'code models' 검색 결과

Iterative rubric-based evaluation and self-improvement loop. Builds a scoring rubric interactively, evaluates an artifact with multiple models in parallel (Codex, Gemini, Claude), then autonomously improves the artifact one criterion at a time until a score threshold is met or circuit breaker fires. "/rulph", "rubric evaluate", "rubric score", "multi-model evaluate", "score and improve", "evaluate and iterate", "grade this", "루브릭 루프", "채점 루프", "자율 개선", "개선 루프", "루브릭 평가"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert guidance for Comet ML, the platform for tracking machine learning experiments, managing models, and monitoring production ML systems. Helps developers log experiments, compare model versions, and build reproducible ML pipelines with automatic code/data versioning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Cerebras Inference is a hosted LLM API that runs open models on Cerebras wafer-scale chips at thousands of tokens per second, through an OpenAI-compatible endpoint. Use when a user wants the lowest-latency chat, code completion or agent loop, asks how to call the Cerebras API or SDK, needs streaming, tool calling, strict JSON schema output or reasoning control, or hits a "model not found" error from a retired Cerebras model ID.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when writing or changing PyTorch code — model, Dataset/DataLoader, training and evaluation loops, determinism, mixed precision, torch.compile, checkpoints with safetensors, Hugging Face models, ONNX export, and tests that run on CPU

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use when building native iOS with SwiftUI - small composable views, observable state models, atomic components, Swift 6.2+ concurrency, and system styling over hardcoded values

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Runs security audits on codebases — full scans, diff reviews, threat models, vulnerability triage, remediation guidance, and finding tracking. Activate when the user says "security scan", "audit this repo", "review this PR for security", "threat model", "triage vulnerabilities", "fix this vuln", or "track findings".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create and render OpenSCAD 3D models. Generate preview images from multiple angles, extract customizable parameters, validate syntax, and export STL files for 3D printing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Threat-models and hardens an application, API, data flow, dependency, or deployment against authentication and authorization flaws, injection, secrets exposure, unsafe deserialization, SSRF, abuse, privacy loss, and supply-chain risk. Use for security review, threat modeling, hardening, or sensitive changes. Not for generic code style review or an unexplained bug without a security hypothesis.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Stop and consult this skill whenever your response would include specific facts about Anthropic's products. Covers: Claude Code (how to install, Node.js requirements, platform/OS support, MCP server integration, configuration), Claude API (function calling/tool use, batch processing, SDK usage, rate limits, pricing, models, streaming), and Claude.ai (Pro vs Team vs Enterprise plans, feature limits). Trigger this even for coding tasks that use the Anthropic SDK, content creation mentioning Claude capabilities or pricing, or LLM provider comparisons. Any time you would otherwise rely on memory for Anthropic product details, verify here instead — your training data may be outdated or wrong.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implementing robust business logic, API integrations, data models, and reproducible AI image-generation code with type safety. Use for production implementation, Gemini image API pipelines, or interactive pair programming.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Translate a confirmed mini-program product specification or bounded feature into an implementable architecture covering modules, pages, components, services, canonical state sources, data models, interfaces, permissions, failures, caching, concurrency, idempotency, migration, rollback, external dependencies, and architecture decision records. Use when users ask how to structure a mini program, design data or APIs, divide frontend and cloud responsibilities, evolve an existing system safely, or prepare implementation after product semantics are stable. Preserves product meaning, current repository constraints, user-owned changes, and verifiable acceptance behavior rather than redesigning the product for technical convenience.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Authoritative reference for Pi (@earendil-works/pi-coding-agent): install, configure, run, and extend it. Use for the CLI and configuration, providers and models, extensions, skills, MCP, codemode, the SDK/RPC, security and trust, design philosophy, and the pi repository's internals and contribution rules.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Python backend development standards for FastAPI, SQLModel, pytest, Ruff, and Pyright. Use when writing or modifying backend code in src/merchant, src/payment, src/apps_sdk, agents, tests, API routes, services, models, or protocol logic.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Writes or reviews a pre-analysis plan before data collection — registry choice (OSF, AEA RCT Registry, AsPredicted, registered reports), PAP structure, models and decision rules locked in advance, analysis code run on simulated data, contingencies for attrition, failed manipulations, and exclusions, deviation records, and timeline. Operationalizes the pre-data-collection side of DA-RT. Use when the user asks to draft or audit a pre-registration or PAP, choose a registry, decide what to lock versus leave exploratory, or handle a later deviation. Hypotheses and estimands come from hypothesis-building, post-hoc reporting from methods-reporting.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Advises on setting up and validating topic models for survey and experimental text. Covers the discovery-versus-measurement question, choosing STM, LDA, or BERTopic, preprocessing consequences, prevalence and content formulas, topic-count selection across coherence, exclusivity and FREX, held-out likelihood, and residuals, validation of topics against human judgment, robustness, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are present, how many topics to use, about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Fixed-codebook coding goes to text-classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Designs publication tables for social-science manuscripts. Covers whether a result belongs in a table at all, column order, row grouping and panels, decimal precision, standard-error and confidence-interval conventions, self-contained titles and notes, and code-generated output. Use when building or revising a regression, balance, descriptive, or robustness table, or when working with modelsummary, stargazer, gt, esttab, or booktabs. Finished tables go to figure-table-audit for end-stage QA.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Advises on setting up and validating topic models for survey and experimental text: discovery vs. measurement, STM/LDA/BERTopic choice, preprocessing, covariate formulas, choosing K across coherence, FREX, held-out likelihood, and residuals, validating topics against human coding, robustness, and reporting. Coding against a fixed codebook goes to text-classification.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Writes or reviews a pre-analysis plan before data collection: registry choice (OSF, AEA, AsPredicted, registered reports), locked models and decision rules, code run on simulated data, contingencies, and deviation records. Use when drafting or auditing a pre-registration or PAP, choosing a registry, or handling a deviation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Auto-selects and enforces the strongest valid Python typing lane for the detected Python version and dependencies — no user input required. Use when adding or tightening type annotations, eliminating Any usage in internal code, designing boundary validators or parsers, choosing between stdlib typing (TypedDict, Protocol, dataclasses), Pydantic models, or Hypothesis property tests, addressing ty or mypy failures, or applying version-specific features (TypeIs, ReadOnly, PEP 695 generics, PEP 649 deferred evaluation). Enforces boundary isolation — raw payloads validated immediately at ingress and returned as typed internal objects.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write and optimize prompts for AI-generated outcomes across text and image models. Use when crafting prompts for LLMs (Claude, GPT, Gemini), image generators (Midjourney, DALL-E, Stable Diffusion, Imagen, Flux), or video generators (Veo, Runway). Covers prompt structure, style keywords, negative prompts, chain-of-thought, few-shot examples, iterative refinement, and domain-specific patterns for marketing, code, and creative writing.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use for "how does X work", code walkthroughs before changing something, and placement / ownership / layering questions ("where should this live", "which package owns this", "is this the right layer"). Explains subsystem architecture, runtime flow, onboarding mental models. Can critique architecture. Use why for motivation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Odoo 17 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 17 or custom addons, including traceback diagnosis, addons, manifests, XML errors, HTTP controllers, business rules, feature work, refactoring, or reviews.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.