'local models' 검색 결과

Search local session histories across Codex, Claude Code, OpenCode, and other coding agents. Find earlier requests, inspect transcripts and child sessions, and reconstruct past work using recorded evidence, with filters for dates, projects, platforms, and models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding work done a few days ago
• Checking whether a migration was already completed
• Reviewing tasks delegated to child agents

Find, generate, and prepare media for HyperFrames projects, saving assets locally with records for reuse. Covers images, music, voiceovers, transcription, captions, background removal, media edits, and visual treatments such as color correction or retro styling.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find and save images, music, and sound effects
• Produce voiceovers, transcripts, and captions
• Correct dark footage or apply retro styling

Run protein structure prediction, molecular design, and docking tools on Tamarind Bio without a local GPU. Guides tool selection, input validation, job submission, batch processing, and result retrieval through its REST API or MCP server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Predicting protein structures in the cloud
• Running binder or antibody design tools
• Submitting batches of sequences for analysis

Simulate quantum dynamics, steady states, and spectra with QuTiP 5. The skill guides solver selection and checks physical assumptions, units, state validity, and numerical convergence so local simulation results can be reviewed with their limitations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Simulating quantum dynamics with environmental dissipation
• Choosing a solver for a quantum model
• Checking numerical convergence and state validity

Set up and customize Pi, a terminal coding agent framework. This skill guides model configuration, extension development, terminal interfaces, and integration with other applications through its SDK, a software development kit, or process messaging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Install Pi and configure model access
• Create custom tools and terminal interfaces
• Embed Pi in another application

Search Apple Notes by meaning or exact text, discover related notes and unexpected connections, and synthesize ideas with citations. The skill guides local setup and tool selection, with on-device search and optional local or cloud language models for synthesis.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find past notes by topic or exact phrase
• Discover unexpected connections between notes
• Summarize ideas with citations to source notes

Build and debug Apify Actors, cloud programs for web scraping, automation, and data processing. Get guidance on language templates, input and output schemas, runtime code, local testing, and deployment, with practices for handling credentials and external data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an Actor for web scraping
• Defining Actor inputs and structured outputs
• Debugging an Actor with local test data

Analyze bond prices, yields, and issuers’ ability to repay debt. The skill covers interest rate exposure, default risk models, yield spreads, Chinese local government financing platform bonds, and securities backed by pools of loans.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Calculate bond prices, yields, and interest rate sensitivity
• Assess corporate default risk from financial data
• Compare yield spreads across issuers and maturities

Convert language models to GGUF and reduce their size through quantization, which stores model weights at lower precision. Guides cover compression choices, local inference on CPUs and Apple Silicon, and integration with tools such as Ollama.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Running a language model on a CPU-only computer
• Choosing model compression for available memory
• Configuring Metal acceleration on Apple Silicon

Check today's AI coding costs and token usage from local session logs with CodeBurn. Get a short summary of total spending, the most expensive models and projects, unusual usage, and one concrete suggestion for reducing costs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking today's AI coding costs and token usage
• Finding which models and projects cost the most
• Reviewing unusual usage and a concrete way to spend less

Test Omnigent’s Cursor SDK integration against a live local server. Build test agents, run real conversations, and check authentication, model selection, and tool connections while investigating empty responses or bridge processes left after shutdown.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test Cursor authentication and real responses
• Verify file operations and sub-agent delegation
• Investigate invalid models and empty output

Use extracted document content, design evidence, and local assets in iPolloWork video, slide, website, or design templates. Apply confirmed requirements and verify that supplied assets actually appear in the output, without using models to reparse references.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create slides using facts and figures from reference documents
• Apply supported reference styles to a website template
• Reuse document assets in video scenes

Check AltLLM Portal credit, expiry dates, billing transactions, and usage through the local altllm CLI. Review activity by period, model, or API key, and redeem promo codes using a saved Portal session.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check credit, expiry dates, and available models
• Review billing transactions by type
• Compare usage by period, model, or API key

Investigate a Codex CLI session by matching its local conversation record with production router logs. Use the session ID to examine missing reasoning summaries, routing markers, unexpected models, or unusual tool calls and trace relevant code paths.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigate missing reasoning summaries
• Confirm which model actually served a turn
• Trace missing or unexpected routing markers

Transcribe audio files locally with the Whisper command-line tool, without an API key. Export text or translated subtitles, and choose smaller or larger models to balance processing speed and transcription accuracy.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Transcribing an MP3 recording into a text file
• Creating translated subtitles from an M4A file
• Choosing a model for speed or accuracy

Search local session histories across Codex, Claude Code, OpenCode, and other coding agents. Find earlier requests, inspect transcripts and child sessions, and reconstruct past work using recorded evidence, with filters for dates, projects, platforms, and models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding work done a few days ago
• Checking whether a migration was already completed
• Reviewing tasks delegated to child agents

Explain an existing machine learning model by summarizing influential input features and examining individual predictions. It helps investigate unexpected feature interactions and communicate model behavior to nontechnical stakeholders, while noting limitations and potentially misleading explanations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining why a model made a particular prediction
• Identifying influential input features
• Investigating unexpected feature interactions

Fine-tune a pretrained KERMT model on a CSV of molecular structures and labeled targets. The workflow checks inputs, prepares features, launches training in a container, and saves trained models, logs, and test results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Train a molecular model on labeled CSV data
• Select specific prediction targets for training
• Reuse prepared data to compare training settings

Audits, diffs, copies, and lists providers/models, adds models, and switches the default model in local openclaw.json configs; diagnoses gateway/plugin failures (down, parked, broken, or out of date after an upgrade). Use for 虾/龙虾 config audits. Not for model-error repair (use openclaw-model-switch).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert knowledge for Azure AI Custom Vision development including best practices, decision making, limits & quotas, security, integrations & coding patterns, and deployment. Use when exporting Custom Vision models, calling prediction APIs, using ONNX/TensorFlow, managing CMK/RBAC, or Smart Labeler, and other Azure AI Custom Vision related development tasks. Not for Azure AI Vision (use azure-ai-vision), Azure AI services (use microsoft-foundry-tools), Azure Machine Learning (use azure-machine-learning), Azure AI Foundry Local (use microsoft-foundry-local).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Implement local persistence with SwiftData, Core Data, and secure storage. Use when setting up SwiftData models, Core Data stacks, or local persistence in iOS.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Find and compare recommended Hugging Face models for a task using benchmarks, model size, and device constraints. Use for model selection questions; use huggingface-local-models for GGUF setup and hf-cli for Hub operations.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Run evaluations for Hugging Face Hub models using inspect-ai and lighteval on local hardware. Use for backend selection, local GPU evals, and choosing between vLLM / Transformers / accelerate. Not for HF Jobs orchestration, model-card PRs, .eval_results publication, or community-evals automation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Manual QA harness for the senpi coding agent itself. MUST USE after changing packages/ai, packages/agent, packages/coding-agent, or packages/tui — a green typecheck and `npm test` are NOT QA. Drives the real CLI from source in an isolated sandbox (never touches ~/.senpi or real credentials) across four channels: remote RPC (--mode rpc JSONL stdio), TUI smoke (node-pty on Windows, tmux on POSIX), mock loop (a local fake model server for deterministic, zero-token agent-loop runs), and CLI smoke (--help/--print/--list-models). Every helper ships a --self-test. Use whenever someone says qa senpi, test the agent, verify my change, rpc qa, tui qa, mock-loop qa, smoke the cli, or needs evidence that an agent-loop, tool, keybinding, or provider change works end to end. Capture evidence to local-ignore/qa-evidence/.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.