Results for “iteration”

Persists through implement-verify-fix loops until a task is fully completed and verified, overriding default stopping behavior.

Resize a product photo to Shopify's recommended 2048×2048px square format, sharpen, and convert to WebP for fast storefront load times.

Generates, remixes, and manages short video clips via OpenAI's Sora API for cinematic shots, b-roll, and rapid concept iteration.

Diagnoses and fixes bugs in emergency mode with a maximum of two iterations, then commits, pushes, and creates a pull request.

Educational single-threaded TypeScript vanity address generator for Solana using @solana/web3.js with async iteration, event-loop yielding, streaming generator API, and best-effort memory zeroization.

Create, edit, and export videos from the terminal using the Bazaar CLI, with a gated workflow for context, generation, review, and iteration.

Adopt the voice and methodology of Spotify's Daniel Ek to guide product strategy, marketplace design, and iterative growth decisions.

Detect when backtest iteration has stalled and generate structurally different strategy pivot proposals to break out of local optima.

Read and query onchain data using events, The Graph, Dune, Ponder, Multicall3, and WebSocket subscriptions, with guidance on block iteration limitations.

Mines git history and codebase to reconstruct the development lifecycle, identify sequential and parallel patterns, and produce actionable recommendations for future iterations.

Generates a complete Figma Make prompt package with context primer, checkpoint, build prompts, and iteration templates for designing UI pages, components, or screens.

Design effective logos using AI image generation, covering logo types, prompting techniques, scalability rules, and iteration workflows.

Treats rough ideas as competing organisms on an evolution island, scoring, selecting, crossing, and evolving them through structured rounds to surface the strongest concepts.

Create, synthesize, and iteratively improve agent skills following the Agent Skills specification. Handles source capture, precision passes, authoring, registration, and validation.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques for 1.5-3.6× speedup without quality loss.

Guides project-level decisions for LLM-powered systems: task-model fit, pipeline architecture, token and cost estimation, and agent-assisted iteration.

Validate and use CUDA graph capture in Megatron Bridge, including local full-iteration graphs and Transformer Engine scoped graphs for attention, MLP, and MoE modules.

Covers MariaDB-specific behavior of the Connector/C++ sql:: API, including driver and connection setup, 1-based indexes, placeholder syntax, ResultSet iteration, raw pointer ownership, SQLString, autocommit, and exception handling.

Guides the agent to act as a product manager, collecting context, proposing three requirement plans (bug fix, new feature, iteration), scoring them by ROI, and recommending one with an MVP slice and timeline.

Manages persistent coding sessions across multiple AI coding engines including Claude Code, Codex, Antigravity, Cursor, and OpenCode, with support for multi-agent councils, cross-session messaging, deep planning, parallel code review, and autonomous workspace iteration.

Generates comprehensive unit tests with edge cases, error paths, and boundary values. Auto-detects the test framework, scans for untested functions, adopts existing conventions, runs tests, and self-heals failures in up to 3 iterations while measuring coverage.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.

Accelerate LLM inference using speculative decoding, Medusa multiple heads, and lookahead decoding techniques. Use when optimizing inference speed (1.5-3.6× speedup), reducing latency for real-time applications, or deploying models with limited compute. Covers draft models, tree-based attention, Jacobi iteration, parallel token generation, and production deployment strategies.

Runs a two-wave validation pipeline (deterministic checks then rule-based checks) before every git commit, writing a sentinel file that a pre-commit hook enforces. Includes optional quick mode (first wave only) and a fix loop with up to three iterations.

Use a live Jupyter kernel for stateful, iterative Python execution via hamelnb. Load this skill when the task involves exploration, iteration, or inspecting intermediate results — data science, ML experimentation, API exploration, or building up complex code step-by-step. Uses terminal to run CLI commands against a live Jupyter kernel. No new tools required.

Use when a task is well-defined, has an automatic way to verify success (tests, linter, build), and is tedious or large enough to benefit from unattended iteration - e.g. "get all tests passing", "implement this spec end to end", "fix every lint error". Not for one-shot changes or work that needs human judgment mid-stream.

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

Guide users through a structured workflow for co-authoring documentation. Use when user wants to write documentation, proposals, technical specs, decision docs, or similar structured content. This workflow helps users efficiently transfer context, refine content through iteration, and verify the doc works for readers. Trigger when user mentions writing docs, creating proposals, drafting specs, or similar documentation tasks.

In-session iterative loop — keeps Claude running in the current session until a task is done. Unlike /loop (which spawns background workers), /iloop stays in the current session and re-prompts each iteration via Stop hook. Use for: 'keep fixing until tests pass', 'iterate until this feature works', autonomous debugging. Triggers on: '/iloop', 'in-session loop', 'keep iterating', 'loop until done'.

Niagara emitter color/curve authoring (tint, hue-shift, ColorFromCurve keys), rapid-iteration parameter tuning, and Custom-HLSL scratch-pad authoring (NiagaraEmitterService + NiagaraScratchPadService). Module/renderer CRUD is owned by the engine NiagaraToolsets. Use when the user asks to recolor or hue-shift particles, edit color curves, tune emitter params, or build a scratch-pad/Custom HLSL module. For system-level lifecycle, load niagara-systems.

Expert in using v0.dev for AI-powered UI generation. Covers prompting strategies, component iteration, shadcn/ui integration, export workflows, and customization. Knows how to get the best results from v0 and integrate generated components into production codebases. Use when "v0, v0.dev, generate ui, shadcn component, ai component, generate component, v0, v0-dev, ui-generation, shadcn, component-generation, ai-ui, vercel" mentioned.

Guides the agent to reference official MathWorks Documentation and Help. Determine correct function syntax and workflows from user guides when deeper context is needed. Minimize iterations and repetitive trial and error. Use this skill to: Identify correct syntax and configuration details. Retrieve relevant, version-specific (or release-specific) information from official documentation. Consult user guides when conceptual or workflow context is needed. Apply best practices.

Orchestrates end-to-end autonomous AI research projects using a two-loop architecture. The inner loop runs rapid experiment iterations with clear optimization targets. The outer loop synthesizes results, identifies patterns, and steers research direction. Routes to domain-specific skills for execution, supports continuous agent operation via Claude Code /loop and OpenClaw heartbeat, and produces research presentations and papers. Use when starting a research project, running autonomous experiments, or managing a multi-hypothesis research effort.