'llm implementation' 검색 결과

Reviews code for security vulnerabilities and guides secure implementation using OWASP Top 10:2025, ASVS 5.0, the OWASP Top 10 for LLM Applications (2026), and the OWASP Top 10 for Agentic Applications (2026). Use when reviewing code or a diff for security issues, implementing authentication, authorization, sessions, or cryptography, handling untrusted input, files, or URLs, hardening config, dependencies, or CI, or building LLM and AI agent features.

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UE (user experience engineering) skill for defining end-to-end user experience behavior and interaction logic. Use for tasks like flow design, interaction models, state machines, error recovery, input validation, usability heuristics, accessibility, and turning UX intent into testable acceptance criteria for implementation.

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Architecture-first UI/UX design skill for Codex that helps agents reason from product truth, user journeys, causal design decisions, art direction, interaction, responsive behavior, imagery, iconography, and motion before implementation, then validate the result through rendered browser critique.

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Propose a new change with all artifacts generated in one step. Use when the user wants to quickly describe what they want to build and get a complete proposal with design, specs, and tasks ready for implementation.

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Transforms a rough idea into a detailed design document with implementation plan. Follows Prompt-Driven Development — iterative requirements clarification, research, design, and planning.

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Dual-mode UX quality skill — runs a heuristic/accessibility/motion audit on the current codebase, or validates implementation against design mockups. Fixes all issues found and commits.

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Create QA checklists for verifying design implementation accuracy.

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Translate a vague request or GitHub issue into a concrete, testable spec using five structured elements (problem, scope, acceptance criteria, edge cases, success criterion) before writing any code. Prevents scope creep, PR churn, and mid-implementation pivots.

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Provides reverse engineering techniques. Use when the main job is to understand how a compiled, obfuscated, packed, or virtualized target works before exploiting or solving it, including binaries, APKs, WASM, firmware, custom VMs, bytecode, malware-like loaders, and anti-debug or anti-analysis logic. Do not use it when the vulnerability is already understood and the remaining task is exploitation; use pwn instead. Do not use it for pure web workflows, log or disk forensics, or standalone crypto problems unless reversing the implementation is the real blocker.

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AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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AI/ML Workflow Bundle workflow skill. Use this skill when the user needs AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Use when comparing two or more code implementations against a spec or requirements doc. Triggers on "which repo is better", "compare these implementations", "evaluate both solutions", "rank these codebases", or "judge which approach wins". Also covers choosing between competing PRs or vendor submissions solving the same problem. Does NOT review a single codebase for quality — use code review skills instead. Does NOT evaluate strategy docs — use strategy-review. Requires a spec file and 2+ repo paths.

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Map user journeys, onboarding paths, entry points, first actions, trust points, accessibility risks, and SEO/LLM handoffs for public websites without redesigning the visual system. Use this skill when the user asks how new readers, returning users, project reviewers, community members, buyers, or AI agents should move through a site and what each page must make clear before layout implementation.

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Write a durable feature spec before touching code. Reads the codebase map, reads related models/migrations to ask technical questions (not generic ones), then produces a structured spec doc in docs/specs/. Designed to pipeline with /implementation --issue N. Use before any feature implementation.

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Fast spec-driven implementation from a committed spec. Use after /spec to implement an issue with a phase roadmap, TDD waves, map patching, review, and pre-commit validation.

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Creates detailed, sectionized implementation plans through research, stakeholder interviews, and multi-LLM review. Use when planning features that need thorough pre-implementation analysis.

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METAINFORMANT rules for directory src/metainformant/ml/llm. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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METAINFORMANT rules for directory src/metainformant/ml/llm/ollama. Use when editing, adding tests, or reviewing code under this path. Read the linked AGENTS.md first; use uv only, write outputs to output/, real implementations.

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AI and machine learning workflow covering LLM application development, RAG implementation, agent architecture, ML pipelines, and AI-powered features.

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Build Retrieval-Augmented Generation (RAG) systems for LLM applications with vector databases and semantic search. Use when implementing knowledge-grounded AI, building document Q&A systems, or integrating LLMs with external knowledge bases.

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미국 의료 소프트웨어의 HIPAA 대응을 검토할 때 환자 정보 처리, 외부 서비스 전송 조건, 최소한의 접근 범위와 감사 기록을 확인하는 스킬입니다.

• AI 진료 요약의 HIPAA 대응을 확인합니다
• 환자 정보의 분석 플랫폼 전송을 검토합니다
• 최소 접근 범위와 감사 기록을 설계합니다

Unsloth로 언어 모델을 추가 학습하도록 돕습니다. LoRA와 QLoRA 구현, 기능과 API 확인, 코드 오류 조사, GPU 메모리를 줄이는 학습 방법 탐색을 지원합니다.

• LoRA와 QLoRA 추가 학습 시작
• Unsloth 기능과 API 확인
• 학습 코드의 오류 조사

Build MCP servers, connectors that let AI models use external services through tools. This skill guides API research, tool design, implementation in TypeScript or Python, testing, and evaluation with realistic questions that require multiple tool calls.

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• Connecting an external API to an AI agent
• Building an MCP server in TypeScript or Python
• Improving tool descriptions, inputs, and error messages

Analyze the last seven days of agent and model token usage to identify unnecessary spending. Recommend changes to model selection, prompt caching, and overlapping work, with estimated dollar savings, percentage reductions, and implementation priorities.

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• Investigating higher-than-expected AI costs
• Matching model choices to task complexity
• Reviewing prompt caching efficiency