Results for “code models”

Odoo 18 development reference for Python models and ORM (search, domain, read_group, compute fields), XML/CSV data and views, OWL/JS client code, QWeb reports, security (ACL, record rules, groups), cron and server actions, migrations and module upgrades, tests, i18n, and performance. Use this skill whenever work involves Odoo 18 or custom addons, including traceback diagnosis, addons, manifests, XML errors, HTTP controllers, business rules, feature work, refactoring, or reviews.

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Use Kiln to create eval datasets, rate outputs, optimize prompts and models, and ship the same AI task or agent definition from a local workbench into production code.

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Author, compile, inspect, and revise WAM low-poly 3D characters, creatures, props, rigs, and animations with ChatGPT Codex. Use for WAM or .wam requests, WoW-style or game-ready models, and one or more reference images or requested render views.

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generate, refine, validate, and render diagrams from natural language, notes, code snippets, schemas, tables, or existing diagram source. use for flowcharts, swimlanes, sequence diagrams, state diagrams, er diagrams, class diagrams, architecture/c4-style diagrams, dependency graphs, gantt charts, mind maps, user journeys, sankey-style flows, org charts, network graphs, and other visual models. supports mermaid by default, graphviz dot for complex graph layout, plantuml for uml-heavy engineering diagrams, and svg output when direct markup is more reliable.

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Use for visual explanations and documents. Triggers: diagrams, charts, interactive models, HTML reports, infographics, 시각화, 그려줘, 문서 만들어줘, 보고서, PDF 생성.

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Analyze pre-trained generative diffusion models (Stable Diffusion, DALL-E, Flux) by computing quality metrics (FID, IS, CLIP score, precision/recall), inspecting noise schedules, extracting and visualizing attention maps, and probing latent spaces. Use when evaluating a pre-trained generative diffusion model's output quality, comparing noise schedule variants, analyzing cross-attention patterns for text-conditioned generation, interpolating between latent codes, or detecting out-of-distribution inputs.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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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.

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Use when loading glTF/GLB models or textures in PlayCanvas, choosing Draco or Basis/KTX2 compression, or setting up decoders and preloading.

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Prepare and validate portable CAD, polyline, node-element, clump, and boundary geometry contracts for PFC 5.0 asphalt models without bundling opaque legacy executables.

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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.

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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.

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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.

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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".

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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.

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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.

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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'.

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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.

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