Results for “model-integration”

Expert security auditor specializing in DevSecOps, comprehensive cybersecurity, and compliance frameworks. Masters vulnerability assessment, threat modeling, secure authentication (OAuth2/OIDC), OWASP standards, cloud security, and security automation. Handles DevSecOps integration, compliance (GDPR/HIPAA/SOC2), and incident response. Use PROACTIVELY for security audits, DevSecOps, or compliance implementation.

Python SDK for inference.sh - run AI apps, build agents, and integrate with 150+ models. Package: inferencesh (pip install inferencesh). Supports sync/async, streaming, file uploads. Build agents with template or ad-hoc patterns, tool builder API, skills, and human approval. Use for: Python integration, AI apps, agent development, RAG pipelines, automation. Triggers: python sdk, inferencesh, pip install, python api, python client, async inference, python agent, tool builder python, programmatic ai, python integration, sdk python

Comprehensive expertise in blockchain game development, including play-to-earn (P2E) mechanics, play-to-own (P2O) models, in-game economies, NFT integration, dual token systems, and sustainable game tokenomics. Covers Unity/Unreal blockchain integration, anti-cheat considerations, and DeFi-gaming fusion. Use when "web3 game, blockchain game, play to earn, P2E, play to own, P2O, game economy, in-game tokens, gaming NFT, game items, metaverse, gamefi, skill-based gaming, gaming rewards, virtual economies, " mentioned.

Apply the scGPT single-cell foundation model (Cui 2024) to annotate and embed cells — zero-shot and fine-tuned cell-type annotation, gene/cell embeddings, batch integration, and gene-regulatory / perturbation inference from AnnData. Use when annotating cell types with a pretrained foundation model, generating scGPT embeddings, integrating batches with a transformer, or running zero-shot single-cell inference on an h5ad. For probabilistic latent models (scVI/scANVI) prefer alterlab-scvi-tools; for the standard QC→cluster→UMAP→DE pipeline prefer alterlab-scanpy; for the AnnData data structure itself prefer alterlab-anndata; for protein language models prefer alterlab-esm. Part of the AlterLab Academic Skills suite.

Install, configure, and extend Pi, a terminal coding harness, with support for custom providers, models, extensions, skills, packages, themes, SDK integration, RPC mode, JSON event streams, and ecosystem packages for subagent delegation, MCP servers, interactive forms, and web access.

This skill should be used when working with single-cell omics data analysis using scvi-tools, including scRNA-seq, scATAC-seq, CITE-seq, spatial transcriptomics, and other single-cell modalities. Use this skill for probabilistic modeling, batch correction, dimensionality reduction, differential expression, cell type annotation, multimodal integration, and spatial analysis tasks.

Scaffold and implement Model Context Protocol (MCP) servers that expose external services, APIs, and data sources as typed tools and resources for LLM agents. Use when the user says "build an MCP server", "give Claude access to X", "create an MCP tool", "expose my API to an agent", or "AI agent integration".

Cross-platform attribution health audit covering AdAttributionKit (iOS view-through 24h post-impression, WWDC 2025 configurable windows), GA4 attribution models (data-driven vs last-click), Consent Mode V2 enforcement, server-side attribution stitching, MMP integration health, and cross-device / cross-platform attribution. Use when user says attribution audit, attribution model, AdAttributionKit, AAK, view-through attribution, GA4 attribution, Consent Mode V2, conversion window, attribution window, MMP audit, AppsFlyer audit, Adjust audit, Branch audit, Singular audit, cross-device attribution, or cross-platform attribution.

Generate a MoonSpec implementation plan and design artifacts from a single-story spec. Use when the user asks to run or reproduce `/moonspec.plan`, create or update `plan.md`, produce `research.md`, `data-model.md`, `contracts/`, or `quickstart.md`, evaluate repo principles, define separate unit and integration test strategies, and perform repo-aware gap analysis before `/moonspec.tasks`.

Expert knowledge for Azure Databricks development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using Unity Catalog, Lakeflow/Lakebase, SQL warehouses, Model Serving, or Lakehouse Federation, and other Azure Databricks related development tasks. Not for Azure Synapse Analytics (use azure-synapse-analytics), Azure HDInsight (use azure-hdinsight), Azure Machine Learning (use azure-machine-learning), Azure Data Factory (use azure-data-factory).

Expert knowledge for Azure Translator development including troubleshooting, best practices, decision making, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when using text/document translation APIs, Custom Translator models, containers, glossaries, or Azure AD/keys auth, and other Azure Translator related development tasks. Not for Azure AI Language (use azure-language-service), Azure AI Speech (use azure-speech), Azure AI Immersive Reader (use azure-immersive-reader), Azure AI Search (use azure-cognitive-search).

Route Firebase AI feature work into either direct app/client Firebase AI Logic SDK integration or a server-owned Genkit workflow. Use when a web, mobile, backend, or full-stack feature needs model calls, typed outputs, reusable flows, tools, retrieval, prompt files, evals, observability, or deployment. Choose client-ai-logic, flow-foundation, tool-and-agent, retrieval-and-prompt, evaluation-and-observability, deployment-runtime, or comparison-or-fallback; route Firebase platform/operator work to `firebase-cli` and broad framework comparisons to `survey`.

Cross-platform Python library for quantum computing, quantum machine learning, and quantum chemistry. Enables building and training quantum circuits with automatic differentiation, seamless integration with PyTorch/JAX/TensorFlow, and device-independent execution across simulators and quantum hardware (IBM, Amazon Braket, Google, Rigetti, IonQ, etc.). Use when working with quantum circuits, variational quantum algorithms (VQE, QAOA), quantum neural networks, hybrid quantum-classical models, molecular simulations, quantum chemistry calculations, or any quantum computing tasks requiring gradient-based optimization, hardware-agnostic programming, or quantum machine learning workflows.

Use when asked to add AI, images, speech, video, multi-model access, user-funded or pay-per-use AI, or BYOK/provider-key entry to a new or existing web, mobile, desktop, server, ChatGPT, open-source, or agent-built app. Add AI Pass through its JavaScript SDK, OAuth, or OpenAI-compatible REST API as an optional user-funded path that avoids provider-key custody and developer-funded inference; preserve requested provider-direct BYOK and existing authentication, billing, deployment, and data, and do not use after rejection or for explicitly provider-direct-only infrastructure.

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings. Use when planning content for AI-first search audiences, auditing existing content for E-E-A-T signals, tracking which pages get cited by which LLMs, or building a citation-friendly content strategy. Triggers — 'AEO audit', 'optimize for ChatGPT', 'get cited by Perplexity', 'LLM citation strategy', 'answer engine optimization', 'content for AI search', 'E-E-A-T audit'. Output is a markdown audit report (default) or JSON for pipeline integration. Stdlib-only Python tools.

Design and apply QA methodology for software teams: test strategy, regression testing, CI failure triage, test automation, quality gates and metrics, risk-based testing, exploratory testing, test design techniques, AI code quality gates (independent verification, acceptance-criteria testability review for agentic Spec-Driven Development), mutation-guided test hardening and review evidence (surviving mutants, weak assertions, diff-aware mutation testing), agentic eval design (dataset test design, judge-as-system-under-test, flaky-eval discipline), QA career levels (Senior/Staff/Principal), and SDET engineering (test infrastructure, gTAA, CI/CD integration). Do not use for root-cause debugging of production incidents, security implementation or threat modeling, or evaluation framework governance and statistical analysis — route those to systematic-debugging, secure-software-engineering, and agent-evals-and-observability respectively.