Search Results for "Code Models" | AgentCC

external-model-selection

Choose optimal external AI models for code analysis, bug investigation, and architectural decisions. Use when consulting multiple LLMs via Claude, comparing model perspectives, or investigating complex Go/LSP/transpiler issues. Provides empirically validated model rankings (91/100 for MiniMax M2, 83/100 for Grok Code Fast) and proven consultation strategies based on real-world testing.

code-to-diagram

Generate architecture diagrams, ER diagrams, sequence diagrams, flowcharts, and class diagrams from codebases using Mermaid.js. Use when users ask to visualize code structure, draw architecture diagrams, create ER diagrams from database models, generate sequence diagrams from API flows, or produce any diagram from source code. Triggers on: 'draw architecture', 'generate diagram', 'visualize code', 'ER diagram', 'sequence diagram', 'class diagram', 'flowchart from code', 'module dependency graph'.

Code-to-Diagram

Generate architecture diagrams, ER diagrams, sequence diagrams, flowcharts, and class diagrams from codebases using Mermaid.js. Use when users ask to visualize code structure, draw architecture diagrams, create ER diagrams from database models, generate sequence diagrams from API flows, or produce any diagram from source code. Triggers on: 'draw architecture', 'generate diagram', 'visualize code', 'ER diagram', 'sequence diagram', 'class diagram', 'flowchart from code', 'module dependency graph'.

llm-icon-finder

Finding and accessing AI/LLM model brand icons from lobe-icons library. Use when users need icon URLs, want to download brand logos for AI models/providers/applications (Claude, GPT, Gemini, etc.), or request icons in SVG/PNG/WEBP formats.

claudish-usage

CRITICAL - Guide for using Claudish CLI ONLY through sub-agents to run Claude Code with any AI model (OpenRouter, Gemini, OpenAI, local models). NEVER run Claudish directly in main context unless user explicitly requests it. Use when user mentions external AI models, Claudish, OpenRouter, Gemini, OpenAI, Ollama, or alternative models. Includes mandatory sub-agent delegation patterns, agent selection guide, file-based instructions, and strict rules to prevent context window pollution.

sub-agent-patterns

Comprehensive guide to sub-agents in Claude Code: built-in agents (Explore, Plan, general-purpose), custom agent creation, configuration, and delegation patterns. Use when: creating custom sub-agents, delegating bulk operations, parallel research, understanding built-in agents, or configuring agent tools/models.

enferno-dev

Development skill for Enferno Flask framework. Use when implementing features, fixing bugs, or writing code for Enferno-based applications. This includes creating models, API endpoints, Vue.js frontend components, database operations, or any development task within the Enferno ecosystem. Triggers: creating blueprints, adding models, building APIs, Vue/Vuetify components, Celery tasks, database migrations.

prompt-refiner-gpt

Refine prompts for GPT models (GPT-5, GPT-5.1, Codex) using OpenAI's best practices. Use when preparing complex tasks for GPT.

axiom-foundation-models-ref

Charleswiltgen Axiom Axiom Foundation Models Ref

Reference — Complete Foundation Models framework guide covering LanguageModelSession, @Generable, @Guide, Tool protocol, streaming, dynamic schemas, built-in use cases, and all WWDC 2025 code examples

mx-review

Review code for MX Space project conventions. Checks NestJS patterns, TypeGoose models, Zod schemas, API design, etc.

rails-concern

Thibautbaissac Rails Ai Agents Rails Concern

Creates Rails concerns for shared behavior across models or controllers with TDD. Use when extracting shared code, creating reusable modules, DRYing up models/controllers, or when user mentions concerns, modules, mixins, or shared behavior.

socket-programming

A5c Ai Babysitter Socket Programming

Deep integration with socket APIs for TCP/UDP programming across platforms. Execute socket operations, analyze socket options and buffer configurations, debug connection states, and generate optimized socket code for different I/O models.

domain-model-extractor

Extract domain models from monolithic codebases using DDD principles for microservices decomposition

evaluating-code-models

Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.

EvaluationCode GenerationHumanEval+6

openrouter-trending-models

Fetch trending programming models from OpenRouter rankings. Use when selecting models for multi-model review, updating model recommendations, or researching current AI coding trends. Provides model IDs, context windows, pricing, and usage statistics from the most recent week.

task-external-models

Quick-reference for using external AI models in orchestration workflows. External models are invoked via Bash+claudish CLI (deterministic, 100% reliable). Use when confused about how to run external models, "claudish with Bash", "external model in /team", or "how to specify external model". Trigger keywords - "external model", "claudish", "Bash claudish", "external LLM", "model parameter".

external-modelquick-referencebash+2

proxy-mode-reference

Reference guide for using external AI models via claudish CLI. Use when running multi-model reviews, understanding how /team invokes external models, or debugging external model integration issues. Includes routing prefixes for MiniMax, Kimi, GLM direct APIs.

multi-model-validation

Run multiple AI models in parallel for 3-5x speedup with ENFORCED performance statistics tracking. Use when validating with Grok, Gemini, GPT-5, DeepSeek, MiniMax, Kimi, GLM, or Claudish proxy for code review, consensus analysis, or multi-expert validation. NEW in v3.2.0 - Direct API prefixes (mmax/, kimi/, glm/) for cost savings. Includes dynamic model discovery via `claudish --top-models` and `claudish --free`, session-based workspaces, and Pattern 7-8 for tracking model performance. Trigger keywords - "grok", "gemini", "gpt-5", "deepseek", "minimax", "kimi", "glm", "claudish", "multiple models", "parallel review", "external AI", "consensus", "multi-model", "model performance", "statistics", "free models".

orchestrationclaudishparallel+11

model-tracking-protocol

MANDATORY tracking protocol for multi-model validation. Creates structured tracking tables BEFORE launching models, tracks progress during execution, and ensures complete results presentation. Use when running 2+ external AI models in parallel. Trigger keywords - "multi-model", "parallel review", "external models", "consensus", "model tracking".

orchestrationtrackingmulti-model+2

data-modeling

Data modeling with Entity-Relationship Diagrams (ERDs), data dictionaries, and conceptual/logical/physical models. Documents data structures, relationships, and attributes.

pytm

Python-based threat modeling using pytm library for programmatic STRIDE analysis, data flow diagram generation, and automated security threat identification. Use when: (1) Creating threat models programmatically using Python code, (2) Generating data flow diagrams (DFDs) with automatic STRIDE threat identification, (3) Integrating threat modeling into CI/CD pipelines and shift-left security practices, (4) Analyzing system architecture for security threats across trust boundaries, (5) Producing threat reports with STRIDE categories and mitigation recommendations, (6) Maintaining threat models as code for version control and automation.

threat-modelingstridedfd+4

Wheels Anti-Pattern Detector

Automatically detect and prevent common Wheels framework errors before code is generated. This skill activates during ANY Wheels code generation (models, controllers, views, migrations) to validate patterns and prevent known issues. Scans for mixed arguments, query/array confusion, non-existent helpers, and database-specific SQL.

tool-use-structured-output

aws-solutions-library-samples

Use Bedrock tool_use to guarantee structured JSON outputs from Claude models. Eliminates JSON parsing failures by forcing responses through typed tool schemas.

grpo-finetuning

aws-solutions-library-samples

Implement GRPO (Group Relative Policy Optimization) fine-tuning for vision-language models on small datasets. Use when SFT underperforms or training data is limited (<1000 examples).

dhh-ruby-style

Write Ruby and Rails code in DHH's distinctive 37signals style. Use this skill when writing Ruby code, Rails applications, creating models, controllers, or any Ruby file. Triggers on Ruby/Rails code generation, refactoring requests, code review, or when the user mentions DHH, 37signals, Basecamp, HEY, or Campfire style. Embodies REST purity, fat models, thin controllers, Current attributes, Hotwire patterns, and the "clarity over cleverness" philosophy.

saving-codeacts

Save executed Python code as reusable tools in the gentools package. Use when preserving successful code executions for later reuse. Covers creating package structure (api.py, impl.py), defining Pydantic output models, and implementing the run() interface.

local-llm-router

Route AI coding queries to local LLMs in air-gapped networks. Integrates Serena MCP for semantic code understanding. Use when working offline, with local models (Ollama, LM Studio, Jan, OpenWebUI), or in secure/closed environments. Triggers on local LLM, Ollama, LM Studio, Jan, air-gapped, offline AI, Serena, local inference, closed network, model routing, defense network, secure coding.

typescript-advanced-patterns

Advanced TypeScript patterns for type-safe, maintainable code using sophisticated type system features. Use when building type-safe APIs, implementing complex domain models, or leveraging TypeScript's advanced type capabilities.

agent-architect

Create and refine OpenCode agents via guided Q&A. Use proactively for agent creation, performance improvement, or configuration design. Examples: - user: "Create an agent for code reviews" → ask about scope, permissions, tools, model preferences, generate AGENTS.md frontmatter - user: "My agent ignores context" → analyze description clarity, allowed-tools, permissions, suggest improvements - user: "Add a database expert agent" → gather requirements, set convex-database-expert in subagent_type, configure permissions - user: "Make my agent faster" → suggest smaller models, reduce allowed-tools, tighten permissions

python-executor

Execute Python code in a safe sandboxed environment via [inference.sh](https://inference.sh). Pre-installed: NumPy, Pandas, Matplotlib, requests, BeautifulSoup, Selenium, Playwright, MoviePy, Pillow, OpenCV, trimesh, and 100+ more libraries. Use for: data processing, web scraping, image manipulation, video creation, 3D model processing, PDF generation, API calls, automation scripts. Triggers: python, execute code, run script, web scraping, data analysis, image processing, video editing, 3D models, automation, pandas, matplotlib

llm-models

Access Claude, Gemini, Kimi, GLM and 100+ LLMs via inference.sh CLI using OpenRouter. Models: Claude Opus 4.5, Claude Sonnet 4.5, Claude Haiku 4.5, Gemini 3 Pro, Kimi K2, GLM-4.6, Intellect 3. One API for all models with automatic fallback and cost optimization. Use for: AI assistants, code generation, reasoning, agents, chat, content generation. Triggers: claude api, openrouter, llm api, claude sonnet, claude opus, gemini api, kimi, language model, gpt alternative, anthropic api, ai model api, llm access, chat api, claude alternative, openai alternative

ruby-on-rails-best-practices

Ruby on Rails architecture and coding patterns from Basecamp. Use when writing, reviewing, or refactoring Rails code to follow proven conventions for models, controllers, jobs, and concerns. Triggers on tasks involving Rails models, concerns, controllers, background jobs, or Turbo/Hotwire.

fast-mlx

Optimize MLX code for performance and memory. Use when asked to implement or speed up MLX models or algorithms, reduce latency/throughput bottlenecks, tune lazy evaluation, type promotion, fast ops, compilation, memory use, or profiling.

anti-duplication

Before implementing new code (endpoints, components, services, models), search the codebase for existing patterns to reuse. Prevent code duplication by finding and suggesting similar implementations. Auto-trigger when user asks to create, implement, add, or build new functionality.

foundation-models

On-device LLM integration using Apple's Foundation Models framework. Use when implementing AI text generation, structured output, or tool calling.

architecture-spec

Generates technical architecture specification from PRD. Covers architecture pattern, tech stack, data models, and app structure. Use when creating ARCHITECTURE.md or designing system architecture.

apple-intelligence

Apple Intelligence skills for on-device AI features including Foundation Models, Visual Intelligence, and intelligent assistants. Use when implementing AI-powered features.

create-openapi-schemas-from-golang-models

Create OpenAPI schemas from Golang models in Layer5 Cloud, generating schema artifacts in Meshery Schemas repository. Handles cross-repository schema creation workflow with proper naming conventions, Go code generation, and backwards compatibility validation.

pytorch-model-recovery

This skill should be used when reconstructing PyTorch models from weight files (state dictionaries), checkpoint files, or partial model artifacts. It applies when the agent needs to infer model architecture from saved weights, rebuild models without original source code, or recover models from corrupted/incomplete saves. Use this skill for tasks involving torch.load, state_dict reconstruction, architecture inference, or model recovery in CPU-constrained environments.

rstan-to-pystan

This skill provides guidance for translating RStan (R-based Stan interface) code to PyStan (Python-based Stan interface). It should be used when converting Stan models from R to Python, migrating Bayesian inference workflows between languages, or adapting R data preparation logic to Python equivalents.

dojo-test

Write tests for Dojo models and systems using spawn_test_world, cheat codes, and assertions. Use when testing game logic, verifying state changes, or ensuring system correctness.

dojo-review

Review Dojo code for best practices, common mistakes, security issues, and optimization opportunities. Use when auditing models, systems, tests, or preparing for deployment.

rlm

Recursive Language Models (RLM) CLI - enables LLMs to recursively process large contexts by decomposing inputs and calling themselves over parts. Use for code analysis, diff reviews, codebase exploration. Triggers on "rlm ask", "rlm complete", "rlm search", "rlm index".

rag-implementation

applied-artificial-intelligence

Comprehensive guide to implementing RAG systems including vector database selection, chunking strategies, embedding models, and retrieval optimization. Use when building RAG systems, implementing semantic search, optimizing retrieval quality, or debugging RAG performance issues.

huggingface-transformers

applied-artificial-intelligence

Hugging Face Transformers best practices including model loading, tokenization, fine-tuning workflows, and inference optimization. Use when working with transformer models, fine-tuning LLMs, implementing NLP tasks, or optimizing transformer inference.

dev_invoke_gemini-cli

Delegate QAQC and review tasks to Google Gemini CLI using markdown file handoff pattern. Write review request to REVIEW.md, Gemini analyzes, outputs findings to FINDINGS.md. Use for code review, security audits, documentation review, large context analysis. Triggers: gemini, gemini cli, delegate to gemini, gemini subagent, code review, QAQC, quality check, security audit, documentation review, large context, second opinion, architecture review, gemini-3-pro-preview, gemini-3-flash-preview Prerequisites: Gemini CLI authenticated (gemini login or GEMINI_API_KEY) Models: gemini-3-pro-preview (default), gemini-3-flash-preview (large context)

dev_invoke_kimi-cli

Delegate testing, QA, and code review tasks to Opencode CLI using Kimi K2.5 model via markdown file handoff. Write test request to TASK.md, Opencode with Kimi K2.5 generates tests/reviews, outputs to OUTPUT.md. Use for test generation, QA verification, edge case detection, code coverage analysis, security reviews. Triggers: kimi, kimi k2.5, kimi cli, opencode kimi, test generation, QA, quality assurance, code review, unit tests, integration tests, edge cases, test coverage, testing, kimisubagent, togetherai kimi, opencode/kimi-k2.5-free, togetherai/moonshotai/Kimi-K2.5 Prerequisites: Opencode CLI installed, Together.ai API key (if using togetherai provider) Models: opencode/kimi-k2.5-free (recommended), togetherai/moonshotai/Kimi-K2.5 (alternative)

rails-audit-thoughtbot

Perform comprehensive code audits of Ruby on Rails applications based on thoughtbot best practices. Use this skill when the user requests a code audit, code review, quality assessment, or analysis of a Rails application. The skill analyzes the entire codebase focusing on testing practices (RSpec), security vulnerabilities, code design (skinny controllers, domain models, PORO with ActiveModel), Rails conventions, database optimization, and Ruby best practices. Outputs a detailed markdown audit report grouped by category (Testing, Security, Models, Controllers, Code Design, Views) with severity levels (Critical, High, Medium, Low) within each category.