Results for “code mode”

Deploy AI models to embedded hardware using MathWorks tools (MATLAB, Simulink, Embedded Coder). Covers two workflow patterns: (1) MathWorks-native or 3P-imported models rebuilt as dlnetwork for lean hardware (Cortex-M, DSP), (2) direct C/C++ code generation from PyTorch and LiteRT models for high-performance hardware (Cortex-A, x86, GPU). Trigger when: user wants to deploy AI to embedded targets; generate C/CUDA from neural networks; compress AI models for MCU/DSP; integrate AI in Simulink for system-level simulation; import PyTorch/ONNX/TensorFlow models for embedded deployment; optimize AI for resource-constrained hardware; or use loadPyTorchExportedProgram, importNetworkFromPyTorch, dlquantizer, exportNetworkToSimulink, or Embedded Coder with AI models.

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Code mode operational spec for the team lead. Returns lead identity, facilitator identity, mode-specific rules, suggest-members guidance, and phase arc for code-mode teams.

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Runs several language models as independent coders on one labeling or discovery task and treats disagreement as data. Covers diverse panel assembly, independent voting, consensus rules, Cohen’s and Fleiss kappa, Krippendorff’s alpha, correlated-error limits on validity claims, human validation, and reporting. Use when the user asks about a council, panel, ensemble, or jury of models, how to combine model labels, or what kappa or alpha to report for model coders. Single-model codebook and validation work goes to text-classification, per-item confidence to llm-calibration-logprobs.

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Hand off code review to OpenAI Codex CLI. Use when asked to "review code with codex", "codex review", "get a second opinion on code", "review my changes", "review this PR", or when you want AI-powered code review from a different model perspective.

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Get a second opinion or independent review from Codex (OpenAI) via the local Codex CLI. Use when the user wants Codex to review a design memo, PR, diff, or decision, or to cross-check an approach from another model. Also trigger on "Codexに聞いて", "Codexと相談", "Codexにレビュー", "Codexの意見", "別のモデルで確認".

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[BETA] Detect, review, and fix code-quality and correctness issues in an AEM as a Cloud Service project — locally, with no external services or network calls. Use whenever a user wants to check, review, assess, audit, scan, modernize, upgrade, or fix AEM Java, Sling Models, OSGi, or Maven code — for example: "check my Sling Models are implemented correctly", "review my @Inject usage", "are my Maven dependencies up to date", "scan this AEM project for issues", "modernize my Sling Models", or "fix code-quality problems". Name the files to assess, or ask it to scan the repo; it detects issues, plans, and — only when you ask — applies surgical edits on a branch or in place, then verifies with mvn compile. It recognises the intent and handles each issue type itself, reporting anything it cannot yet fix. This skill is in beta. Verify all outputs before applying them to production projects.

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Add a model provider to OpenClaw and verify it with a real response. The skill validates configuration, keeps credentials in environment or file references, and reports test results and any default model changes.

• Adding a model provider to OpenClaw
• Configuring credentials through environment or file references
• Checking whether a model returns a real response

Compare coding agents on tasks from your own codebase. Define success checks, run agents in separate Git working directories, and report pass rates, available API costs, completion time, and consistency across repeated trials.

• Comparing coding agents on real development tasks
• Evaluating a new tool or model before adoption
• Checking performance after agent updates

Add regression tests to AI-assisted code changes so previously found bugs are checked automatically. Test required API fields and sandbox response contracts, then run tests and builds before AI review to catch recurring omissions and inconsistent behavior.

• Add regression tests after fixing a bug
• Check required API response fields
• Review sandbox and production response consistency

Look up ponytail’s three operating levels, related skills, and commands in one reference card. Find how to activate or stop it, set the default mode, and update it in supported environments.

• Compare the lite, full, and ultra levels
• Find commands for related skills
• Check how to set the default mode

Look up commands for concise response modes, commit messages, review comments, and Markdown compression. The reference also explains how to check the current mode, return to normal responses, and configure startup defaults.

• Look up response mode commands
• Check commands for commits and reviews
• Find how to turn a mode off

Break down a Claude Code session's costs by assistant message to find expensive responses. Compare input, output, cache writes, cache reads, and models, with message cost percentiles providing context for investigating unusually costly activity.

• Investigate messages in a flagged session
• Check the cost contribution of cache writes
• Review expensive messages after a specific time

Audit a codebase with three vulnerability hunters and two engineers who build small demonstrations of suspected flaws. Verify attack paths, assess severity from demonstrated impact, and report evidence, targeted fixes, and checks to prevent recurrence.

• Review security before a release
• Audit security risks in a pull request
• Verify whether a suspected flaw is exploitable

Build and review ViewModels, objects that manage screen state and actions, with CommunityToolkit.Mvvm. Use attributes to generate change notifications and commands, add input validation, and handle asynchronous operations, cancellation, and command availability.

• Add properties that notify the screen when values change
• Create asynchronous commands with cancellation
• Validate registration forms and settings

Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.

• Adding a new diffusion model to SGLang
• Porting a Diffusers generation pipeline
• Adding a variant by reusing existing model components

Route LinkedIn tasks such as search, outreach, recruiting, and posting to the appropriate Curviate CLI skill. It also explains shared rules for setup checks, response handling, error codes, and previewing changes before sending them.

• Find the right skill for LinkedIn searches
• Plan a workflow from finding people to messaging them
• Check shared CLI rules before running commands

Convert LLMs to GGUF and reduce model size through quantization, which stores weights at lower numerical precision. Prepare models for CPU or Apple Silicon inference, compare compression levels, and integrate them with local AI tools.

• Preparing an LLM for CPU-only use
• Running a local model on Apple Silicon
• Comparing model size and quality across quantization levels

Train language models with SimPO using pairs of preferred and rejected answers. Configure preference training without a separate reference model, adapt settings for base or instruction models, and address unstable training, memory limits, or lost capabilities.

• Training a base model with preferred and rejected answers
• Further tuning an instruction model with preference data
• Adjusting training settings for math and code tasks

Compress large language models to 4-bit weights to reduce GPU memory use and accelerate inference. Covers loading compressed models, configuring quantization, and using Transformers or vLLM, with guidance on hardware requirements and backend choices.

• Running large models with limited GPU memory
• Compressing a model with domain-specific sample text
• Serving AWQ models with vLLM

Reduce GPU memory use by loading language models in 8-bit or 4-bit formats with bitsandbytes and HuggingFace Transformers. Includes configuration examples, QLoRA fine-tuning of compressed models, and optimizers that reduce memory needed during training.

• Load larger models within limited GPU memory
• Compare 8-bit and 4-bit model settings
• Fine-tune a model with QLoRA

Build and train Mixture of Experts models, which activate selected specialist networks for each input. Use DeepSpeed or HuggingFace workflows to configure routing, balance workloads, distribute experts across GPUs, and optimize inference.

• Designing MoE models for a limited compute budget
• Implementing Mixtral-style expert routing
• Adjusting expert workloads and training stability

Review board memos and strategy briefs with available Claude, Codex, and Gemini models. Compare concerns, supporting arguments, and votes, then turn disagreements into questions to resolve before committing to a major business decision.

• Reviewing fundraising terms before signing
• Checking the reasoning behind a major strategic pivot
• Examining an acquisition memo before a decision

Build or update a Figma design system from an existing codebase. Organize shared design values, light and dark themes, reusable components, and documentation through staged reviews, then check consistency, accessibility, and links between design and code.

• Creating shared color and spacing variables from code
• Setting up light and dark themes
• Building a reusable component library with documentation

Introduce new users to OpenWork with explanations suited to their technical background. Cover file summaries, document creation, and organization, plus setup guidance for models, skills, plugins, and connections to external tools.

• Getting started with OpenWork
• Learning how to request file summaries and organization
• Understanding skills and plugins