Use when a Claude Code session loops, drifts from scope, claims a fix that CI rejects, hits context, PATH, or permission walls, or fails in a way another retry will not solve and a root cause diagnosis is needed instead.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a complete research workflow — its instructions, skills, data boundaries, and tool connections — must be assessed for portability across AI agents or environments, so each component is explicitly mapped before migration rather than assumed to transfer.
インストールする前に、エージェントに与えられる指示の中身を確認できます。
Use this skill when a research workflow built in one AI environment — Claude Code, Codex, or any other agent — needs to move to, or be validated against, a second environment: before migration, when evaluating whether an existing skill set will work in a new tool, or when a release checkpoint requires a cross-tool audit of instructions, data boundaries, and permission models. It is not for simply copying a CLAUDE.md file into a new tool and assuming compatibility, and it is not for substituting a portability check for a scientific reproducibility review — those are separate concerns. After a portability audit, hand the verified workflow to cross-agent-second-opinion if a substantive output from the new environment needs independent verification, to mcp-research-stack-triage if MCP server configuration is the migration blocker, to research-lifecycle-pipeline for lifecycle orientation, and to agentic-session-debugger if the destination tooling itself misbehaves during the transition.
Return:
Changing tools does not reset working discipline — every discipline element must be verified in the new environment before it is trusted. The destination agent is not a copy of the source agent; equivalent functions must be tested separately. Two environments agreeing on a portability decision is not evidence the destination environment is correctly configured. Sensitive data, raw interview transcripts, and student-identifying material pass through sensitive-data-anonymization-gate before any AI tool sees them, including the destination agent. Keep AI contribution visible: record the migration stage, the source and destination models, the contribution level, and the human review step.
"Bu Claude Code projesini başka bir ajan ortamına taşımak istiyorum. Talimat, skill, veri sınırı, izin modeli, MCP bağlantıları, repo yapısı ve beyan açısından taşınabilirlik matrisi hazırla."
Expected smoke output:
Bu beceri, bir yapay zekâ ortamında kurulmuş çalışma düzenini başka bir araç ya da ajana taşımadan önce her bileşeni tek tek denetler. Kalıcı talimatlar, veri klasörleri, skill listesi, araç bağlantıları ve beyan şablonları kaynak ortamdaki haliyle değil, hedef ortamda gerçekten çalışıp çalışmadığı sorusuyla ele alınır. Araç değişimi disiplini sıfırlamaz ama her disiplin öğesinin yeni ortamda yeniden doğrulanması gerekir. Hassas veriler, ham mülakat dökümleri ve kimlik içeren materyaller hedef araca girmeden önce sensitive-data-anonymization-gate kapısından geçirilerek anonimleştirme sürecinden geçirilir. İki ortamın taşınabilirlik kararında mutabık kalması, hedef ortamın doğru yapılandırıldığının kanıtı değildir — ortak hata her zaman mümkündür. Taşınabilirlik kararı insan araştırmacıya aittir — beceri karar taslağı üretir, uyumu garanti etmez. Büyük uyuşmazlıklar ya da yeni ortamın ilk çıktılarına güvensizlik durumunda cross-agent-second-opinion'a devredin, araç yapılandırması sorun yaratıyorsa mcp-research-stack-triage'a başvurun.
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
Use when a Claude Code session loops, drifts from scope, claims a fix that CI rejects, hits context, PATH, or permission walls, or fails in a way another retry will not solve and a root cause diagnosis is needed instead.
日本語の概要は準備中です。原文の説明を表示しています。
Use when AI contribution metadata must match the actual workflow, when model alias and dated identifiers need auditing, when verified and fabricated citation counts feed a release decision, or when a venue's AI disclosure statement needs evidence behind it.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a draft in Turkish, English, or both reads as AI-generated and needs revision that removes machine writing patterns and translation calques while preserving meaning, citations, statistics, and the author's voice.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a reference list needs APA 7 cleanup, when DOIs must be confirmed real against Crossref or PubMed, when the user asks whether AI-suggested citations actually exist, or when a manuscript needs a citation hygiene pass before submission or defense.
日本語の概要は準備中です。原文の説明を表示しています。
Use when authorship order, CRediT contribution roles, or AI-assistance attribution must be recorded transparently for a manuscript, when an author dispute needs an evidence trail, or before a contributorship statement is required at submission; this skill maintains the human-attribution ledger of who contributed what and in which role (authorship order, CRediT taxonomy, revision responsibilities), which is distinct from the disclosure-metadata audit owned by ai-disclosure-auditor.
日本語の概要は準備中です。原文の説明を表示しています。
Use when paired tr.md and en.md files need a parity verdict, when titles, frontmatter, headings, or DOI sets may have drifted between languages, or when a bilingual document is approaching release and its translation coverage needs an audit.
日本語の概要は準備中です。原文の説明を表示しています。