Results for “code mode”

OpenAI Codex CLI wrapper — three modes. Code review: independent diff review via codex review with pass/fail gate. Challenge: adversarial mode that tries to break your code. Consult: ask codex anything with session continuity for follow-ups. The "200 IQ autistic developer" second opinion. Use when asked to "codex review", "codex challenge", "ask codex", "second opinion", or "consult codex". (gstack) Voice triggers (speech-to-text aliases): "code x", "code ex", "get another opinion".

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Locate CCSPlayerPawn::SetModelFromClass in CS2 server.dll / libserver.so via IDA Pro MCP and emit a fresh, minimal-unique signature or offset for the WeaponPaints gamedata entry "CCSPlayerPawn::SetModelFromClass" (symbol CCSPlayerPawn_SetModelFromClass). Shortlist via callers of CBaseModelEntity::SetModel inside pawn model-selection code: this variant takes the pawn's class model. The body resolves a cached model precache handle from the player class and calls SetModel. Distinguish from SetModelFromLoadout by the ABSENCE of loadout/inventory item lookups in the call chain. Trigger: CCSPlayerPawn_SetModelFromClass, CCSPlayerPawn::SetModelFromClass

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Use Codex to manage Odysseus todos, calendars, memory, and documents, and read or draft email. Through its permission-scoped API, the skill also supports reminders and launching, inspecting, or stopping Cookbook model servers.

• Create a timed reminder as a todo
• Check calendar events and add appointments
• Read email and prepare an editable reply draft

Delegate a clearly scoped repository task to Kimi Code without opening its interactive interface. The skill reads structured execution events, extracts the final response, and checks for errors, with separate handling for read-only work and authorized changes.

• Delegate changes to specific files
• Inspect a repository using a read-only copy
• Extract Kimi Code's final response and check errors

Build and troubleshoot MCP servers, which connect AI agents to external tools, for codewhale. Covers local and remote connections, credential configuration, tool validation, and testing both successful requests and failures.

• Building an MCP server for a local tool
• Connecting codewhale to a remote service
• Configuring credentials and connection headers

Document a local codebase with file references, architecture diagrams, and verified project commands. For legacy modernization, assess what still runs and create a phased migration plan with validation methods suited to each component’s current condition.

• Creating an onboarding guide for a codebase
• Mapping system architecture and dependencies
• Planning a legacy system migration or rewrite

Combine capabilities from multiple fine-tuned LLMs without retraining using mergekit. Choose a merging method, configure how models are blended, and evaluate the resulting model on tasks such as math, code generation, and conversation.

• Combining math, coding, and conversation capabilities
• Blending two models at different ratios
• Comparing variants made with different merging methods

Train smaller language models using knowledge distillation, where a larger model supplies examples or prediction targets. This skill covers training losses, generated training data, parameter choices, and output comparisons to support model compression and specialized student models.

• Training a smaller model from a larger teacher
• Fine-tuning with teacher-generated responses
• Distilling domain knowledge into a specialized model

Chat with NanoClaw directly inside Emacs and send selected text or org-mode sections for help. Replies can appear under child headings in your notes, supporting code review, meeting summaries, research questions, and draft revisions through a local connection.

• Ask about code or errors without leaving Emacs
• Send selected code for review
• Summarize meeting notes in org-mode

Get answers about Qwen Code usage, features, configuration, and troubleshooting from its official documentation. The skill also helps inspect and edit user or project settings files when configuration changes are requested.

• Learning Qwen Code features and commands
• Checking how to configure MCP server connections
• Reviewing or changing settings.json

Compare the installed Claude Code version with release notes and recent bug reports to decide whether to stay, update, or use a specific version. Recommendations account for reported problems, relevant fixes, and how Claude Code was installed.

• Deciding whether to update Claude Code
• Checking reports of problems in a recent release
• Choosing a version to roll back to

Clear the active model pin in Weave Router using a short alias for unforce-model. A bundled script sends the release signal through its output, allowing the router to remove the pin and continue the current turn.

• Clear the active model pin
• Request unforce-model through a short alias
• Release a model pin during the current turn

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Configure and troubleshoot agents connected to BB through ACP, an agent connection protocol. Manage agent discovery, registry additions, model choices, custom agent settings, skill locations, and supported conversation compaction, which condenses session context.

• Add an agent from the official ACP registry
• Investigate missing OpenCode models or modes
• Configure custom agents and skill locations

Diagnose Codex behavior within BB, including question cards, MCP requests for user input or approval, Computer Use app access, model availability, service tiers, session options, and persistent goals, using thread and provider controls.

• Investigate a Codex turn waiting for an answer
• Review app access and MCP tool approvals
• Check models and service tiers on the execution host

Review changed code for duplication, quality, efficiency, and clarity. Use review-only mode for findings, or request behavior-preserving cleanup by the main agent, with focused tests or other validation when the chosen mode calls for it.

• Check changed code for reuse opportunities
• Simplify redundant state and complex control flow
• Find duplicate reads and unnecessary computation

Leverage OpenAI Codex/GPT models for autonomous code implementation, code review, and plan review. Triggers: "codex", "use gpt", "gpt-5", "let openai", "full-auto", "adversarial review", "second opinion review", "用codex", "让gpt实现", "对抗式审查", "让codex审查计划", "第二意见". Use this skill whenever the user wants to delegate coding tasks to OpenAI models, run code or plan reviews via codex, get a second-opinion review from a different model, or execute tasks in a sandboxed environment.

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Use the skill to control and verify the code interface configuration of your model — how Simulink® model elements are represented in the generated C or C++ code. This includes — (1) specifying names, types (storage classes), and placement of variables that represent model elements (for example, making a model parameter tunable as a global extern variable); (2) specifying names, types, and placement of functions that represent model algorithms; (3) selecting the deployment type (Component, Subcomponent, or Automatic); (4) selecting the interface configuration type (data or service interface); (5) linking a shared Embedded Coder dictionary to a model; (6) creating Embedded Coder dictionary entries and setting their properties; (7) for service interface configuration — specifying service interface definitions, including sender, receiver, client, and server services. Items 1 and 2 can be set per element or as a category-wide default. Item 1 applies to GRT and ERT models; the rest to ERT models only.

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Bridge Codex to web-based AI model products by packaging local task context, scrub-checking it, sending it through an approved browser surface to ChatGPT Pro, Claude, Grok, Gemini, or another web model, waiting for the answer, and returning the model response to the user or Codex. Also guide DevSpace-like MCP Connector Mode when the user wants ChatGPT Pro or another MCP-capable web host to access approved local workspaces without relying on Codex browser automation. Use when the user asks for GPT Pro, ChatGPT Pro, Claude web, Grok, Gemini web, web model bridge, external model consult, ask another model, second opinion, use a web AI model, browserless agent access to GPT Pro, MCP connector, DevSpace-like workflow, or send local repo context to a browser-based model for planning, review, debugging, architecture discussion, or implementation guidance.

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Connect external services to Claude Code plugins through MCP, a protocol that exposes service functions as tools. Get guidance on server configuration, connection methods, authentication, tool permissions, and testing connections and tool calls.

• Adding a local MCP server to a plugin
• Configuring authentication for a hosted service
• Using multiple services in commands and agents

View recorded output tokens, cache-read tokens, response counts, and available mode attribution for a Claude Code session. In other agent environments, find the native usage report where supported, without treating observed usage as proof of savings.

• Checking recorded token usage for the current session
• Reviewing response counts and mode attribution
• Aggregating recorded output across sessions

View and configure status displays for Codex CLI and OMX workflows. Show the model, context usage, active modes, worker counts, and turns, with compact or detailed presets, live updates, and JSON output for scripts.

• Check active modes and worker counts
• Monitor workflow status with live updates
• Choose compact or detailed status displays

Combine code from local directories or GitHub into one structured file for AI analysis. Examine project structure, search for related code, and check estimated tokens, the units AI models use to process text.

• Preparing a single codebase file for AI analysis
• Reviewing a project's directory structure
• Finding authentication code and database models

Analyze a codebase to identify valuable assets, attack entry points, and realistic abuse paths. Produce a concise Markdown threat model that explains risk priorities, distinguishes existing protections from proposed mitigations, and makes assumptions explicit.

• Map attack entry points in a codebase
• Assess threats within a specific project directory
• Prioritize risks to sensitive data and credentials