Results for “loop”

Improves AI skills for activation, clarity, and cross-model reliability through benchmarking, salience tuning, and regression triage.

Coordinate multi-stage edge research pipelines from candidate detection through strategy design, review, revision, and export.

Creates a hero frame with looping CSS animations including a rotating type ring, animated globe, ticking timer, and parallax labels.

Explains how Agent Script instruction resolution works across three phases, with patterns for pre-LLM setup, LLM reasoning, and post-action loops.

Build educational TypeScript vanity-address tooling for Pump.fun/Solana wallets with @solana/web3.js, async generators, event-loop yielding, prefix/suffix matching, batch search, and best-effort memory zeroization.

Run the Ouroboros specification-first development loop: reduce ambiguity with a Socratic interview grounded in live git data (commits, churn, contributors), freeze an immutable seed/spec, render the execution plan through spec-kit (/speckit.plan → /speckit.tasks), execute against that contract through cli-anything agent-native CLI harnesses (cli-hub, --json evidence), verify before claiming success, and keep looping until completion is actually verified. Use when the user wants spec-first clarification, git-aware interviews, immutable requirements, drift-aware implementation, harness-driven execution, or a persistent completion loop that should keep going until tests / checks / acceptance criteria pass. Triggers on: ooo, ouroboros, interview, seed, run workflow, evaluate, evolve, ooo ralph, specification first, socratic interview, git-aware interview, ambiguity reduction, execution plan, cli harness execute, persistent completion.

Apply Boyd's OODA loop to navigate fast-moving, uncertain, or competitive situations — Observe what is actually happening, Orient through mental models and context, Decide on the clearest path, Act quickly, then loop again. Load when the situation is changing faster than the current plan, when a competitive response is needed, when the user is stuck in analysis paralysis in a dynamic environment, when shipping under uncertainty, or when deep-thinking diagnoses a fast-moving / competitive frame. Triggers on "what should we do right now", "the situation is changing", "how do we respond to this", "competitive response", "we need to move fast", or "we're stuck deciding". Based on John Boyd's OODA loop — Observe, Orient, Decide, Act — adapted for product and business contexts (OODA Canvas 2026).

Run a Codex prompt repeatedly on a fixed interval. Use for "/loop", "run this every N minutes/hours", "poll X every 5 minutes", "repeat this Codex prompt", or any recurring interval-based Codex job. This is the interval-repeat companion to the separate `schedule` skill (cron / specific-time). For a durable, always-on alternative that survives reboots and terminal exits, prefer Codex app Automations; this local CLI is a terminal fallback that only fires while its daemon process is running.

Makes AI responses feel more human by adding an inner dialogue loop that reflects on and revises the initial instinctive reply.

Provides deep expertise in Node.js internals: V8 engine, libuv event loop, native addon development, build systems, and performance debugging.

Runs a disciplined diagnosis loop for hard bugs and performance regressions, from reproduction through hypothesis testing, instrumentation, fixing, and regression testing.

Iteratively builds websites with Stitch by reading a baton file, generating pages via MCP tools, integrating them, and writing the next task.

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Test-driven development with red-green-refactor loop. Use when: user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Train machine learning models end-to-end, covering data loading, preprocessing, architecture selection, training loops, validation, and checkpointing. Use when the user requests model training or provides relevant inputs for this workflow.

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Build web-based games and game engines using HTML5 Canvas, WebGL, and JavaScript with frameworks like Phaser, Three.js, and Babylon.js.

Breaks a topic into a sequence of adaptive learning articles, adjusting depth and pace based on user feedback from the previous lesson.

Human-in-the-loop web studio to tune AI-generated output by eye. Stand up a local interactive studio (sliders, pickers, drag handles) or an inline edit/highlight/comment annotation studio for prose &

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Analyze complex systems through stocks, flows, and feedback loops to find high-leverage interventions. For organizational, environmental, social, and technical systems exhibiting circular causality. NOT for linear problems or simple cause-effect chains.

LLM-assisted human-in-the-loop review. Make sense of a change, focus attention where it matters, test. Use when the user says "checkpoint", "human review", or "walk me through this change".

Use this when the system needs scheduled tasks, cron-style jobs, periodic maintenance, sync loops, cleanup tasks, or non-interactive background automation that is not simply an API request or queue worker.

LLM-assisted human-in-the-loop review. Make sense of a change, focus attention where it matters, test. Use when the user says "checkpoint", "human review", or "walk me through this change".

Test-driven development with red-green-refactor loop. Use when user wants to build features or fix bugs using TDD, mentions "red-green-refactor", wants integration tests, or asks for test-first development.

Design the small details -- triggers, rules, feedback, loops and modes -- that separate good products from great ones. Use when the user mentions "microinteraction", "button feedback", "loading state", "toggle design", "animation detail", "interaction polish", "state transitions", or "input feedback". Also trigger when designing form validation responses, progress indicators, confirmation dialogs, or any UI element where the user expects immediate feedback. Covers trigger design, state rules, feedback mechanisms, and progressive loops. For overall UI polish, see refactoring-ui. For affordance design, see design-everyday-things.

Runs a persistent verification-and-fix loop, executing existing test suites and browser/desktop checks until the requested surface is verified or a real blocker remains.

Turns a rough request into working software via a governed build loop: align first, plan, then iterate one task at a time with deterministic backpressure and holdout-scenario judging.

Set up a new autoresearch experiment interactively. Collects domain, target file, eval command, metric, direction, and evaluator. Use when the user runs /ar:setup or asks to start optimizing a file with the autoresearch loop.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.

Automated PR review and CI auto-fix for GitHub and GitLab using the Composio CLI. Pulls diffs, fetches failing job logs, posts review comments, and loops fix commits until checks go green.

Expert in LangGraph - the production-grade framework for building stateful, multi-actor AI applications. Covers graph construction, state management, cycles and branches, persistence with checkpointers, human-in-the-loop patterns, and the ReAct agent pattern.

Implement iterative evaluation and refinement loops for AI agent outputs, using self-critique, evaluator-optimizer patterns, and rubric-based scoring to improve quality.

Design the small details—triggers, rules, feedback, loops, and modes—that separate good products from great ones, based on Dan Saffer's framework.

Provides patterns and strategies for migrating imperative TypeScript code to fp-ts functional programming patterns, covering error handling, null checks, callbacks, dependency injection, and loops.