Results for “loop”

PIV workflow orchestrator - Plan, Implement, Validate loop for systematic multi-phase software development. Use when building features phase-by-phase with PRPs, automated validation loops, or multi-agent orchestration. Supports PRD creation, PRP generation, codebase analysis, and iterative execution with validation.

Provides battle-tested PyTorch training recipes for LLMs, vision, diffusion, and biomedical domains, covering training loops, optimizer selection, LR scheduling, mixed precision, and debugging.

Compact the current conversation into a handoff document for another agent to pick up.

**Trigger**: Use when working with GKE Storage — Google Kubernetes Engine configuration and management.

One iteration of an unattended development loop. Use when invoked by name.

[OMX] Strict autonomous loop: $deep-interview -> $ralplan -> $ultragoal (+ $team if needed) -> $code-review -> $ultraqa

Guides users through defining goals, metrics, and scope, then runs an autonomous loop of code changes, testing, measuring, and keeping or discarding results for any programming task with a measurable outcome.

Design and document core game mechanics, systems interactions, feedback loops, and emergent behavior. Use when defining how the game plays at a systems level. Also trigger for "game mechanics", "gameplay systems", "game loop", "game rules", "combat system", "crafting system", or "progression system".

Autonomous experiment loop — iteratively mutate code, measure a metric, keep only improvements (hill-climbing ratchet)

Loop feature-interviews over a brainstorm idea set and consolidate survivors into the roadmap

Launch and scale mobile apps with ASO, viral loops, monetization, and App Store growth tactics.

Build consumer apps with validated demand, sustainable unit economics, and growth loops that compound.

Improve existing content and close the learning loop without cannibalizing new-content work.

Expert en croissance virale (K-factor, network effects, referral loops, embedded virality)

Provides React/Next.js UI components for displaying tool call lifecycle states including pending, running, approval, success, and error, with support for human-in-the-loop approval flows.

Runs a closed-loop SEO growth workflow using Search Console data, keyword demand analysis, SERP Authority Mark checks, and the available publishing path, adapting to CMS/API, local repo, hybrid, and advisory-only environments.

Autonomous design space exploration loop for computer architecture and EDA. Runs a program, analyzes results, tunes parameters, and iterates until objective is met or timeout. Use when user says "DSE", "design space exploration", "sweep parameters", "optimize", "find best config", or wants iterative parameter tuning.

Use only for developer-facing products; plan adoption loops, examples, templates, community, and proof artifacts

**Trigger**: Use when working with the Google Cloud CLI (`gcloud`) — authentication, resource management, configuration, and troubleshooting.

Use when reviewing a branch, PR, or work-in-progress changes against standards and spec

Defines an inter-agent communication protocol for C-suite agent teams, including invocation syntax, loop prevention, isolation rules, and response formats to coordinate cross-functional analysis and board meetings.

**Trigger**: Use when developing Flutter applications — widget patterns, state management (Riverpod/Bloc), navigation, and platform integration.

**Trigger**: Use when creating, editing, or formatting PDF documents — generation, template application, content extraction, and validation.

Drive systematic growth with acquisition loops, experimentation frameworks, retention systems, and product-led strategies.

Keep a PR merge-ready by triaging comments, resolving clear conflicts, and fixing CI in a loop.

Stateful single-mission improvement loop with strict evaluator contract, markdown decision logs, and max-runtime stop behavior

Designs and governs recursive self-improvement loops where an agent mines its own failures and proposes edits to its own harness, prompts, or workflow, covering acceptance gates, diversity preservation, and the optimization ladder.

Python asyncio - Modern concurrent programming with async/await, event loops, tasks, coroutines, primitives, aiohttp, and FastAPI async patterns

Run a structured, multi-source deep-research loop on a question until the answer stops changing.

Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.

Multi-agent adversarial verification with convergence loop. Two independent review agents must both pass before output ships.

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

Autonomous experiment loop that optimizes any file by a measurable metric. Inspired by Karpathy's autoresearch. The agent edits a target file, runs a fixed evaluation, keeps improvements (git commit), discards failures (git reset), and loops indefinitely. Use when: user wants to optimize code speed, reduce bundle/image size, improve test pass rate, optimize prompts, improve content quality (headlines, copy, CTR), or run any measurable improvement loop. Requires: a target file, an evaluation command that outputs a metric, and a git repo.

This skill should be used when the user asks to "improve test coverage", "add unit tests", "TDD", "test this module", "write tests for", "increase coverage", "/ut command", or discusses unit testing strategies. Covers the unit test loop workflow, React Testing Library best practices, query priorities, and coverage improvement strategies.

Design habit-forming product loops using the Hook Model (Trigger, Action, Variable Reward, Investment). Use when the user mentions "users arent coming back", "engagement loops", "habit formation", "push notifications", "variable rewards", "daily active users", "habit zone", or "user retention loops". Also trigger when designing notification strategies, building streaks or progress systems, or analyzing why users stop using a product after initial signup. Covers ethics evaluation and onboarding for habits. For friction reduction and B=MAP, see improve-retention. For viral sharing, see contagious.

This skill should be used when the user asks for "browser tests", "playwright tests", "end-to-end testing", "test user flows", "E2E coverage", "integration tests for UI", "page object pattern", "/e2e command", or discusses automated browser testing. Covers the E2E test loop workflow, Playwright patterns, page objects, and selector strategies.