publish-study

name	publish-study
description	Publish a completed study to GitHub - creates a repo with the analysis report as README.md, all charts, data, and a reproducibility footer linking to Expected Parrot
allowed-tools	Read, Glob, Grep, Bash(python3:*), Bash(gh *), Bash(git *), Bash(cp *), Bash(mkdir *), Write, AskUserQuestion
user-invocable	true
arguments	study_dir

Publish Study

Takes a completed study directory (containing results, an experiment design, and an analysis report) and publishes it as a GitHub repository. The analysis report becomes README.md so it renders nicely on GitHub.

Usage

/publish-study
/publish-study 2026-02-08_do-llms-exhibit-maternal-default-bias-when

If no study directory is specified, auto-detect from the current working directory by looking for directories matching YYYY-MM-DD_*.

Workflow

1. Locate the Study Directory

If a directory was provided, confirm it exists. Otherwise, find candidates:

import glob
import os

candidates = sorted(glob.glob("./[0-9][0-9][0-9][0-9]-[0-9][0-9]-[0-9][0-9]_*"), key=os.path.getmtime, reverse=True)
candidates = [d for d in candidates if os.path.isdir(d)]

• If zero candidates, tell the user no study directories were found.
• If exactly one, use it automatically.
• If multiple, use AskUserQuestion to let the user pick:

Question: "Which study do you want to publish?"
Header: "Study"
Options: [list each directory as an option with its name as label and first line of experiment_design.md as description]

2. Select the Analysis

Find analysis_N/ subdirectories that contain report.md:

import os, glob

analysis_dirs = sorted(glob.glob(f"{study_dir}/analysis_*/"))
with_reports = [d for d in analysis_dirs if os.path.isfile(os.path.join(d, "report.md"))]

• If zero, tell the user to run /analyze-results first.
• If exactly one, use it automatically.
• If multiple, use AskUserQuestion to let the user pick which analysis to feature as the README:

Question: "Which analysis should be the primary report (README.md)?"
Header: "Analysis"
Options: [list each analysis_N with first line of its report.md as description]

3. Ensure Results Are on Expected Parrot

Check for results.json.gz in the study directory and push it to Expected Parrot so the repo can link to it for reproducibility.

from edsl import Results

results = Results.load(f"{study_dir}/results")  # loads results.json.gz

# Push to Expected Parrot (unlisted by default)
info = results.push(description="Results for: <study_slug>")

# Extract the UUID and URL from the push response
# info is a dict with 'uuid' and 'url' keys
results_uuid = info["uuid"]
results_url = info["url"]
print(f"Results pushed: {results_url}")
print(f"UUID: {results_uuid}")

If the push fails or results.json.gz doesn't exist, warn the user but continue — the reproducibility section will note that results are not available on Expected Parrot.

4. Stage Repo Contents

Build the repository contents in a temporary directory. Copy files from the study and analysis directories:

# Create temp staging directory
mkdir -p /tmp/publish-study-staging
mkdir -p /tmp/publish-study-staging/images
mkdir -p /tmp/publish-study-staging/data
mkdir -p /tmp/publish-study-staging/questions
mkdir -p /tmp/publish-study-staging/study

File Mapping

Source	Destination in Repo	Notes
analysis_N/report.md	README.md	Rewrite image/data paths, append reproducibility footer
analysis_N/*.png	images/*.png	All chart PNGs from the analysis
study_*.py	study/study_*.py	All EDSL component files (survey, agents, models, scenarios)
create_results.py	study/create_results.py	Results runner script
analyze_*.py	study/analyze_*.py	Analysis scripts
Makefile	study/Makefile	Build system
design_spec.json	study/design_spec.json	Experimental design spec
conjoint_choice_sets.json	study/conjoint_choice_sets.json	Choice sets (if conjoint)
*.md (top-level, not README)	study/*.md	Design docs (conjoint_design.md, experiment_design.md)
analysis_N/survey.md	survey.md	Survey documentation
analysis_N/results.csv	data/results.csv	Tabular results
analysis_N/<slug>/answer.md	questions/<slug>.md	Answer-question outputs (if any)
analysis_N/<slug>/*.png	questions/<slug>/*.png	Answer charts (if any)
—	LICENSE	MIT license (generated)

Copy Commands

STUDY_DIR="<study_dir>"
ANALYSIS_DIR="<analysis_dir>"
STAGING="/tmp/publish-study-staging"

# Clean staging area
rm -rf "$STAGING"
mkdir -p "$STAGING/images" "$STAGING/data" "$STAGING/questions" "$STAGING/study"

# Copy analysis PNGs to images/
cp "$ANALYSIS_DIR"/*.png "$STAGING/images/" 2>/dev/null || true

# Copy all study Python files into study/ directory
cp "$STUDY_DIR"/study_*.py "$STAGING/study/" 2>/dev/null || true
cp "$STUDY_DIR"/create_results.py "$STAGING/study/" 2>/dev/null || true
cp "$STUDY_DIR"/analyze_*.py "$STAGING/study/" 2>/dev/null || true

# Copy Makefile
cp "$STUDY_DIR"/Makefile "$STAGING/study/" 2>/dev/null || true

# Copy JSON design files
cp "$STUDY_DIR"/design_spec.json "$STAGING/study/" 2>/dev/null || true
cp "$STUDY_DIR"/conjoint_choice_sets.json "$STAGING/study/" 2>/dev/null || true

# Copy design/experiment docs (top-level .md files, excluding README)
for md in "$STUDY_DIR"/*.md; do
  [ -f "$md" ] && cp "$md" "$STAGING/study/" 2>/dev/null || true
done

# Copy survey documentation
cp "$ANALYSIS_DIR/survey.md" "$STAGING/survey.md" 2>/dev/null || true

# Copy results CSV
cp "$ANALYSIS_DIR/results.csv" "$STAGING/data/results.csv" 2>/dev/null || true

Copy Answer-Question Outputs

Check for subdirectories inside the analysis directory that contain answer.md:

import glob, os, shutil

staging = "/tmp/publish-study-staging"
analysis_dir = "<analysis_dir>"

answer_dirs = [d for d in glob.glob(f"{analysis_dir}/*/")
               if os.path.isfile(os.path.join(d, "answer.md"))]

for answer_dir in answer_dirs:
    slug = os.path.basename(answer_dir.rstrip("/"))
    dest = f"{staging}/questions/{slug}"
    os.makedirs(dest, exist_ok=True)

    # Copy answer.md as <slug>.md
    shutil.copy2(f"{answer_dir}/answer.md", f"{staging}/questions/{slug}.md")

    # Copy any PNGs
    for png in glob.glob(f"{answer_dir}/*.png"):
        os.makedirs(f"{dest}", exist_ok=True)
        shutil.copy2(png, f"{dest}/{os.path.basename(png)}")

5. Generate README.md

Read the original report.md and apply three modifications:

import re

with open(f"{analysis_dir}/report.md", "r") as f:
    readme = f.read()

# 1. Rewrite image paths: (foo.png) -> (images/foo.png)
#    Match markdown image syntax ![desc](filename.png)
readme = re.sub(
    r'(!\[[^\]]*\])\(([^/)][^)]*\.png)\)',
    r'\1(images/\2)',
    readme
)

# 2. Rewrite results.csv links: (results.csv) -> (data/results.csv)
readme = readme.replace("(results.csv)", "(data/results.csv)")

# 3. Append reproducibility footer
footer = f"""

---

## Reproducibility

This study was conducted using [Expected Parrot EDSL](https://docs.expectedparrot.com/).

### Results on Expected Parrot

The full results object is available on Expected Parrot:

- **URL:** [{results_url}]({results_url})
- **UUID:** `{results_uuid}`

### Pull the Results

```python
from edsl import Results

results = Results.pull("{results_uuid}")
df = results.to_pandas()

Study Code

The full study code is available in the study/ directory, including:

• EDSL component definitions (survey, agents, models, scenarios)
• Results runner script
• Makefile for reproducibility
• Experimental design specification

License

This study and its artifacts are released under the MIT License.

---

Generated with Expected Parrot EDSL """

readme += footer

with open(f"{staging}/README.md", "w") as f: f.write(readme)

If the EP push failed (no `results_uuid`), use a simpler footer without the EP link:

```python
footer_no_ep = """

---

## Reproducibility

This study was conducted using [Expected Parrot EDSL](https://docs.expectedparrot.com/).

### Study Code

The full study code is available in the [`study/`](study/) directory, including:
- EDSL component definitions (survey, agents, models, scenarios)
- Results runner script
- Makefile for reproducibility
- Experimental design specification

### License

This study and its artifacts are released under the [MIT License](LICENSE).

---

*Generated with [Expected Parrot EDSL](https://docs.expectedparrot.com/)*
"""

6. Generate LICENSE

Write a standard MIT license file:

from datetime import datetime

year = datetime.now().year

license_text = f"""MIT License

Copyright (c) {year}

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
"""

with open(f"{staging}/LICENSE", "w") as f:
    f.write(license_text)

7. Ask User for Repo Details

Use AskUserQuestion to confirm the repo name, visibility, and GitHub account:

Question: "What should the GitHub repo be named?"
Header: "Repo name"
Options:
  1. "<study-slug>" - "Default: slug from the study directory name (without date prefix)"
  2. "Custom name" - "Enter a custom repository name"

Then ask about visibility:

Question: "Should the repo be public or private?"
Header: "Visibility"
Options:
  1. "Public (Recommended)" - "Anyone can see this repository"
  2. "Private" - "Only you and collaborators can see this repository"

The study slug is derived from the directory name by stripping the date prefix:

# "2026-02-08_do-llms-exhibit-maternal-default-bias-when" -> "do-llms-exhibit-maternal-default-bias-when"
study_slug = os.path.basename(study_dir).split("_", 1)[1] if "_" in os.path.basename(study_dir) else os.path.basename(study_dir)

8. Create GitHub Repo and Push

REPO_NAME="<repo_name>"
VISIBILITY="--public"  # or "--private"
STAGING="/tmp/publish-study-staging"

# Create the GitHub repo
gh repo create "$REPO_NAME" $VISIBILITY --description "Research study: <short description>"

# Initialize git in the staging directory and push
cd "$STAGING"
git init
git add .
git commit -m "Initial commit: publish study results

Generated with Expected Parrot EDSL
Co-Authored-By: Claude Opus 4.6 <[e-mail]>"

# Add remote and push
git remote add origin "$(gh repo view "$REPO_NAME" --json url -q .url)"
git branch -M main
git push -u origin main

9. Report Back

Tell the user:

• The GitHub repo URL
• A summary of what was published (number of images, whether results are on EP, etc.)
• The direct link to the README (which is the repo homepage)

Question: (none — just report)

Example output:

Published to: https://github.com/<user>/demand-and-price-sensitivity-for-frozen-chicken

Repository contents:
- README.md (analysis report with 3 charts)
- survey.md
- study/ (11 files: Python scripts, Makefile, design specs, docs)
- data/results.csv
- images/ (3 PNGs)
- LICENSE (MIT)

Results are also available on Expected Parrot:
  https://expectedparrot.com/content/<uuid>

Example: Publishing a Conjoint Study

Given a study directory:

2026-02-11_demand-and-price-sensitivity-for-frozen-chicken/
├── experiment_design.md
├── conjoint_design.md
├── design_spec.json
├── conjoint_choice_sets.json
├── study_survey.py
├── study_agent_list.py
├── study_model_list.py
├── study_scenario_list.py
├── create_results.py
├── analyze_conjoint.py
├── Makefile
├── results.json.gz
├── results.csv
└── analysis_1/
    ├── report.md
    ├── report.html
    ├── survey.md
    ├── results.csv
    ├── part_worth_utilities.png
    ├── attribute_importance.png
    └── segment_analysis.png

The published repo would contain:

demand-and-price-sensitivity-for-frozen-chicken/
├── README.md                        # report.md with rewritten paths + reproducibility footer
├── survey.md
├── LICENSE
├── data/
│   └── results.csv
├── images/
│   ├── part_worth_utilities.png
│   ├── attribute_importance.png
│   └── segment_analysis.png
└── study/
    ├── study_survey.py
    ├── study_agent_list.py
    ├── study_model_list.py
    ├── study_scenario_list.py
    ├── create_results.py
    ├── analyze_conjoint.py
    ├── Makefile
    ├── design_spec.json
    ├── conjoint_choice_sets.json
    ├── experiment_design.md
    └── conjoint_design.md

Checklist Before Publishing

• Study directory exists and contains results.json.gz
• At least one analysis_N/ directory has a report.md
• study/ directory contains Python files (study_*.py, create_results.py, etc.)
• study/ directory contains design docs (experiment_design.md, design_spec.json, etc.)
• All image paths in README.md point to images/
• results.csv link points to data/results.csv
• Reproducibility footer includes EP link (if push succeeded)
• LICENSE file is present
• GitHub repo was created successfully
• All files were committed and pushed

Output

File	Description
README.md	Analysis report adapted for GitHub with image paths rewritten and reproducibility footer
study/study_*.py	EDSL component definitions (survey, agents, models, scenarios)
study/create_results.py	Results runner script
study/analyze_*.py	Analysis scripts
study/Makefile	Build system for reproducibility
study/design_spec.json	Experimental design specification
study/conjoint_choice_sets.json	Choice set definitions (if conjoint)
study/*.md	Design docs (experiment_design.md, conjoint_design.md)
survey.md	Survey documentation (questions, types, options)
data/results.csv	Tabular results data
images/*.png	All analysis chart PNGs
questions/<slug>.md	Answer-question outputs (if any)
questions/<slug>/*.png	Answer-question charts (if any)
LICENSE	MIT license

Tips

• Run /analyze-results before /publish-study — you need at least one analysis with a report.md
• If you want to refine the report before publishing, edit analysis_N/report.md first
• The repo name defaults to the study slug (directory name without date prefix)
• Results are pushed to Expected Parrot as "unlisted" by default — change visibility on EP separately if needed
• If gh CLI is not authenticated, run gh auth login first