'loops' 검색 결과

Audit website redirects after URL migrations, domain changes, or CMS upgrades. Record each redirect path and HTTP status code, identify unnecessary steps or loops, and update redirects to point directly to the final destination.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking redirect paths after a URL migration
• Finding redirect chains and loops after a domain change
• Simplifying redirect rules accumulated through CMS upgrades

Review JavaScript loops and replace suitable ones with standard array and object methods. Make transformations, filtering, and aggregation easier to understand, with practical explanations and browser checks to confirm that changes preserve the intended behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Refactoring manual loops into array methods
• Clarifying data transformation, filtering, and aggregation
• Reviewing object transformations and deep copying

Analyze compiled programs without running them by recognizing branches, loops, data structures, and common instruction patterns. Use disassembly and decompilation, which translate machine code into readable representations, to reconstruct program logic and document findings.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Understand an unfamiliar executable
• Reconstruct branches and loops from assembly
• Infer data structures and function parameters

Design workflows that evaluate and refine AI outputs through self-review, separate generation and evaluation roles, tests, or weighted scoring criteria. Includes patterns for structured feedback, iteration limits, and stopping when quality stops improving.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding self-review and revision loops to AI outputs
• Separating generation from evaluation
• Refining generated code using test results

Design autonomous AI agents that break goals into tasks, use tools, and review their own work. Covers reasoning and action loops, separate planning and execution, and reliability measures such as cost limits, validation, and logging.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing how an agent plans and executes tasks
• Breaking goals into constrained steps
• Adding self-review and correction to an agent

Design interview processes for specific roles and experience levels, with round objectives, timing, suggested questions, and scoring criteria. Help interviewers align their judgments and review potential bias before using the process.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Designing interview rounds for a specific role
• Preparing questions for each interview round
• Aligning scoring criteria across interviewers

Find repeated engineering tasks and turn them into agent workflows with clear checks, stopping rules, and approval boundaries. Compare published workflows, adapt suitable examples, or review existing ones for weak verification and uncontrolled repetition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find recurring tasks in code and conversation history
• Compare and adapt published agent workflows
• Review workflows for weak checks and missing stop rules

Review game data and formulas for combat, economy, progression, or loot. Compare values with documented targets, identify dominant strategies and broken resource loops, and produce prioritized adjustment recommendations while clearly marking areas that lack enough data.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing weapon damage and combat effectiveness
• Finding resource loops and pricing problems
• Reviewing experience and power progression

Investigate recurring problems by mapping feedback loops, the cycles through which effects influence their causes. Identify underlying structures, match known patterns, and assess where changes could help and what unintended effects they might produce.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating recurring incidents
• Understanding coordination problems as teams grow
• Assessing unintended effects before changing a policy

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Create print-friendly slides explaining system architecture or a pipeline, a sequence of processing steps. The layout combines a cream background, blueprint grids, process boxes, rust-red callouts, and dashed feedback loops.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Explaining a system architecture
• Presenting the steps in a processing pipeline
• Showing feedback loops in a workflow

Create SVG illustrations of active figures using one continuous black line and offset color patches that resemble misaligned printing. The skill guides poses, colors, composition, rendering, and visual checks for product pages and announcement cards.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Illustrations for empty states and 404 pages
• Playful figures for onboarding screens
• Artwork for blog headers and launch announcements

Measure and improve animation performance on frontend pages. Identify offscreen activity, pause CSS and canvas or WebGL loops, audit resource cleanup, and compare runtime observations before and after changes to investigate scrolling issues and long-session slowdowns.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Pause animations outside the viewport
• Investigate jerky scrolling
• Audit cleanup after page navigation

Review Apache Pinot changes affecting frequently executed code or making performance claims in a pull request. Relevant areas include operators, segment readers, aggregation functions, and locking inside loops that process individual rows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review changes to frequently executed processing code
• Review performance improvement claims in a pull request
• Review locking inside per-row loops

Review regular expressions used to detect errors in agent engines. Check for empty matches, test patterns against real logs, and add safeguards against repeated matches that can cause JavaScript loops to hang.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review new error detection patterns
• Test patterns against empty strings
• Check detection using real error logs

Draft clear instructions for an agent to keep working toward a measurable goal. It defines the objective, constraints, validation commands, and stopping conditions, and guides setup, monitoring, and troubleshooting for the agent's supported runtime.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Drafting a goal prompt with a measurable stopping condition
• Planning repeated test and fix cycles
• Setting up and monitoring an autonomous run

Run DOCA service containers on BlueField using YAML deployment files and standalone kubelet, the agent that starts containers. Check service readiness and troubleshoot startup failures, image downloads, missing mounts, and repeated restarts.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting a DOCA service container on BlueField
• Investigating image download failures or restart loops
• Checking whether a running container's service is ready

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", "state transitions", "input feedback", "the interface feels dead", "make the UI feel responsive", or "add polish to interactions". Also trigger when designing form-validation responses, progress indicators, confirmation dialogs, or any 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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Use this skill when designing, generating an MVP blueprint for, auditing, troubleshooting, refactoring, or explaining an agentic harness for any domain. Covers provider-neutral agent architecture for OpenAI, Anthropic, and OpenAI-compatible APIs: agent loops, tool design, record provenance, interactive presentation, user-memory lifecycles, environment-adaptive tools, speculative tool execution, late-bound capabilities, permissions, system prompts, planning, goals, always-on agents and durable runtime, adaptive agent teams, context compaction, memory, skills, MCP/external connectors, public-board communications, realtime voice agents, hardware agents and board deployment, self-refining recursive harnesses, programmable context, continual refinement, observability, evals, prompt caching, agent-legible environments, feedback loops, and safety.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Compares Hi-C contact maps between conditions across the right scale -- differential bin-pair contacts (multiHiCcompare, diffHic), differential A/B compartments (dcHiC), differential TAD boundaries (delta insulation), and differential loops (diffloop, DiffHiChIP) -- with distance-stratified between-sample normalization, replicate-aware NB-GLM FDR, HiCRep SCC reproducibility gating, and CNV correction for cancer/aneuploid samples. Use when comparing Hi-C between treatment and control, finding differential contacts/compartments/boundaries/loops, normalizing two maps of unequal depth, choosing a replicate-aware test, gating replicates with SCC, or correcting copy-number artifacts before a tumor-vs-normal comparison.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Renders Hi-C contact matrices honestly and reproducibly with matplotlib, cooltools, HiCExplorer, pyGenomeTracks, FAN-C, CoolBox, and plotgardener. Covers the raw/ICE-balanced/observed-over-expected transform choice, LogNorm vs symmetric-diverging colormaps with vmax/percentile clipping, resolution-to-feature matching (compartments 100-500kb, TADs 10-40kb, loops 5-10kb), square vs rotated-triangle track-stacking, NaN/white-stripe handling, virtual 4C, APA/saddle/on-diagonal pileups, two-condition side-by-side and log2-ratio maps, and interactive (HiGlass) vs scripted-static publication figures. Use when plotting a contact matrix, choosing a normalization or color scale, building a multi-track Hi-C figure, making a virtual 4C profile, piling up loops/boundaries, or comparing two conditions.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Patterns for AI agents that learn from their own execution, detect failure modes, and improve autonomously. Use when building agents that get better over time, managing auto- memory, or designing self-correcting feedback loops.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Detect growth loops in the business and model each loop's amplification by script. "what growth loops do we have"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.