القائمة
Write an AsPredicted pre-registration for a study. Walk through each field with the user, then produce a complete pre-registration document.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | aspredicted |
| description | Write an AsPredicted pre-registration for a study. Walk through each field with the user, then produce a complete pre-registration document. |
When this skill is triggered, walk the user through writing an AsPredicted pre-registration. Go field by field, drafting each section based on what you know about the study from context. Present a draft for each field and let the user revise before moving on. At the end, compile the full pre-registration into a clean document.
Have any data been collected for this study already?
What is the main question being asked or hypothesis being tested in this study?
Describe the key dependent variable(s), specifying how they will be measured.
How many and which conditions will participants be assigned to?
Specify exactly which analyses you will conduct to examine the main question/hypothesis.
Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
How many observations will be collected or what will determine sample size?
Anything else you would like to pre-register?
Give a title for this AsPredicted pre-registration.
After all fields are finalized, compile into a single clean markdown document saved to the project directory as aspredicted_<short-name>.md. Use concise, precise scientific language. Avoid hedging or filler.
End-of-data-collection workflow. Exports raw MongoDB data as JSONL, writes a companion README documenting schema + provenance, commits both to the repo, and archives the raw dataset to Zenodo with a permanent DOI. Use when the user says "data collection is done", "freeze the data", "archive the raw data", "post the raw data to Zenodo", "collection is complete", or is closing out a study.
Build a Qualtrics survey from specs using the qualtrics_sdk Python package. Give a description of your survey (questions, blocks, logic, embedded data) and this skill generates and runs a Python script to create it on Qualtrics.
Interact with Google Workspace (Drive, Docs, Sheets, Gmail, Calendar, etc.) using the `gws` CLI. Use this skill when the user wants to read, create, edit, or search Google Docs, Sheets, Drive files, or any Google Workspace resource.
This is default workflow for data analysis. It includes steps for data cleaning, exploration, and visualization.
End-of-session wrap-up — updates todo.md, comms.md, and current_state.md with session summary, commits/pushes if git repo, and creates a handoff briefing for the next session. Use when the user says "log off", "wrap up", "call it a night", "done for today", or is ending a work session.
Send the user an iMessage notification when a long-running task finishes. Use after any multi-step or long-running task, especially one the user has stepped away from.
