888건 · 관련도순
Validate improvements from .turbo/improvements.md, recommend a working set tailored to what's in the backlog, and run one lane: direct fixes, investigation, or planned work. One lane per session. Use when the user asks to "implement improvements", "work on improvements", "address improvements", "process improvement backlog", "tackle improvements", or "implement noted improvements".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Triage the improvements backlog at .turbo/improvements.md: merge duplicate entries, state the user story or simplification behind each one, and decide with the user which to keep and which to drop. Use when the user asks to "triage improvements", "triage the backlog", "clean up the backlog", "clean up improvements", "groom the backlog", "prune the improvements backlog", or "go through the backlog with me".
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Capture an out-of-scope improvement opportunity so it doesn't get lost. Use when the user asks to "note improvement", "save improvement", "track this for later", "remember this improvement", "note this idea", "log improvement", "backlog this", or "park this idea". Also invoke proactively when noticing something improvable during work that falls outside the current task's scope, or after deliberately shipping a simpler approach that accepts a known ceiling — briefly mention it to the user and offer to note it.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Design loops that improve an agent’s own prompts, workflows, or execution code from failure records. Define bounded edits, preserve candidate histories, and accept changes through independent tests that check improvements and regressions.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Record concrete improvements in Paperclip when an agent encounters significant workflow friction. The skill turns observed problems with tools, permissions, environments, or documentation into brief internal feedback while keeping the main task moving.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
Run repeated code improvement experiments with multiple agents. It plans and reviews candidate changes, compares benchmark results, merges candidates that pass rechecking, and records progress until a target, iteration limit, or another stopping condition is reached.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
Find concrete LifeOS improvements in Anthropic updates, practitioner examples, and internal reflections. Check each candidate against the current setup, previous decisions, and compatibility, then present actionable recommendations tied to specific files or components.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
Measure code patterns such as casts to any or disabled lint rules, then run repeated, targeted edits. Keep changes that improve the chosen metric, revert unsuccessful changes, and track results for each improvement loop.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
This skill should be used when the user asks to "apply skill improvements", "update skill from plan", "execute improvement plan", "fix skill issues", "implement skill recommendations", or mentions applying improvements from quality review reports. Reads improvement-plan-{name}.md files generated by skill-quality-reviewer and intelligently merges and executes the suggested changes to improve Claude Skills quality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
This skill should be used when the user asks to "apply skill improvements", "update skill from plan", "execute improvement plan", "fix skill issues", "implement skill recommendations", or mentions applying improvements from quality review reports. Reads improvement-plan-{name}.md files generated by skill-quality-reviewer and intelligently merges and executes the suggested changes to improve Claude Skills quality.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Transform transcript analysis findings into actionable improvements. Triggers on "generate hooks from findings", "improve agent", "fix anti-pattern", "kaizen improvement", "generate hook proposals", or "create improvement plan". Provides templates for hook generation, agent prompt refinement, skill patches, CLAUDE.md updates, and script automation from analysis data.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Marp形式プレゼンテーションの専門レビュー・評価・改善スキル。 10軸スコアリング（100点満点）で品質を定量評価し、具体的な改善コード付きで提案する。 Use when the user asks to: - Review, evaluate, or critique a Marp presentation / プレゼンをレビュー / 評価 / 添削 - Improve, polish, or upgrade existing slides / スライドを改善 / ブラッシュアップ - Check slide quality, score slides, or audit a deck / スライドの品質チェック / 採点 - "このプレゼンどう？" "スライドを見てほしい" "プレゼン資料のフィードバック" Reads .md files with Marp frontmatter, scores across 10 dimensions, and outputs a detailed review report with concrete before/after code improvements. Works as a companion to the marp-slides skill (generation → review → improvement cycle).
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
AI 에이전트가 정확성·완전성·명확성·실행 가능성·간결성의 다섯 기준으로 결과물을 자체 평가합니다. 근거가 있는 점수와 평균, 우선순위별 개선안을 제공합니다.
Rank problems and proposed fixes using four scored criteria: expected benefit, implementation cost, deployment ease, and risk. The skill produces a sorted priority table with scoring rationales and explains why the top three items should come first.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Build an end-to-end evaluation pipeline for Python AI applications using pixie-qa and real language-model calls. Define criteria, prepare realistic test data, score application runs, and analyze the evidence to produce a prioritized improvement plan.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
Review AWS infrastructure code and deployed resources against the six Well-Architected pillars. Produce risk-ranked findings, remediation examples, and an architecture diagram, then create GitHub issues for tracking improvements after user confirmation.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
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 module relationships and change history to identify structural improvements in a codebase. Receive ranked candidates with file references, suggested changes, and risks, plus a recommendation for where to start, without modifying code.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.
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.
Build dashboards and reports for security operations teams using incident and alert data. Track detection and resolution times, false alarms, analyst workload, and detection coverage to assess operational trends and the effects of improvement initiatives.
한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.