「model debugging」の検索結果

Kaggle-first end-to-end competition workflow for scored submissions. Use when Codex must run Kaggle or competitive ML workflows through scored submission, including code competitions, validation, metrics, policy-safe public notebook/discussion intel, tabular/text/image modeling, tuning, ensembling/stacking, proactive multi-notebook architectures, producer notebooks that train models and export private Kaggle artifact datasets, downstream consumer notebooks, Kaggle GPU offload, kagglehub access, hidden-test debugging, and public score retrieval.

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Use when writing or debugging prompts for ByteDance Seedance video models (Seedance 2.5, 2.0, 2.0 Mini, 1.5 Pro, 1.0) on Dreamina, Jimeng AI, Doubao, BytePlus ModelArk or ComfyUI, when a generated video drifts off the reference face, grows unwanted subtitles or watermarks, duplicates a character, jumps at an extension seam, or when planning multi-shot or multi-reference video briefs.

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Use when EZ_math_model coder or packaging fails repeatedly with the same error and needs root-cause analysis instead of another blind retry.

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Use when the user is debugging a bug, tracing an error, or asking why something fails. Examples: "Why is X failing?", "Where does this error come from?", "Trace this bug"

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Design, configure, debug, and optimize OpenClaw AI agent deployments. Master guide for gateway configuration, openclaw.json settings, model routing and fallback chains, skills development and publishing, cron job scheduling, memory systems (Qdrant, Neo4j, SQLite), Docker infrastructure, and Tailscale VPN networking. Includes config analyzer that audits your openclaw.json and suggests improvements, plus health checker that validates all OpenClaw subsystems. Built for AI agents — Python stdlib only, no dependencies. Use for OpenClaw setup, gateway debugging, skill building, cron management, model optimization, cost reduction, and infrastructure troubleshooting.

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Load when creating, scaffolding, configuring, debugging, testing, containerizing, or publishing a C#/.NET Model Context Protocol (MCP) server using the official Microsoft ModelContextProtocol SDK; not for non-.NET MCP servers, general ASP.NET Core APIs without MCP, or consuming external MCP tools as a client.

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Manage AI assistant configurations and peer-to-peer mesh network connections in the NV oOS Pro Toolkit. Covers assistant creation, model configuration, provider setup, peer discovery, mesh networking, and cross-assistant communication. Use when creating or editing AI assistants, configuring model providers, setting up peer connections, managing mesh network topology, or debugging assistant behavior.

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Use when working with Dbt Cloud — dbt Cloud management — monitor projects, environments, job runs, model status, and data freshness. Use when reviewing job run health, debugging failed models, inspecting run artifacts, or auditing project configuration.

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Operating discipline distilled from Claude Fable 5 for sessions and subagents on weaker models (Opus 4.x, Sonnet 5/4.x, Haiku 4.5) doing agentic coding. Load it at the START of any non-trivial task when not running on Fable 5: debugging, refactoring, changes across 2+ files, or unfamiliar code; and instruct a subagent on Opus/Sonnet/Haiku to read this skill with a non-trivial assignment. Triggers include "think like Fable", "fable-mode", "turn on discipline", "work carefully", dispatching a subagent on a weaker model, and repeated mistakes or rework in the session. NOT needed for trivial edits (rename, typo, one-liner) or purely conversational answers.

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TypeScriptのバックエンドでPrismaを使う際の設計・実装を支援します。検索や一括更新、移行、接続管理のパターンと注意点を具体例で確認できます。

• Prismaのモデルと関連を設計したいとき
• 検索処理とページ分割の見直し
• トランザクションや一括更新の不具合調査

Capability-based multi-tool matrix for 3D modeling, CAD, point clouds, rendering, GPU debugging, and fabrication. Covers mesh/parametric/photogrammetry and the path from idea to printed part or game-ready asset.

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Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.

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Align a fine-tuned model with preference data using DPO, ORPO, KTO, or SimPO. Use when preference pairs or thumbs-up/down feedback exist, when choosing between preference-optimization methods, or when a DPO run needs hyperparameters or debugging.

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Generate D2 database diagrams from Entity Framework Core models. USE FOR: EF Core database diagram, Entity Framework Core ERD, DbContext diagram, C# entity relationship diagram, PostgreSQL schema visualization, generate .d2 file from EF Core entities, Fluent API mapping diagram, migrations-based database diagram, table relationships, owned types, many-to-many join tables, indexes and constraints. DO NOT USE FOR: runtime debugging, database migration execution, schema deployment, SQL performance tuning, or draw.io diagrams.

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Recall and persist long-term memory through the OpenViking MCP tools. Use at the start of any substantive task (coding, configuration, debugging, multi-step or tool-based work) to retrieve relevant prior knowledge with find/search/read, and during or after work to persist durable facts, preferences, decisions, and lessons with remember. Do not use for casual chat or simple factual questions the model can answer directly.

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Use when writing or debugging integration tests for error processors in packages/core/src/processors/. Covers the MockLanguageModelV2 pattern for simulating API errors and verifying retry behavior, plus the build prerequisites for focused vitest runs.

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Troubleshoots Google Cloud Gemini Enterprise Agent Platform issues (Agent Gateway, Registry, Identity, Policies, Model Armor, Identity-Aware Proxy (IAP)). Use when agent requests fail with 403 (especially unauthorized egress), Agent Runtime queries return 500, or gateway/IAP logs show permission errors. Don't use for general Google Cloud Identity and Access Management (IAM) debugging or networking issues unrelated to the Agent Platform stack.

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Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

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This skill should be used to explain or reason about the foundational concepts of context engineering: what context is, the anatomy of a context window, how attention mechanics work, the U-shaped attention curve, why context quality matters more than quantity, and the mental models needed to interpret every other context-engineering decision. Use this for conceptual explanation, onboarding, and background reading. Route operational work to the specialized skills: debugging attention failures goes to context-degradation, token-efficiency work goes to context-optimization, conversation summarization goes to context-compression, and project-shape decisions go to project-development.

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Use when writing E2E tests with Playwright, setting up test infrastructure, or debugging flaky browser tests. Invoke to write test scripts, create page objects, configure test fixtures, set up reporters, add CI integration, implement API mocking, or perform visual regression testing. Trigger terms: Playwright, E2E test, end-to-end, browser testing, automation, UI testing, visual testing, Page Object Model, test flakiness.

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Build, deploy, and maintain applications on Hugging Face Spaces — Gradio / Docker / Static SDKs, ZeroGPU and dedicated hardware, model loading, debugging, buckets, inference providers, community grants. Use whenever the user asks to create or host an app on Hugging Face, port code onto ZeroGPU, fix a Space that won't build or run, or otherwise work with `hf spaces …`, `@spaces.GPU`, Space README frontmatter, or the `spaces` Python package.

日本語の概要は準備中です。原文の説明を表示しています。

Post-train LLMs with TRL (Transformers Reinforcement Learning) — SFT, DPO, GRPO, KTO, and reward-model training. Use when writing or debugging training code with the TRL Python API or the trl CLI.

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Spin up a live local Omnigent server and exercise the Cursor SDK harness end-to-end — build cursor agents, run real turns, smoke-test, and bug-bash. Load when developing, testing, or debugging the cursor harness (omnigent/inner/cursor_executor.py, cursor_harness.py, cursor_auth.py) or its auth / model / tool-bridge behavior.

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Spin up a live local Omnigent server + runner and exercise the native Pi TUI harness (pi-native) end-to-end — launch the real `pi` CLI via `omnigent pi`, drive turns through the web/bridge, smoke-test, and bug-bash. Load when developing, testing, or debugging the pi-native harness (omnigent/inner/pi_native_executor.py, pi_native_harness.py, omnigent/pi_native.py, pi_native_bridge.py, pi_native_credentials.py) or its bridge / extension / auth / model behavior.

日本語の概要は準備中です。原文の説明を表示しています。