'improve' 검색 결과

에이전트가 작업 중 실제로 겪은 공통적인 장애를 구체적인 개선안으로 정리해 Paperclip에 기록하고, 주요 작업을 멈추지 않고 공유합니다.

• 도구 동작의 불일치 보고
• 부족한 설명이나 맥락 전달
• 반복되는 승인 절차 개선

Review, improve, rewrite, or draft Storybook documentation in /docs. Assess each page’s purpose, audience, structure, and examples, then make suitable edits or provide a content plan, with formatting checks after changes.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improve the structure and examples of a documentation page
• Draft documentation for a new feature
• Plan page types and outlines

Turn a repeatable workflow into a skill, or improve an existing one through test prompts and feedback. Compare results with and without the skill, review performance measurements, and refine its description so it triggers appropriately.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a recurring workflow into a reusable skill
• Improve an existing skill using feedback
• Compare outputs with and without a skill

Compare ruflo and HAL systems for running the GAIA benchmark, an AI evaluation. Examine differences in code execution, browsing, file handling, memory, and model selection to prioritize improvements by expected impact and effort.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Plan the next round of GAIA improvements
• Compare code execution and browser capabilities
• Weigh improvement priorities against implementation effort

Create reusable agent skills or improve existing ones through test prompts and user feedback. Compare output quality, execution time, and token usage against a baseline, then refine instructions and descriptions so the skill is invoked in appropriate situations.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Turn a recurring workflow into a reusable skill
• Improve an existing skill using test results
• Compare quality, runtime, and token usage across versions

Improve existing code structure while preserving its behavior, using Martin Fowler's refactoring approach. Assess tests, identify structural problems, agree on a phased plan, and make small, tested changes with reversible commits and before-and-after comparisons.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Splitting long functions into clearer responsibilities
• Removing duplicate or unused code
• Planning gradual improvements to legacy code

Improve code through repeated experiments against a measurable target, such as execution time or memory use. After defining scope and constraints, the agent tests changes, keeps improvements, reverts unsuccessful attempts, and logs results.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reduce execution time through measured code experiments
• Optimize memory use or bundle size
• Compare changes using benchmark results

Review AI prompts for harmful output risks, bias, privacy concerns, security weaknesses, and clarity. Receive an assessment, a revised prompt, and suggested tests with explanations of the principles behind each improvement.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Review a prompt before putting it into use
• Check wording for stereotypes and bias
• Assess data exposure and prompt injection risks

Run repeated change-and-evaluation cycles for one predefined improvement mission within an explicit runtime limit. Save each evaluation as JSON and each decision in readable Markdown logs, with support for periodic reruns through Claude Code native cron.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Repeating improvements under a fixed evaluation method
• Keeping evaluation results and decision logs
• Running experiments within a time limit

Guide a structured review after a security incident, using timelines and response data to identify root causes and process gaps. Produce review notes, response metrics, and practical improvements with named owners and deadlines.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing response after a ransomware incident
• Turning exercise findings into process improvements
• Updating incident response playbooks

Improve an existing skill by checking its structure and category requirements, proposing focused fixes, and testing again. Compare failure and warning counts to decide whether to keep changes or restore the previous content.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Fix missing fields and instructions in a skill
• Address findings from category checks
• Compare check results before and after edits

Improve code, prompts, skills, or agent definitions through repeated changes and evaluation. Measure numerical targets such as latency or size, or use an LLM to judge pass-or-fail criteria, keeping changes that improve the result.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving page load performance
• Reducing build output size
• Refining prompts against evaluation criteria

Build repeatable DeepEval tests for AI agents, chatbots, and applications that retrieve documents to answer questions. Reuse or generate evaluation datasets, inspect failures, and improve prompts, tools, or retrieval through repeated evaluation runs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Add repeatable quality tests for an AI agent
• Evaluate chatbot behavior across multiple conversation turns
• Generate evaluation datasets from documentation

Review past dsh agent sessions to identify recurring tasks and test proposed skill or memory improvements. This skill guides preview runs, validation on reserved tasks, staged proposals, adoption with backups, and scheduled nightly cycles.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Extract recurring tasks from past agent sessions
• Test proposed skill and memory improvements
• Review staged changes before adopting them

Audit existing SKILL.md files for clear activation conditions, usable instructions, and defined outputs. Get prioritized findings and specific improvements, with changes applied only when requested and the author's intent preserved.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Audit existing skills for quality issues
• Clarify when a skill should activate
• Move lengthy examples into supporting files

Run repeated Ouroboros execution and improvement cycles as a background job, starting from a Seed specification or continuing an existing lineage. The loop stops on a quality check pass, convergence, cancellation, or the generation limit, and reports the outcome.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Starting repeated improvement from a Seed specification
• Continuing an existing improvement lineage
• Following meaningful background progress

Guided journey from a shipped app that works but feels rough to a product that fits the job, flows without friction, reads clearly, and persuades honestly. Orchestrates nine skills phase by phase - jobs-to-be-done, ux-heuristics, design-everyday-things, refactoring-ui, microinteractions, made-to-stick, influence-psychology, high-perf-browser, steve-jobs-design-review - asking the user questions at every decision point and recording results in the project docs/ folder (CUSTOMER.md, DESIGN.md, POSITIONING.md, IMPROVE-APP-PLAN.md) so the journey resumes across sessions. Use when the user wants to fix a clunky product, cut UX friction, sharpen in-app copy, or says 'the app works but feels rough'. Not for code, tests, or hardening - use improve-code-quality (fresh prototype) or remove-technical-debt (aged); no app yet, create-app; needs growth loops, grow-app; marketing-site friction, improve-website; one leaking flow, conversion-optimization. For one framework in isolation, invoke that skill directly.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Agent Performance Optimization Workflow workflow skill. Use this skill when the user needs Systematic improvement of existing agents through performance analysis, prompt engineering, and continuous iteration and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Optimize existing blog posts and web content for GEO (Generative Engine Optimization) — AI search visibility on ChatGPT, Perplexity, Claude, and Google AI Overviews. Use whenever a user provides a blog URL or pastes existing content and asks to optimize it for AI search, rewrite for LLM visibility, improve GEO score, make content answer-first, add FAQ sections, restructure headings for AI retrieval, improve citation potential, or audit content for AI readiness. Also triggers on "GEO optimize this", "make this AI-search ready", "rewrite for Perplexity", "optimize for ChatGPT", "optimize for Google AI Overviews", "improve AI visibility", "reformat for AI search", "make this content citation-worthy", "content isn't showing up in AI search", "why isn't my blog cited by AI", "GEO audit", "answer-first rewrite", "restructure for AI", "make my blog AI-friendly", "improve LLM citability". Do NOT trigger for writing new content from scratch — use geo-content for that.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Skill Optimization: Auto Research. Autonomously optimize any Claude Code skill by running it repeatedly, scoring outputs against binary evals, mutating the prompt, and keeping improvements. Based on Karpathy's autoresearch methodology. Use when: optimize this skill, improve this skill, run autoresearch on, make this skill better, self-improve skill, benchmark skill, eval my skill, run evals on. Outputs: an improved SKILL.md, a results log, and a changelog of every mutation tried.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Improve an existing agent SKILL.md through one gated cycle: select the target (default this skill), snapshot the committed baseline, diagnose against skill-design-principles plus four rubric dimensions, apply exactly one focused change, and ship only if a different agent than the author PASSes with quoted evidence. Use when the user says "improve this skill", "improve-skill", "iterate on SKILL.md", or "apply skill-design-principles to a skill". Do not use for code slop over a duration (anneal), generic timeboxed work (timeboxed-iterating), graph orchestration (workgraph), metric hill-climb on a codebase (autoresearch), or stripping slop from existing prose (catalog-deslop).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Deliberate system-improvement loop — audit your own Claude Code setup (rules / memory / skills / MCP / agents / registry), evaluate incoming tools via /vet, adopt the worthwhile ones with discipline (backup → verify), and record every decision. Use when bringing in a tool/idea/collection to improve the setup, or when doing a tooling-improvement pass. Triggers — "upgrade the workflow", "audit our setup", "let's improve the tooling", "evaluate this for us", when handed a post/repo/reel with a tool.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

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

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.