did NOT check" (security → /security-review, runtime perf, flaky detection). Opt-in flags `--verify-build`, `--mutation-test`, `--reconcile`, `--apply-safe`. Public-skill posture — zero auto-install, graceful skip on missing native tools.
Clean and reconstruct raw auto-generated captions (Zoom, YouTube, Teams, Google Meet, Otter.ai, etc.) into readable, coherent transcripts. Use when the user provides raw caption files (.txt, .vtt, .srt), meeting transcripts with timestamps and speaker tags, or asks to clean up/refine a transcript. Handles: timestamp removal, speaker tag normalization, filler word removal, broken sentence reconstruction, transcription error correction, paragraph formation. Preserves every piece of substantive content while removing noise. Trigger phrases: 'clean this transcript', 'refine captions', 'fix this transcript', 'process Zoom captions', 'clean up meeting notes'.
Typst Academic Paper Assistant (supports Chinese and English papers, conference/journal submissions). Domains: Deep Learning, Time Series, Industrial Control, Computer Science. Trigger Words (any module can be called independently): - "compile", "compile", "typst compile" → Compilation Module - "format", "format check", "lint" → Format Check Module - "grammar", "grammar", "proofread", "polish" → Grammar Analysis Module - "long sentence", "long sentence", "simplify", "decompose" → Complex Sentence Analysis Module - "academic tone", "academic expression", "improve writing" → Academic Expression Module - "logic", "coherence", "logic", "cohesion", "methodology", "methodology" → Logical Cohesion & Methodology Depth Module - "translate", "translate", "Chinese to English" → Translation Module - "bib", "bibliography", "bibliography" → Bibliography Module - "deai", "de-AI", "humanize", "reduce AI traces" → De-AI Editing Module - "title", "title", "title optimization", "create title" → Title Optimization Module - "template", "template", "IEEE", "ACM" → Template Configuration Module
LLM-as-judge evaluation framework with 5-dimension rubric (accuracy, groundedness, coherence, completeness, helpfulness) for scoring AI-generated content quality with weighted composite scores and evidence citations