From Claude Code for Social Scientists
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.
How this skill is triggered — by the user, by Claude, or both
Slash command
/social-cc-plugin:agent-portability-matrixThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
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 simp...
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.
npx claudepluginhub onourimpram/claude-code-for-social-scientists --plugin social-cc-pluginGuides completion of development work by verifying tests, detecting environment, and presenting structured options for merge, PR, or cleanup.
Guides creation and editing of skills using test-driven development with pressure scenarios and subagents to verify agent compliance.
Dispatches multiple subagents concurrently for independent tasks without shared state. Use when facing 2+ unrelated failures or subsystems that can be investigated in parallel.