refactor/ create architecture for package by mandylan75 · Pull Request #62 · Imageomics/repo-exporter

mandylan75 · 2026-06-30T16:39:37Z

__about__.py: Package version string (version = "2.0.0").
__init__.py: Package entry point, exposes GitHubExporter, HuggingFaceExporter, and __version__ for external imports.
base.py: BaseExporter abstract base class. Holds all logic shared between GitHub and Hugging Face: shared utilities (is_inactive, extract_display_name, ensure_string_value), Google Sheets helpers (_get_sheet, _build_batch_body, _write_batch, _apply_conditional_formatting), and the run() method that both platform exporters inherit. Sheets logic lives directly in base.py rather than a separate sheets.py, since the helpers are tightly coupled to BaseExporter's state.
github.py: GitHubExporter(BaseExporter). GitHub-specific repo fetching and metadata logic (contributors, DOI, license, package requirement files, website/paper/dataset/model detection from README) and GH-specific sheet column/color config.
huggingface.py: HuggingFaceExporter(BaseExporter). Hugging Face-specific repo fetching (models/datasets/spaces) and metadata logic (author, contributors, license, DOI, associated models/spaces, README field extraction) plus HF-specific sheet column/color config.
__main__.py: CLI entry point with argparse support (--platform, --token, --org, --spreadsheet-id, --sheet-name, --credentials-path, --repo-type); builds and runs the appropriate exporter based on --platform, with CLI args overriding .env values.

Copilot

Pull request overview

Introduces a new modular repo_exporter package architecture that consolidates shared logic between the GitHub and Hugging Face exporters into a common base class and provides a unified CLI entry point.

Changes:

Added BaseExporter with shared utilities, Google Sheets write helpers, and common run() orchestration.
Implemented GitHubExporter and HuggingFaceExporter subclasses for platform-specific fetching/metadata + sheet formatting.
Added a CLI (main.py) and package entry points (__init__.py, __about__.py) for external imports and versioning.

Reviewed changes

Copilot reviewed 6 out of 6 changed files in this pull request and generated 5 comments.

Show a summary per file

File	Description
src/repo_exporter/base.py	Adds shared exporter base class, Sheets helpers, and run orchestration.
src/repo_exporter/github.py	Adds GitHub-specific exporter implementation and metadata extraction.
src/repo_exporter/huggingface.py	Adds Hugging Face-specific exporter implementation and metadata extraction.
src/repo_exporter/main.py	Adds CLI entry point and `export_repos()` dispatcher.
src/repo_exporter/init.py	Exposes exporters and `__version__` as the package API surface.
src/repo_exporter/about.py	Defines the package version string.

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egrace479

This is a HUGE PR, and you've done great. Preliminary feedback on the structuring.

Co-authored-by: Elizabeth Campolongo <38985481+egrace479@users.noreply.github.com>

Copilot

Pull request overview

Copilot reviewed 6 out of 6 changed files in this pull request and generated 12 comments.

+    def get_column_index(self, header:list, col_name: str):
+            try:
+                return header.index(col_name)
+            except ValueError:
+                return None


+        try:
+            if repo.get_readme():
+                return "Yes"
+        except GithubException:
+            return "No"
+


+        try:
+            if repo.get_license():
+                return "Yes"
+        except GithubException:
+            return "No"
+


Copilot

Pull request overview

Copilot reviewed 6 out of 6 changed files in this pull request and generated 11 comments.

egrace479 · 2026-08-12T22:27:31Z

+        rules = []
+        for col_set, color in [
+            (red_columns, {"red": 1, "green": 0.5, "blue": 0.5}),
+            (secondary_columns, secondary_color),
+        ]:
+            for col_name in col_set:
+                col_index = self.get_column_index(header, col_name)
+                if col_index is None:
+                    continue
+                rules.append({
+                    "addConditionalFormatRule": {
+                        "rule": {
+                            "ranges": [{
+                                "sheetId": sheet.id,
+                                "startRowIndex": HEADER_ROW_INDEX,
+                                "endRowIndex": HEADER_ROW_INDEX + len(df),
+                                "startColumnIndex": col_index,
+                                "endColumnIndex": col_index + 1,
+                            }],
+                            "booleanRule": {
+                                "condition": {
+                                    "type": "TEXT_EQ",
+                                    "values": [{"userEnteredValue": "No"}],
+                                },
+                                "format": {"backgroundColor": color},
+                            },
+                        },
+                        "index": 0,
+                    }
+                })
+
+        sheet.spreadsheet.batch_update({"requests": rules})


@copilot, please don't commit anything but suggest a fix.

Here's a suggested fix that deletes all existing conditional format rules for the sheet before adding new ones. This prevents rule accumulation across runs.

def _apply_conditional_formatting( self, sheet, header: list[str], df: pd.DataFrame, red_columns: set[str], secondary_columns: set[str], secondary_color: dict, ) -> None: HEADER_ROW_INDEX = 2 spreadsheet = sheet.spreadsheet sheet_id = sheet.id # Delete all existing conditional format rules for this sheet to prevent accumulation existing_rules = spreadsheet.fetch_sheet_metadata() sheets_meta = existing_rules.get("sheets", []) delete_requests = [] for s in sheets_meta: if s["properties"]["sheetId"] == sheet_id: num_rules = len(s.get("conditionalFormats", [])) # Delete from last to first to keep indices stable for i in range(num_rules - 1, -1, -1): delete_requests.append({ "deleteConditionalFormatRule": { "sheetId": sheet_id, "index": i, } }) break # Build new rules new_rules = [] for col_set, color in [ (red_columns, {"red": 1, "green": 0.5, "blue": 0.5}), (secondary_columns, secondary_color), ]: for col_name in col_set: col_index = self.get_column_index(header, col_name) if col_index is None: continue new_rules.append({ "addConditionalFormatRule": { "rule": { "ranges": [{ "sheetId": sheet_id, "startRowIndex": HEADER_ROW_INDEX, "endRowIndex": HEADER_ROW_INDEX + len(df), "startColumnIndex": col_index, "endColumnIndex": col_index + 1, }], "booleanRule": { "condition": { "type": "TEXT_EQ", "values": [{"userEnteredValue": "No"}], }, "format": {"backgroundColor": color}, }, }, "index": 0, } }) all_requests = delete_requests + new_rules if not all_requests: return spreadsheet.batch_update({"requests": all_requests})

The key change: before adding rules, we fetch the sheet metadata, count existing conditional format rules on the target sheet, and issue deleteConditionalFormatRule requests (from last index to first to keep indices stable). Then the deletes and new adds are sent in a single batch_update call.

Is it possible to efficiently address this through checking for existing rules? @mandylan75, see the response here.

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…m/Imageomics/repo-exporter into feature/issue-58/consolidate-code

Co-authored-by: Elizabeth Campolongo <38985481+egrace479@users.noreply.github.com>

Copilot

Pull request overview

Copilot reviewed 18 out of 21 changed files in this pull request and generated no new comments.

Suppressed comments (3)

src/repo_exporter/base.py:318

_apply_conditional_formatting() currently indexes all existing conditional formats by startColumnIndex, and then deletes any rules whose column isn't in desired (lines 308-347). That can unintentionally delete user- or manually-created conditional formatting rules on the sheet that have nothing to do with the exporter.

Consider only managing rules that match the exporter’s own signature (TEXT_EQ == "No" and the expected startRowIndex), and ignore all other conditional formats so they’re left untouched.

        existing_rules = {}  # col_index -> (rule_index, rule_dict)
        metadata = sheet.spreadsheet.fetch_sheet_metadata()
        for sheet_meta in metadata.get("sheets", []):
            if sheet_meta["properties"]["sheetId"] != sheet_id:
                continue
            for i, rule in enumerate(sheet_meta.get("conditionalFormats", [])):
                ranges = rule.get("ranges", [{}])
                if not ranges:
                    continue
                existing_rules[ranges[0].get("startColumnIndex")] = (i, rule)
            break

        requests = []

        # 1. Updates first; index safe since nothing has shifted yet.
        for col_index, color in desired.items():
            if col_index not in existing_rules:
                continue
            rule_index, existing_rule = existing_rules[col_index]
            existing_range = existing_rule.get("ranges", [{}])[0]
            existing_color = (
                existing_rule.get("booleanRule", {}).get("format", {}).get("backgroundColor", {})
            )
            if existing_range.get("endRowIndex") != end_row or self._normalize_color(existing_color) != self._normalize_color(color):
                requests.append({
                    "updateConditionalFormatRule": {
                        "index": rule_index,
                        "sheetId": sheet_id,
                        "rule": self._build_conditional_rule(sheet_id, col_index, end_row, color),
                    }
                })

        # 2. Deletes next, highest index first; deleting descending never
        # shifts the index of a rule we still need to delete
        stale_indices = sorted(
            (rule_index for col_index, (rule_index, _) in existing_rules.items() if col_index not in desired),
            reverse=True,
        )
        for rule_index in stale_indices:
            requests.append({"deleteConditionalFormatRule": {"sheetId": sheet_id, "index": rule_index}})

test_normalize.py:13

This file looks like a one-off debug script (prints to stdout) rather than a test or library module. Since it lives at the repo root it’s easy to confuse with real tests/code, but it won’t be executed by pytest (testpaths is tests). Consider removing it from the package PR or moving the relevant assertion into an actual pytest test (e.g. tests/repo_exporter/test_base.py).

from repo_exporter.base import BaseExporter

# Simulate the API omitting zero-valued channels
api_response = {"red": 1}  # green/blue omitted because they're 0
literal = {"red": 1, "green": 0.5, "blue": 0.5}

print(BaseExporter._normalize_color(api_response))
print(BaseExporter._normalize_color(literal))
print(BaseExporter._normalize_color(api_response) == BaseExporter._normalize_color(literal))

same_full = {"red": 1, "green": 0.5, "blue": 0.5}
same_partial = {"red": 1, "green": 0.5, "blue": 0.5}
print(BaseExporter._normalize_color(same_full) == BaseExporter._normalize_color(same_partial))  # should be True

test_new_column_logic.py:114

This appears to be an ad-hoc test harness for the legacy gh_repo_exporter.update_google_sheet function (and includes __main__ prints). Since it’s at the repo root it won’t run under the configured pytest testpaths = ["tests"], and it also couples this refactor PR back to the legacy script.

Consider either removing it, or converting it into a real pytest module under tests/ that targets the new BaseExporter._sync_new_columns behavior instead.

import pandas as pd
from unittest.mock import MagicMock, patch, call
 
from gh_repo_exporter import update_google_sheet
 
 
def _make_mock_sheet(fake_header, fake_data_rows):
    mock_sheet = MagicMock()
    mock_sheet.title = "GH-Repos"
    mock_sheet.id = 0
    mock_sheet.row_values.return_value = fake_header
    mock_sheet.get_all_values.return_value = [[], fake_header] + fake_data_rows
 
    mock_spreadsheet = MagicMock()
    mock_spreadsheet.worksheet.return_value = mock_sheet
    mock_sheet.spreadsheet = mock_spreadsheet
 
    mock_client = MagicMock()
    mock_client.open_by_key.return_value = mock_spreadsheet
 
    return mock_sheet, mock_spreadsheet, mock_client
 
 
def _run(df, mock_client):
    with patch("gh_repo_exporter.gspread.authorize", return_value=mock_client), \
         patch("gh_repo_exporter.Credentials.from_service_account_file", return_value=MagicMock()):
        update_google_sheet(df, "fake_spreadsheet_id", "GH-Repos", "fake_creds.json")
 
 
def test_new_column_is_appended():
    """Existing behavior: a DataFrame column not in the sheet header gets appended."""
    fake_header = ["Repository Name", "Stars", "README"]
    fake_data_rows = [["=HYPERLINK(\"url\", \"cool-project\")", "10", "Yes"]]
    mock_sheet, mock_spreadsheet, mock_client = _make_mock_sheet(fake_header, fake_data_rows)
 
    df = pd.DataFrame([{
        "Repository Name": '=HYPERLINK("url", "cool-project")',
        "Stars": 10,
        "README": "Yes",
        "TEST_COLUMN": "test123",
    }])
 
    _run(df, mock_client)
 
    assert mock_sheet.update.call_args == call(range_name="D2", values=[["TEST_COLUMN"]])
    print("PASS: test_new_column_is_appended")
 
 
def test_renamed_column_creates_duplicate_not_inplace_update():
    """Documents current behavior: renaming a column (e.g. 'Language' -> 'Primary Language')
    does NOT update the existing 'Language' header in place. Instead the new name is treated
    as a brand-new column and appended, leaving the old header untouched.
    """
    fake_header = ["Repository Name", "Language"]
    fake_data_rows = [["=HYPERLINK(\"url\", \"cool-project\")", "Python"]]
    mock_sheet, mock_spreadsheet, mock_client = _make_mock_sheet(fake_header, fake_data_rows)
 
    df = pd.DataFrame([{
        "Repository Name": '=HYPERLINK("url", "cool-project")',
        "Primary Language": "Python",
    }])
 
    _run(df, mock_client)
 
    # "Primary Language" gets appended as a new column at index 3 (C2), NOT written into
    # the existing "Language" column at index 2 (B2).
    assert mock_sheet.update.call_args == call(range_name="C2", values=[["Primary Language"]])
    print("PASS: test_renamed_column_creates_duplicate_not_inplace_update (documents current behavior)")
 
 
def test_no_new_columns_skips_header_update():
    """When every DataFrame column already exists in the sheet header, sheet.update()
    (the header-writing call) should never be called."""
    fake_header = ["Repository Name", "Stars", "README"]
    fake_data_rows = [["=HYPERLINK(\"url\", \"cool-project\")", "10", "Yes"]]
    mock_sheet, mock_spreadsheet, mock_client = _make_mock_sheet(fake_header, fake_data_rows)
 
    df = pd.DataFrame([{
        "Repository Name": '=HYPERLINK("url", "cool-project")',
        "Stars": 20,
        "README": "No",
    }])
 
    _run(df, mock_client)
 
    mock_sheet.update.assert_not_called()
    print("PASS: test_no_new_columns_skips_header_update")
 
 
def test_multiple_new_columns_appended_together():
    """Two new columns at once should be appended starting at the correct index,
    in the same order they appear in df.columns."""
    fake_header = ["Repository Name", "Stars"]
    fake_data_rows = [["=HYPERLINK(\"url\", \"cool-project\")", "10"]]
    mock_sheet, mock_spreadsheet, mock_client = _make_mock_sheet(fake_header, fake_data_rows)
 
    df = pd.DataFrame([{
        "Repository Name": '=HYPERLINK("url", "cool-project")',
        "Stars": 10,
        "Model": "No",
        "Dataset": "No",
    }])
 
    _run(df, mock_client)
 
    assert mock_sheet.update.call_args == call(range_name="C2", values=[["Model", "Dataset"]])
    print("PASS: test_multiple_new_columns_appended_together")
 
 
if __name__ == "__main__":
    test_new_column_is_appended()
    test_renamed_column_creates_duplicate_not_inplace_update()
    test_no_new_columns_skips_header_update()
    test_multiple_new_columns_appended_together()

…nditional formatting

…m/Imageomics/repo-exporter into feature/issue-58/consolidate-code

Copilot

Pull request overview

Copilot reviewed 16 out of 19 changed files in this pull request and generated no new comments.

Suppressed comments (3)

src/repo_exporter/huggingface.py:166

get_card_field() only reads repo.card_data; the legacy exporter uses repo.cardData (and HF objects may expose only that attribute). If card_data is missing, this will fall into the exception path and return "N/A" even when cardData contains the requested fields (license/description). Consider supporting both attribute names.

        try:
            for key in keys:
                value = repo.card_data.get(key, "")
                if value:

src/repo_exporter/github.py:288

get_top_contributors() sleeps 20s between retries whenever get_stats_contributors() returns a falsy value, which can add 40s per repo even though a commit-based fallback exists. This can make large org exports take hours. Consider only waiting on the specific "202 stats are being generated" response and using a shorter backoff.

            for _ in range(3):
                stats = repo.get_stats_contributors()
                if stats:
                    break
                time.sleep(20)

src/repo_exporter/main.py:121

The --repo-type help text advertises the default as GH_REPO_TYPE (often "None"), but export_repos() actually defaults to "all" when GH_REPO_TYPE is unset. Updating the help keeps CLI behavior and documentation aligned.

        help=f"Repo type filter: all, public, private, forks, sources, member "
             f"(overrides GH_REPO_TYPE in .env; default: {GH_REPO_TYPE})"

beanbean9339

Approved! Solid refactor with thorough test coverage across the Hugging Face exporter, CLI, edge cases, and error handling. No blocking issues from me. Nice work! 👍

beanbean9339 · 2026-08-20T17:00:52Z

+        """
+        ...
+
+    @abstractmethod


I agree with making red_columns and secondary_columns abstract properties. update_google_sheet() depends on them, so this makes the subclass contract explicit and keeps the architecture consistent with the other abstract methods.

beanbean9339 · 2026-08-20T17:11:50Z

+
+    repo = NoCitationRepo()
+    readme = '[![DOI](https://zenodo.org/badge/195575274.svg)](https://zenodo.org/badge/latestdoi/195575274)'
+    assert has_doi(repo, readme) == "https://zenodo.org/badge/latestdoi/195575274"


Nice coverage of the fallback behavior. Could we add a case where CITATION.cff contains an invalid DOI but the README has a valid Zenodo badge? That would verify the fallback works not only when the DOI is absent, but also when it fails validation.

Great idea, I will add that that to the tests!

Actually, re-reading this, I don't think we would want that to count. We don't want repos with citations that have invalid DOIs, since they would then be cited incorrectly.

beanbean9339 · 2026-08-20T17:14:07Z

+    """
+    repo = FakeRepo(citation)
+    assert is_valid_doi("10.9999/zenodo.10000000") is True
+    assert has_doi(repo) == "https://doi.org/10.9999/zenodo.10000000"


Could we add a case with multiple entries in identifiers, including a non-DOI entry before the valid DOI? This would verify that unrelated identifiers are skipped correctly.

Yes, this shouldn't be a problem since i created functions that made it so that specifically the zenodo DOI gets checked by its format, but I will add that to the tests as well.

beanbean9339 · 2026-08-20T17:21:38Z

I think test_identifiers_type_doi is sufficient here since it tests the standard identifiers structure in CITATION.cff. I'd drop test_identifiers_doi unless the shorthand format is intentionally supported.

beanbean9339 · 2026-08-20T17:25:42Z

One thing worth confirming separately is the documented License == "N/A" behavior for repos without license metadata, since the test notes that this differs from the legacy exporter. Otherwise, I don't see any blocking issues.

Thank you for the feedback! I will consider adding these suggestions into a separate PR since this PR desperately needs to be approved but I will for sure come back to it!

NetZissou

I've configured the .env locally, and was able to export huggingface repos to the googlesheet successfully. The github repo export takes much longer, and it paused at around 90% for a long time with no warnings or time-out errors. I'll try it again this afternoon.

The README wasn't updated for the refactor, it's unchanged from main and still documents the deleted scripts.

python gh_repo_exporter.py / python hf_repo_exporter.py no longer work on this branch. They should be replaced with the new pkg entry point commands. And the new repo-exporter github|huggingface CLI with its flags (--org, --repo-type, --spreadsheet-id, …) should be documented as well.

NetZissou

.github/workflows/gh-repo-exporter.yml
.github/workflows/hf-repo-exporter.yml

Both workflow files still run the deleted scripts (python gh_repo_exporter.py / python hf_repo_exporter.py + pip install -r requirements.txt). They should be updated and schedule a re-run to make sure it can be executable.

NetZissou · 2026-08-25T18:47:47Z

And just a follow-up on my previous comment, the GitHub repo export executed successfully in the second attempt. The features are functioning well.

Copilot

Pull request overview

Copilot reviewed 22 out of 22 changed files in this pull request and generated 3 comments.

mandylan75 · 2026-08-27T20:01:18Z

+        repo_type - String. Repo type.
+        """
+        try:
+            commits = self.api.list_repo_commits(repo_id=repo_id, repo_type=repo_type)


This should probably be listed in a separate issue, similarly to get_top_contributors()

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egrace479

A couple small notes on presentation

egrace479 · 2026-08-27T20:06:34Z

 # Repository Exporter [![DOI](https://zenodo.org/badge/1080019710.svg)](https://doi.org/10.5281/zenodo.17835081)

-Python scripts that gather metadata for all repositories in a provided GitHub or Hugging Face organization and automatically export the data into a desired Google Sheet (using a Google Cloud Console Service Account) for easy viewing and analysis.
+Python package that gathers metadata for all repositories in a provided GitHub or Hugging Face organization and automatically exports the data into a desired Google Sheet (using a Google Cloud Console Service Account) for easy viewing and analysis.


Suggested change

Python package that gathers metadata for all repositories in a provided GitHub or Hugging Face organization and automatically exports the data into a desired Google Sheet (using a Google Cloud Console Service Account) for easy viewing and analysis.

Python package that gathers metadata for all repositories in a provided GitHub or Hugging Face organization and automatically exports the data into a designated Google Sheet (using a Google Cloud Console Service Account) for easy viewing and analysis.

egrace479 · 2026-08-27T20:09:20Z

+
+| Flag | Overrides | Description |
+|---|---|---|
+| `--org` | `HF_ORG_NAME` | Hugging Face organization name (case-sensitive) |


egrace479 · 2026-08-27T20:12:19Z

    - **Run both exporters (wait for one to finish before running the other)**
      ```
-      python hf_repo_exporter.py
-      python gh_repo_exporter.py
+      repo-exporter huggingface
+      repo-exporter github
+      ```
+


I think we could replace this with a simple statement that they can be run sequentially; each must be run individually. (I can't directly make the suggestion)

Do they have to be run one at a time? As in, is there any reason I couldn't run the commands one after the other before it's finished?

Great catch! I double checked with the code and there's no dependency between them, so they can both be run individually since they're separate processes.

create architecture

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rename maim file

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lots of fixes

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reorganized functions

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add argparse for GH exporter #61

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implement copilots suggestions

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add gh and hf subparsers

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Merge branch 'feature/issue-58/consolidate-code' of https://github.co…

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tests for package

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Add Pyproject.toml #63

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incorporate feedback and add pyproject.toml from PR #63

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add pip install -e in pr-test workflow

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add .[dev] in pr workflow

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fix onlyy manage rules matching exporter's TEXT_EQ no signature in co…

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remove temporary local testing files

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add all fallback to gh_repo_type

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update workflows to package instead of legacy script

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mandylan75 requested review from NetZissou and a lite review from Copilot August 27, 2026 18:29

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mandylan75 and others added 2 commits August 27, 2026 16:07

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update test function in test_huggingface.py

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egrace479 reviewed Aug 27, 2026

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mandylan75 mentioned this pull request Aug 27, 2026

Tests: Apply additional test coverage to new package #76

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update README.md presentation

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mandylan75 requested a review from egrace479 August 28, 2026 13:30

	Python package that gathers metadata for all repositories in a provided GitHub or Hugging Face organization and automatically exports the data into a desired Google Sheet (using a Google Cloud Console Service Account) for easy viewing and analysis.
	Python package that gathers metadata for all repositories in a provided GitHub or Hugging Face organization and automatically exports the data into a designated Google Sheet (using a Google Cloud Console Service Account) for easy viewing and analysis.

	\| `--org` \| `HF_ORG_NAME` \| Hugging Face organization name (case-sensitive) \|
	\| `--org` \| `HF_ORG_NAME` \| Hugging Face organization name (case-sensitive) \|

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mandylan75 commented Jun 30, 2026 •

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