'iterative' 검색 결과

Use whenever the user mentions Betaflight, FPV drone firmware, flight-controller config, PID/filter/rates tuning, or blackbox log analysis — or shares a Betaflight artifact: a CLI diff/dump (lines like 'diff all', 'set ', 'feature ', 'resource ') or a .bbl/.bfl blackbox log, even when not named as such — or runs a chirp tuning session via Sweep Studio / FPVLogForge (a chirp log ID to analyse, 'analyse log', 'résultat chirp', iterative Bode/step/Ms tuning). Covers flight symptoms (oscillations, wobble, propwash, jello, hot motors, drift), motor/ESC and RX/ELRS/CRSF setup, builds from 3" to 10" (cinewhoop, tinywhoop, longrange, X-class), version migrations (4.4 → 4.5 → 4.6), and configuring a new drone from scratch (setup wizard). Trigger even when phrased casually ("mon drone wobble", "PID help", "config FC") — under-triggering is worse than over-triggering.

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Iteratively review and fix issues on the current branch until the review passes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iteratively study and apply elegant coding patterns. Each iteration - understand the code, research what simple and elegant code looks like, apply learnings, verify with CI. Use as a standalone refactoring pass or when the user asks to make code more elegant, simple, or idiomatic.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Persistent browser and Electron interaction through `js_repl` for fast iterative UI debugging.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create aesthetically beautiful interfaces following proven design principles. Use when building UI/UX, analyzing designs from inspiration sites, generating design images with ai-multimodal, implementing visual hierarchy and color theory, adding micro-interactions, or creating design documentation. Includes workflows for capturing and analyzing inspiration screenshots with chrome-devtools and ai-multimodal, iterative design image generation until aesthetic standards are met, and comprehensive design system guidance covering BEAUTIFUL (aesthetic principles), RIGHT (functionality/accessibility), SATISFYING (micro-interactions), and PEAK (storytelling) stages. Integrates with chrome-devtools, ai-multimodal, media-processing, ui-styling, and web-frameworks skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iterative literature-review workflow for a research topic. Use this skill to draft up to 10 search keywords, build a seed set from OpenAlex title-and-abstract matches, expand by backward and forward citations, screen candidates by title and abstract, repeat until no new relevant papers remain, rank the final set, inspect up to 30 full texts, build a concept-reference knowledge graph, and write a literature review grounded only in the final included references.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build a targeted resume and cover letter for a role folder that already has a job description and role analysis. Runs iterative adversarial review rounds until the materials stabilize, then hands off for human review before submission.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Identify and remove phrasing, structure, and word choices that signal AI authorship. Runs iterative passes through fresh voice-agent instances with the orchestrator acting as gatekeeper on every change. Use on a cover letter before submission, or on any prose that has to read as human-written.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

"Apply action research through Plan-Act-Observe-Reflect cycles and Participatory Action Research (PAR) to generate knowledge while improving practice. Use this skill when the user needs to design practitioner research that integrates inquiry with intervention, facilitate participatory research with stakeholders, structure iterative improvement cycles, or when they ask 'how do I research my own practice', 'how do I involve participants as co-researchers', or 'how do I combine research with practical change'.".

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Get productive with agentic coding assistants — task framing, iterative prompting, file-scoped workflows, review loops, and multi-file refactors. Use when pairing with an AI coding agent on real codebases.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Craft effective AI image prompts with structured syntax, style control, and iterative refinement techniques.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iterative manuscript improvement workflow where a reviewer agent critiques, an author agent revises, a verifier checks the revision, and the loop repeats until acceptance or a human-pause condition is reached. Use for virtual peer review, reviewer-author revision loops, rebuttal preparation, resubmission polishing, journal paper improvement, response-to-reviewers drafting, and deciding when additional data, experiments, theory, modeling, or author judgment is required before further revision.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Write and harden reusable agent-loop specification documents for biomechanics, data science, and vailá coding work. Use when the user says `/preto-loop`, asks to write/create/forge/specify/document an agent loop, turn an iterative vailá task into an autonomous loop, make an overnight loop, or design a biomechanics, data-pipeline, markerless-tracking, testing, refactoring, or performance loop. Include external verification, scientific validity, GUI/CLI parity, reproducibility, memory, guardrails, and named terminal states. Do not use merely to repeat a fixed scheduled prompt, execute an existing loop, or create unrelated documents.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iterative measurement-driven improvement loop. Measure, profile, mutate, re-measure, commit. Works for performance, bundle size, complexity, test coverage — anything quantifiable. Use when the user wants to systematically improve a metric through repeated cycles of profiling and targeted changes.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iteratively study and apply elegant coding patterns. Each iteration - understand the code, research what simple and elegant code looks like, apply learnings, verify with CI. Use as a standalone refactoring pass or when the user asks to make code more elegant, simple, or idiomatic.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Orchestrate AI coding agents (Claude Code, Codex, OpenCode) in isolated sandboxes using the @ai-hero/sandcastle SDK. Use when the user needs to (1) run agents AFK in Docker/Podman containers, (2) build multi-agent pipelines with plan-execute-review patterns, (3) run parallel agents on separate worktrees, (4) create iterative agent loops with maxIterations, (5) extract structured output from agent runs, (6) set up sandcastle in a new or existing project, or (7) write prompt files with template args and shell expressions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Produces an attorney-ready memo from a corpus of legal documents supplied by the user. Use when a user shows up with a folder, zip, or vault of case documents and asks for a case summary, case evaluation, litigation package, intake memo, matter overview, or "can you summarize this case for me." The skill ingests the corpus into a searchable index, OCRs anything non-searchable, inventories and diagnoses the practice area, loads the appropriate practice-area playbook module(s) (PI/tort, commercial litigation, IP infringement, or user-authored extensions), iteratively searches the corpus across eight core dimensions plus any module-specific dimensions, defers specialized document clusters (depositions, medical records, discovery, liens) to dedicated sibling skills, and synthesizes a cited memo.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Iterative development workflow that coordinates implementation, refactoring, QA, and documentation agents to complete features systematically. Use when the user wants a full development workflow with quality checks.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Autonomous bug-elimination loop. Iteratively invokes /bug-hunt and /implement-batch until findings converge below an operator-specified severity floor. At termination, runs /review-test scoped to the run's new reproducing tests and fixes quality issues above the floor. Auto-approves /bug-hunt and /review-test ticket proposals; pulls the andon cord on contested findings, repeated implementation failure, hard-cap exhaustion, or breaking-change proposals. Optional /refactor finisher.

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Autonomous iterative refactoring workflow. Scans for tactical code quality improvements (DRY, dead code, naming, complexity), implements through SMEs, verifies with QA, commits atomically, and loops until no improvements remain.

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Implement 10 advanced AI agent node types for any DSL/runtime system — plan compiler, code interpreter, critique loops, intelligent routing, multi-agent ensemble, persistent memory, external API tools, iterative loops, data transforms, and parallel execution. Use this skill whenever the user wants to add agent capabilities to a workflow engine, build an AI orchestration framework, implement Karpathy-style AI upgrades, add new node types to a DSL runtime, or create autonomous agent pipelines. Also trigger when the user mentions AetherLang, flow engines, node types, agent frameworks, or wants to upgrade their AI system with capabilities like self-programming, reflection, routing, ensemble, memory, or tool use.

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Iteratively define problem and solution shapes.

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Refines ideas iteratively. Refine ideas through structured divergent and convergent thinking. Use "idea-refine" or "ideate" to trigger.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.