592건 · 관련도순
Ultra-granular code analysis for deep architectural context building. Line-by-line and block-by-block analysis using First Principles, 5 Whys, 5 Hows methodology at micro scale. Builds mental models, tracks invariants and assumptions, and maps cross-function call flows for security audit preparation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Process unhandled Slack messages as Claudicle with persistent memory. Loads user models and soul state, posts terminal-styled thinking messages, generates cognitive-step responses, updates memory based on mentalQuery decisions. Requires Session Bridge listener running.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Build complete userModels for people Claudicle collaborates with. Creates core persona files, social dossiers with voice analysis, content archives, and INDEX registration. Use when asked to create a user model, build a persona, or document someone's voice and identity.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Generate 3D models from text or images via 5 providers: Meshy (text-to-3D, texturing, batch), fal.ai Hunyuan3D Pro ($0.225, PBR), WaveSpeedAI Rapid ($0.0225, bulk), Trellis 2 (poly control), and Local Depth (free offline ONNX depth maps + mesh). Includes GLB viewer. Triggers on 3D model, image to 3D, GLB, depth map, text to 3D, texturing, 3D asset, Meshy, Hunyuan.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
OpenAI GPT Image generation and editing (gpt-image-2, 1.5, 1, mini). Text-to-image, mask-based inpainting, multi-reference composition, multi-turn conversational editing via Responses API, streaming with partial images. This skill should be used when generating or editing images via OpenAI's image models, when near-perfect text rendering in images is needed, when mask-based region-aware editing is required, when multi-turn conversational image editing is desired, or when streaming progressive image delivery is needed. Complements nano-banana-pro (Gemini) as a parallel image generation backend.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Azure Cosmos DB performance optimization and best practices guidelines for NoSQL, partitioning, queries, SDK usage, and vector search. Use when writing, reviewing, or refactoring code that interacts with Azure Cosmos DB, designing data models, optimizing queries, or implementing high-performance database operations.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user wants to generate an image or video via Higgsfield AI. Covers 30+ models: Soul V2, Seedance 2.0, Kling 3.0, Veo 3.1, GPT Image 2, Nano Banana 2. Also covers Marketing Studio — branded ad video/image with avatars and products. Use whenever: "generate an image", "make a video", "animate this photo", "image-to-video", "img2vid", "edit this image with AI", "produce a clip", "create an ad", "make a UGC video", "marketing video", "brand video", "TV spot", "import product from URL", "create avatar for ad", "Seedance", "Kling", "Veo", "Soul gen", "Nano Banana", "Marketing Studio". Chain with higgsfield-soul-id when user wants a reusable face identity. Owns AI-generated "marketing video" / "brand video" / ad creative. NOT for: training a Soul Character (use higgsfield-soul-id), product photoshoots with mode-specific enhancer (use higgsfield-product-photoshoot), text/TTS, screen-recorded product demos (use marketing-demo), slides-to-MP4 assembly (use video-content), Remotion code (use remotion-best-practices). Requires Higgsfield CLI installed and authed.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Create, inspect, analyze, and edit spreadsheets (`.xlsx`, `.xlsm`, `.csv`, `.tsv`) with local code execution. Use for formulas, formatting, charts, validation, financial models, and spreadsheet deliverables.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when loading glTF/GLB models or textures in PlayCanvas, choosing Draco or Basis/KTX2 compression, or setting up decoders and preloading.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Prepare and validate portable CAD, polyline, node-element, clump, and boundary geometry contracts for PFC 5.0 asphalt models without bundling opaque legacy executables.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and audit PFC 5.0 wall servo, loading-rate control, equilibrium checks, micro-to-macro calibration, two-target local solves, DOE campaigns, and independent confirmation for asphalt models.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Extract sensitive training data (PII, API keys, intellectual property, or code) directly from a deployed Large Language Model (LLM). This AI Red Teaming skill focuses on forcing models to regurgitate memorized, unredacted data from their massive internet-scraped datasets through repetition attacks, prefix continuation, and context window manipulation.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Execute sophisticated Data Extraction and Privacy Leakage attacks explicitly against Large Language Models (LLMs) to natively force the neural network entirely into organically regurgitating exact, verbatim strings of Highly Confidential Personally Identifiable Information (PII), proprietary source code, or copyrighted material categorically memorized intrinsically during its foundational pre-training phase.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Check whether the work is actually finished rather than finished-looking — what was promised versus what really runs. Several models compare the task against the code, and every completion claim has to survive a command that was actually executed. Use before calling something done, before a PR, at the end of a session, or on "is this done", "did I finish", "check if done", "what did we skip".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design and implement features from raw data for machine learning models. Use when the user says "engineer features", "feature engineering for X", "create features from this dataset", "transform these variables", "encode categoricals", "handle skew", "create interaction features", "lag features", "extract features from text or dates", "improve model performance with better features", or wants to move from raw columns to model-ready inputs.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Deep learning framework development with tinygrad - a minimal tensor library with autograd, JIT compilation, and multi-device support. Use when writing neural networks, training models, implementing tensor operations, working with UOps/PatternMatcher for graph transformations, or contributing to tinygrad internals. Triggers on tinygrad imports, Tensor operations, nn modules, optimizer usage, schedule/codegen work, or device backends.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Review clinical data models and APIs for HL7 FHIR conformance, terminology standards, interoperability, and clinical workflow correctness. Triggers: 'check FHIR compliance', 'review clinical data model', 'audit HL7 conformance', 'validate medical terminology codes'.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Redact PII in your own code before it reaches Agentforce prompts, models, and logs. Trigger keywords: agentforce pii, pii redaction, data masking llm, prompt pii filter, audit pii leakage. NOT for turning on the platform's own masking, zero-retention and audit-trail controls — use agentforce/einstein-trust-layer. NOT for masking PII in a refreshed sandbox — use security/sandbox-data-masking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Retrospective learning from a completed experience. Takes a project, incident, decision that played out, or time period; gathers ground truth (observations) separately from recollections; spawns reflectors applying different lenses (what-worked-vs-got-lucky, what-didn't, what-surprised, system-rewards-vs-intent, decisions-that-aged, what-to-tell-past-self, patterns-that-recur) in isolation; synthesizes into updated mental models as first-class output. Produces feedback only — no code, no tickets, no artifacts.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Framework for coordinating meaning across autonomous agents that use different conceptual models, vocabularies, and knowledge representations. Treats ontologies as first-class negotiable resources rather than hardcoded implementation details.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Comprehensive regression model diagnostics and assumption checking. Use when validating regression models, checking assumptions (linearity, homoskedasticity, normality, independence), detecting outliers/influence, testing multicollinearity, or when user mentions residuals, heteroskedasticity, VIF, Cook's distance, or diagnostic plots.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Predict RBP binding from RNA sequence using deep learning models (RBPNet sequence-to-signal, RNAProt RNN, GraphProt2 GCN with structure, DeepCLIP, DeepRiPe multi-modal CNN) for variant-effect prediction, in silico binding-site discovery, model interpretation, and transfer learning from CLIP and RBNS datasets. Use when computational prediction of RBP binding from sequence is needed, evaluating variant effects on binding without further wet-lab experiments, comparing model performance, or training a custom model on ENCODE eCLIP data.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Reconstruct ancestral states at internal phylogenetic nodes for sequences (PAML codeml, IQ-TREE --ancestral, GRASP, FastML), discrete traits (corHMM hidden-rate Markov, ape::ace, phytools::make.simmap stochastic mapping, BayesTraits), and continuous traits (phytools::fastAnc, geiger Brownian/OU, RPANDA). Use when designing constructs for ancestral protein resurrection, tracing trait evolution along a tree, performing stochastic character mapping, testing models of trait evolution (BM vs OU vs EB), inferring ancestral genome content via Dollo or DTL reconciliation, or quantifying ancestral-state uncertainty for downstream comparative analyses.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Parallel code verification using multiple models with severity classification
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.