281건(Jamie-BitFlight의 저장소) · 인기순
Use when selecting a Mermaid diagram type, looking up Mermaid syntax, adapting a Mermaid example, or locating a Mermaid reference for a flowchart, sequence, UML, architecture, chart, or other diagram.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assess review findings against the target's governing contract, evidence, and consequence without imposing a lens-specific verdict, schema, or gate. Use when classifying a candidate concern in a code or change review.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Decide whether to adopt, adapt, or justify building a new script, tool, utility, library, module, dependency, or bespoke capability. Use in task planning, worker execution (including tooling discovered after planning), or code review when an implementation choice may duplicate an existing solution.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Ensemble SOLID review — fans the ruleset across overlapping focused workers over the same code, then reduces by corroboration.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user explicitly asks Rewrite Room to route documentation work, or when audit/sync/freshness, user-facing authoring, citation-driven writing, docs-to-skill conversion, and AI-instruction optimization overlap and exactly one workflow must be chosen.
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When an application needs to store config, data, cache, or state files. When designing where user-specific files should live. When code writes to ~/.appname or hardcoded home paths. When implementing cross-platform file storage with platformdirs.
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Use when the primary outcome is creating a new portable Agent Skill directory from one local documentation file, documentation directory, or Git repository, including mixed text and capability-gated binary sources that require exhaustive source-to-output accounting.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Relay agent results without corrupting exact counts, failure reasons, source scope or status. Use when receiving agent output or handing results to another actor; distinguish observations from attributed conclusions and reference complete artifacts.
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Route requests to summarize files, URLs, images, inline text, or multiple sources. Select the requested format and preserve evidence, exact counts, uncertainty and source coverage through summarization, synthesis and agent-result relay.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the primary outcome is comparing documentation claims with implementation evidence, synchronizing docs from verified changes, or freshness review, including drift, missing coverage, stale claims, and post-change updates.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the user asks what to do next, asks for a plan under uncertainty, challenges stalled or low-trust work, requests multiple possible approaches before action, or needs an evidence-first loop that uses RT-ICA-style prerequisite checks, adversarial review, subagent coordination, real validation, and durable concern tracking.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Assess and address review feedback on GitHub PRs, GitLab MRs, or in conversation. Use when checking new reviews, responding to comments, rechecking after a push, or following up on approvals and change requests.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Describe images, screenshots, diagrams and charts from visual inspection. Preserve visible text, counts, labels, directions and uncertainty; distinguish observation from interpretation. Use for image summaries, screenshot explanations and diagram descriptions.
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Reference guide for the Twelve-Factor App methodology — 15 principles (12 original + 3 modern extensions) for building portable, resilient, cloud-native applications. Use when evaluating application architecture, designing cloud-native services, reviewing codebases for methodology compliance, advising on configuration, scaling, observability, security, and deployment patterns. Incorporates the 2025 open-source community evolution and cloud-native reinterpretations of each factor.
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Summarize a supplied URL from fetched content with source locations, acquisition coverage and explicit access failures. Use for articles, documentation, API references and web pages; preserve version, author attribution and uncertainty in the requested output format.
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Summarize files or a requested directory scope from actual contents. Extract code, config, data and document evidence, preserve exact counts and source locations, and disclose incomplete or unsupported acquisition. Use for file summaries, code explanations and configuration overviews.
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Use when creating or backfilling AI-categorized daily GitHub releases from branch commits.
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Integrate multiple source findings into an attributed synthesis or comparison. Use for combine these summaries, synthesize results, merge findings or multi-source analysis. Preserve claim-level provenance, coverage, qualifiers, conflicts and uncertainty rather than repeatedly compressing narrative summaries.
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Use when the primary outcome is authoring, rewriting, summarizing, or validating user-facing documentation without implementation comparison or required source attribution, including READMEs, tutorials, API docs, GitLab Markdown, source-faithful summaries, and audience-specific prose.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Use when the primary outcome is creating publishable source-attributed content from URLs or supplied material, including research summaries, articles, briefs, and posts whose factual claims and quotations need reader-visible, verifiable citations.
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Illustrative Claude Code skill configuration. Use when learning the basic SKILL.md shape; follow the canonical runtime reference for the current field inventory.
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Enforce mandatory pre-action verification checkpoints to prevent pattern-matching from overriding explicit reasoning. Use this skill when about to execute implementation actions (Bash, Write, Edit) to verify hypothesis-action alignment. Blocks execution when hypothesis unverified or action targets different system than hypothesis identified. Critical for preventing cognitive dissonance where correct diagnosis leads to wrong implementation.
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Use when the primary outcome is refining an existing AI-facing artifact without dropping behavior, including SKILL.md, AGENTS.md, CLAUDE.md, rules, prompts, and agent definitions that need sharper invocation, structure, or completion criteria.
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Single-pass SOLID review — dispatches one reviewer over the complete ruleset, emitting the fixed candidate schema.
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