Results for “c-lang”

Expert guidance for Zig, the systems programming language focused on performance, safety, and readability. Helps developers write high-performance code with compile-time evaluation, seamless C interop, no hidden control flow, and no garbage collector. Zig is used for game engines, operating systems, networking, and as a C/C++ replacement.

Foundational Java patterns covering core syntax, object-oriented programming, generics, collections, streams, lambdas, and modern Java features. Use when writing Java code, understanding the type system, working with collections/streams, or needing guidance on which specialized Java skill to use. This is the entry point for Java development.

Define the visual identity of a game including art style, color palettes, character design language, environment aesthetics, and asset pipelines. Use when establishing the look and feel of a game project. Also trigger for "art style", "visual style", "game aesthetics", "color palette", "character art", "pixel art", or "game graphics".

Run Karpathy-style autonomous ML search on a real training repo: choose the right mode (setup, program.md, bounded loop, results interpretation, or constrained-hardware adaptation), preserve the immutable prepare.py / 300-second / val_bpb contract, and route prompt/skill eval work away to LangSmith, Promptfoo, Braintrust, or skill-autoresearch.

All-in-one fullstack dev engine. /aura: 46 modes (build/fix/clean/deploy/review/spec/lore/ax/experiment/payment/debug/qa/orchestrate/escalate+), 6-layer security with 32 hooks, tiered models (ZERO/ECO/PRO/MAX), 8 languages, 16 specialized agents, SPEC/EARS/TRUST5/XLOOP/RALF/Autopus absorbed. ~55% token savings.

Drafts a Spill Prevention, Control, and Countermeasure (SPCC) Plan compliant with 40 CFR Part 112 under the Clean Water Act. Covers facility characterization, oil storage inventory, secondary containment, prevention measures, response procedures, training, and PE certification language. Use when drafting SPCC plans, oil spill prevention documents, or EPA compliance submissions.

Drafts structured client memoranda translating legal analysis and strategic recommendations into plain language for non-lawyer audiences. Covers corporate governance, fiduciary duties, compliance, and transactional advice. Enforces standard memo architecture: heading block, executive summary, background, analysis, options, and recommendations. Use when preparing client-facing memos, opinion letters, or governance briefings.

Finds root cause before applying fixes for errors, crashes, regressions, failing tests, broken behavior, and screenshot-reported defects. Use when users report in any language errors, crashes, broken behavior, regressions, failing tests, screenshot evidence, or something that used to work and now fails. Not for code review or new features.

Generates editable diagram source code (Mermaid, Graphviz DOT, PlantUML, SVG) from natural language, code, schemas, or notes for flowcharts, sequence diagrams, ER diagrams, architecture diagrams, and more. Renders to PNG/SVG/PDF on request via a local Python script.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Starts working on a new feature branch using git-flow. This skill should be used when the user asks to "start a feature", "create feature branch", "begin new feature", "git flow feature start", or wants to start a new feature. Accepts either a branch name or a natural-language description.

Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Detects and fixes patterns including: inflated symbolism, promotional language, superficial analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.

Reference for foundational C++ patterns covering core syntax, classes, templates, RAII, move semantics, and modern C++ features (C++11/14/17/20). Use when writing C++ code, understanding the type system, memory management, or needing guidance on which specialized C++ skill to use.

Foundational Objective-C patterns covering classes, protocols, categories, memory management (ARC/retain-release), blocks, GCD, and Foundation framework. Use when writing Objective-C code, working with Cocoa/Cocoa Touch APIs, bridging to Swift, or needing guidance on Apple platform development patterns. This is the entry point for Objective-C development.

Use when a manager, dispatcher, or safety director needs to have a coaching conversation with a driver — about a safety event (harsh brake, hard turn, distraction), an HOS violation, a customer complaint, a missed delivery, repeated minor incidents, or to deliver positive recognition. Covers conversation structure, language, documentation, and escalation.

Runs a budget-aware agent-assisted engineering health audit for instruction/config drift, hooks/MCP, verifier surfaces, and AI maintainability. Use when users ask in any language to audit Claude, Codex, Pi, agent instructions, MCP or hooks, verifier coverage, or AI-maintainability drift. Not for debugging application code or reviewing PRs.

The intelligence layer of WinDAGs. Decomposes natural language tasks into Hierarchical Task DAGs (HTDAGs), matches subtasks to skills, executes waves in parallel, and dynamically expands nodes when complexity exceeds executor capability. Use for 'orchestrate', 'execute DAG', 'parallel agents', 'decompose task', 'coordinate skills'. NOT for single-skill tasks or simple linear workflows.

Produces distinctive, production-grade UI for pages, components, visual interfaces, typography, and screenshot-driven polish. Use when users ask in any language for UI, page, component, frontend, typography, screenshot-grounded visual polish, or complaints that a screen looks unclear, ugly, inconsistent, or visually wrong. Not for backend logic or data pipelines.

Generate, review, and optimize natural language instructions for Business Central agents (Designer or SDK). Triggers on: agent instructions, InstructionsV1.txt, InstructionsV2.txt, MEMORIZE, qualification rules, agent behavior, instruction keywords, agent task instructions, iterate instructions, or improve agent accuracy. Follows the Responsibilities-Guidelines-Instructions framework with official BC agent runtime keywords.

Begins a new version release using git-flow. This skill should be used when the user asks to "start a release", "create release branch", "prepare a release", "git flow release start", or wants to begin a new version release. Accepts either an explicit version or a natural-language description of the release.

Bidirectional conversion between Clojure and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Clojure↔Scala specific patterns. Use when migrating Clojure projects to Scala, translating Clojure patterns to idiomatic Scala, or refactoring Clojure codebases. Extends meta-convert-dev with Clojure-to-Scala specific patterns.

Bidirectional conversion between Haskell and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Haskell↔Scala specific patterns. Use when migrating Haskell projects to Scala, translating Haskell patterns to idiomatic Scala, or refactoring Haskell codebases. Extends meta-convert-dev with Haskell-to-Scala specific patterns.

Build ontology logs (ologs) from problem descriptions using categorical foundations. Use when designing problem taxonomies, classifying tasks for routing, building knowledge libraries, establishing formal analogies between domains via functor search, or translating between natural language and database schemas. NOT for OWL/RDF ontology work, query tuning, or graph modeling without functional-arrow discipline.

Runs a six-phase research workflow that turns unfamiliar domains, source bundles, or collected material into publish-ready output. Use when users ask in any language to research, study, deep-dive, compile sources, synthesize unfamiliar material, or turn a source bundle into a coherent reference. Not for quick lookups or single-file reads.

Begins a hotfix for a production issue using git-flow. This skill should be used when the user asks to "start a hotfix", "create hotfix branch", "fix a critical bug", "git flow hotfix start", or wants to begin a hotfix. Accepts either an explicit version or a natural-language description of the fix.

Verifies claims, articles, screenshots, and URLs through source-grounded analysis with an evidence ledger, source credibility evaluation, and manipulation detection. Supports quick checks, full fact-check cards, two-source comparisons, and prebunking in multiple languages and policy contexts.

Meta-agent for creating new custom agents, skills, and MCP integrations. Expert in agent design, MCP development, skill architecture, and rapid prototyping. Activate on 'create agent', 'new skill', 'MCP server', 'custom tool', 'agent design'. NOT for using existing agents (invoke them directly), general coding (use language-specific skills), or infrastructure setup (use deployment-engineer).

Bidirectional conversion between Elm and Haskell. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elm↔Haskell specific patterns. Use when migrating Elm frontend code to Haskell, translating Elm patterns to idiomatic Haskell, or refactoring Elm codebases. Extends meta-convert-dev with Elm-to-Haskell specific patterns.

Bidirectional conversion between Erlang and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Erlang↔Roc specific patterns. Use when migrating Erlang projects to Roc, translating BEAM/OTP patterns to functional patterns, or refactoring Erlang codebases. Extends meta-convert-dev with Erlang-to-Roc specific patterns.

Automated hypothesis generation and testing using large language models. Use this skill when generating scientific hypotheses from datasets, combining literature insights with empirical data, testing hypotheses against observational data, or conducting systematic hypothesis exploration for research discovery in domains like deception detection, AI content detection, mental health analysis, or other empirical research tasks.

You are an expert in Bolt.new by StackBlitz, the AI-powered full-stack development environment that runs entirely in the browser. You help developers go from idea to deployed app in minutes using natural language prompts — Bolt generates complete applications with frontend, backend, database, and deployment, all running in a WebContainer without local setup.

Bidirectional conversion between Elixir and Roc. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Elixir↔Roc specific patterns. Use when migrating Elixir projects to Roc, translating BEAM/OTP patterns to platform-based architecture, or refactoring Elixir codebases. Extends meta-convert-dev with Elixir-to-Roc specific patterns.

Run Comet's Opik — open-source LLM observability, evaluation, and optimization — from one routing-first skill: install the Python/TypeScript SDK, stand up a server (Comet.com cloud, Docker Compose via `./opik.sh`, or Kubernetes/Helm), wire tracing through `@opik.track` or one of 50+ framework integrations (OpenAI, Anthropic, LangChain, LangGraph, LlamaIndex, CrewAI, DSPy, Ollama, Bedrock, Vercel AI SDK, …), score outputs with LLM-as-a-judge metrics (Hallucination, Moderation, Answer Relevance, Context Precision), and run Datasets/Experiments evaluations including PyTest CI gates. Use when the user wants LLM tracing, prompt evaluation, production LLM monitoring, agent optimization, or guardrails with Opik. Triggers on: opik, comet opik, opik configure, opik.sh, llm observability, llm tracing, llm as a judge, hallucination metric, prompt evaluation, opik dashboard, opik guardrails, agent optimizer.

Bidirectional conversion between Roc and Scala. Use when migrating projects between these languages in either direction. Extends meta-convert-dev with Roc↔Scala specific patterns. Use when migrating Roc projects to JVM/Scala, translating pure functional patterns to object-functional hybrid, or refactoring Roc codebases. Extends meta-convert-dev with Roc-to-Scala specific patterns.

Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM applications. Use when "building RAG, vector search, embeddings, semantic search, document retrieval, context retrieval, knowledge base, LLM with documents, chunking strategy, pinecone, weaviate, chromadb, pgvector, rag, embeddings, vector-database, retrieval, semantic-search, llm, ai, langchain, llamaindex" mentioned.

Drafts opt-out forms and notices of exclusion for class actions, settlements, arbitration clauses, and privacy programs. Covers party identification, election language, consequence statements, signature blocks, and submission instructions per governing orders or contracts (FRCP 23, arbitration windows, CCPA/GDPR). Use when preparing opt-out paperwork, notices of exclusion, settlement opt-outs, or arbitration opt-out letters.