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DanWeinbergerDanWeinberger
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Merge pull request #82 from PopHIVE/feat/claude-skills-mmr-epic
feat: add Claude skills and mmr_epic data source
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‎.claude/settings.local.json‎

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"Bash(cmd.exe /c \"Rscript -e \"\"dcf::dcf_check\\(\\)\"\"\")",
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"Bash(cmd.exe /c \"cd /d \"\"c:\\\\Users\\\\DMW63\\\\OneDrive - Yale University\\\\Desktop\\\\R_Projects\\\\POPHIVE PROJECT\\\\ingest\"\" && \"\"C:\\\\Program Files\\\\R\\\\R-4.3.0\\\\bin\\\\Rscript.exe\"\" -e \"\"dcf::dcf_check\\(\\)\"\"\")",
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"Bash(\"C:/Program Files/R/R-4.4.2/bin/Rscript.exe\" -e \"dcf::dcf_add_source\\(''schoolvax_washpost''\\)\")",
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"Bash(gzip:*)"
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"Bash(gzip:*)",
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"Bash(git checkout:*)",
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"Bash(git stash:*)"
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]
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}
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}

‎.claude/skills/add-source.md‎

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# add-source
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Add a new data source to the PopHIVE/Ingest repository.
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## Usage
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```
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/add-source <source_name>
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```
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## Description
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Creates a new data source folder structure using `dcf::dcf_add_source()`. This initializes all required files and directories for a new data ingestion pipeline.
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## Instructions
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When the user invokes this skill with a source name:
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1. **Validate the source name**:
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- Must be lowercase with underscores (e.g., `cdc_flu_data`, `epic_diabetes`)
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- No spaces or special characters
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- Should be descriptive of the data source
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2. **Detect the R installation** (Windows only):
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First, find available R versions:
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```bash
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powershell -Command "Get-ChildItem 'C:\Program Files\R' | Select-Object Name"
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```
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Use the most recent version found (e.g., `R-4.3.0`).
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3. **Run the dcf command** from the project root:
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```r
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dcf::dcf_add_source("<source_name>")
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```
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**On Windows**, use PowerShell with the detected R version:
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```bash
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cd "<project_root>" && powershell -Command "& 'C:\Program Files\R\<R_VERSION>\bin\Rscript.exe' -e \"dcf::dcf_add_source('<source_name>')\""
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```
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Replace `<R_VERSION>` with the detected version (e.g., `R-4.3.0`).
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**On macOS/Linux**, use:
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```bash
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cd "<project_root>" && Rscript -e 'dcf::dcf_add_source("<source_name>")'
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```
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4. **Verify the created structure**:
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The command should create:
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```
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data/<source_name>/
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├── raw/ # For downloaded source files
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├── standard/ # For standardized output files
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├── ingest.R # Transformation script (needs editing)
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├── measure_info.json # Variable metadata (needs editing)
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└── process.json # Processing state (auto-generated)
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```
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5. **Report success** and remind the user of next steps:
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- Edit `ingest.R` to implement the data transformation
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- Edit `measure_info.json` to document variables
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- Follow the templates in CLAUDE.md
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## Example
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User: `/add-source wastewater_influenza`
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Response: Create the data source folder, then provide guidance on next steps.

‎.claude/skills/commit-push-pr.md‎

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# Commit, Push, and Create PR
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Automate the git workflow for completing a feature or fix.
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## Usage
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```
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/commit-push-pr [optional commit message]
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```
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## Pre-computed Context
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Before proceeding, gather this information:
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- Current branch: `git branch --show-current`
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- Git status: `git status --short`
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- Recent commits on this branch: `git log --oneline -5`
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- Diff summary: `git diff --stat`
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## Workflow
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1. **Review Changes**
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- Check `git status` for all modified/added files
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- Review the diff to understand what's being committed
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- Ensure no sensitive files are staged (.env, credentials, etc.)
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2. **Run Pre-commit Checks** (if applicable)
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- For R files: Check syntax with `Rscript -e "parse(file='<file>')"`
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- Verify data files are compressed (.csv.gz, .parquet)
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3. **Stage and Commit**
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- Stage relevant files: `git add <files>`
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- Create a commit with Conventional Commits format:
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- `feat:` for new features (new data sources, new bundles)
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- `fix:` for bug fixes (data corrections, pipeline fixes)
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- `docs:` for documentation (CLAUDE.md, README updates)
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- `refactor:` for refactoring (code cleanup)
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- `data:` for data updates (new raw data, re-processed outputs)
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- `chore:` for maintenance (dependency updates)
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- Write a clear, concise commit message focusing on "why"
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- Include co-author line: `Co-Authored-By: Claude <noreply@anthropic.com>`
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4. **Push to Remote**
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- Push the branch: `git push -u origin HEAD`
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- If branch doesn't exist on remote, create it
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5. **Create Pull Request**
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- Use GitHub CLI: `gh pr create`
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- Include:
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- Clear title summarizing the change
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- Description with summary and context
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- Reference any related issues
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- Format PR body with:
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```
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## Summary
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<1-3 bullet points>
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## Test plan
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[Bulleted checklist of testing steps]
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🤖 Generated with [Claude Code](https://claude.ai/code)
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```
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## Arguments
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Pass a commit message or leave empty for auto-generated message based on changes.
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Example: `/commit-push-pr feat: add mmr_epic data source`
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## Output
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Return the PR URL when complete.

‎data/mmr_epic/README.md‎

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# mmr_epic
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This is a dcf data source project, initialized with `dcf::dcf_add_source`.
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You can us the `dcf` package to check the project:
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```R
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dcf_check_source("mmr_epic", "..")
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```
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And process it:
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```R
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dcf_process("mmr_epic", "..")
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```

‎data/mmr_epic/ingest.R‎

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#
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# Download
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#
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# add files to the `raw` directory
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#
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# Reformat
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#
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# read from the `raw` directory, and write to the `standard` directory
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‎data/mmr_epic/measure_info.json‎

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{
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"example_variable": {
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"id": "example_variable",
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"short_name": "",
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"long_name": "",
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"category": "",
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"short_description": "",
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"long_description": "",
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"statement": "",
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"measure_type": "",
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"unit": "",
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"time_resolution": "",
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"restrictions": "",
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"sources": [],
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"citations": []
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}
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}

‎data/mmr_epic/process.json‎

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{
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"name": "mmr_epic",
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"type": "source",
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"scripts": [
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{
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"path": "ingest.R",
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"manual": false,
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"frequency": 0,
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"last_run": "",
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"run_time": "",
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"last_status": {
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"log": "",
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"success": true
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}
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}
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],
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"checked": "",
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"check_results": []
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}

‎data/mmr_epic/standard/datapackage.json‎

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{
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"name": "mmr_epic",
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"title": "Mmr Epic",
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"licence": {
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"url": "http://opendatacommons.org/licenses/pddl",
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"name": "Open Data Commons Public Domain",
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"version": "1.0",
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"id": "odc-pddl"
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},
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"resources": []
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}

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