Generating Cookiecutter templates from a GitHub template repo

Simplifying the process into one click on GitHub

There's a further aspect to this which I haven't touched on, which is to involve GitHub Actions to automate away the need to run cookiecutter, and instead use the GitHub interface to 'stamp' a new template repo.

The only example I've seen of this is from datasette maintainer Simon Willison.

Simon's python-lib-template-repository is a GitHub template repo with a self-deleting setup.yml GitHub Actions workflow that runs cookiecutter on the GitHub Actions CI for you, in the background, so when you create a new repo (via the repo's /generate URL path) its name parametrises the associated Cookiecutter template, python-lib

It gets a bit confusing to discuss, since the "GitHub Template Repo" and the "Cookiecutter Template" are both git repositories and both 'templates' (though a GitHub Template Repo and a Cookiecutter Template are distinct types of template).

A GitHub Template Repo is a template due to having the Template repository setting checked in its Settings page. Additionally, here when we refer to a GitHub Template Repo we mean a repo that: - is publicly readable (or else how would we be reviewing other people's) - has an 'installer' that runs on the first push and includes a command to delete the installer before force-pushing the new "filled in" repo

A Cookiecutter Template is a template due to containing a cookiecutter.json file and parameterised file contents and directory/file names.

Where to place the maintenance burden

You essentially have to choose where to place the burden of maintenance:

As an example, if your tests run on Python 3.8, you might either hard-code that version or parameterise it in your cookiecutter JSON config.

If you hardcode it, then you avoid doing the extra work of parameterising the library (though as we've seen it's not actually that hard to do this thanks to tools like find and sed), but if you wish to change the Python version at a later date then you would have to go edit it manually. Since those edits are in the Cookiecutter Template, that's what you'd maintain.

If you parameterise it, then you would pass the Python version into the call to cookiecutter. Since this call takes place in the GitHub Template Repo's setup.yml workflow, you are maintaining the GitHub Template Repo.

It strikes me that you should seek to parameterise as much as possible, so I would opt for the second approach: the GitHub template repo.

I'm going to stop capitalising them now, you get the idea.

Self-service package templates

So let's put it into practice as a 'self-service' package template. I like this term as it's meant to describe a situation where

application owners can rapidly and reliably provision and maintain their application infrastructure without depending on IT operations teams.

OK it's pushing the definition a little, but at least I didn't say self-driving...

To start off, let's clarify the equivalence:

  1. Simon's Cookiecutter template repository is python-lib and mine is [py-pkg-cc-template][py-pkg-cc-template]
  2. Simon's GitHub template repository is python-lib-template-repository and mine is (going to be!) self-serve-py-pkg

To simplify things, I'm now going to refer to the cookiecutter template as the "source", since it's what gets pulled into the latter GitHub template repository.

We already have the first part, but it's worth comparing our implementations.

Simon's source repo has a .github/workflows directory containing one CI program to run, on pushes, whose purpose is to regenerate a demo repo. That's more for demo-ing purposes, so I'm going to skip it unless I start getting more experimental. It also has a README, with usage guide (but I'm writing this blog which doubles as that), a LICENSE, and a .gitignore of commonly commited files. I'm going to skip all of these and move on.

I've so far made my repo entirely from my own packages, so there's no need to include his license, but if you are then you should (the Apache-2.0 license requires preservation of copyright).

To set up the 2nd part begins with creating a new GitHub repo, checking the 'Template repository' setting, and cloning locally.

Simon's template repo is unlicensed, so I'm just going to adapt it and credit him here, leaving mine likewise unlicensed.

I'm starting with a blank repo, and in this one there are 3 YAML workflows:

publish publishes a PyPI package when a branch named release is created (I'm not doing this as discussed in the previous section)

test runs tests with pytest when pushed to (I've already got a CI workflow for testing so again I'm not using this)

setup is the good part. In order, it does the following:

Check out the GraphQL API here in case you want to do more with it, and use the explorer here, e.g. to view all the fields on the Repository object type, the docs tell you to run:

query {
  __type(name: "Repository") {
    fields {

assignableUsers, autoMergeAllowed, branchProtectionRules, codeOfConduct, codeowners, collaborators, commitComments, contactLinks, createdAt, databaseId, defaultBranchRef, deleteBranchOnMerge, deployKeys, deployments, description, descriptionHTML, discussion, discussionCategories, discussions, diskUsage, environment, environments, forkCount, forkingAllowed, forks, fundingLinks, hasIssuesEnabled, hasProjectsEnabled, hasWikiEnabled, homepageUrl, id, interactionAbility, isArchived, isBlankIssuesEnabled, isDisabled, isEmpty, isFork, isInOrganization, isLocked, isMirror, isPrivate, isSecurityPolicyEnabled, isTemplate, isUserConfigurationRepository, issue, issueOrPullRequest, issueTemplates, issues, label, labels, languages, latestRelease, licenseInfo, lockReason, mentionableUsers, mergeCommitAllowed, milestone, milestones, mirrorUrl, name, nameWithOwner, object, openGraphImageUrl, owner, packages, parent, pinnedDiscussions, pinnedIssues, primaryLanguage, project, projectNext, projects, projectsNext, projectsResourcePath, projectsUrl, pullRequest, pullRequestTemplates, pullRequests, pushedAt, rebaseMergeAllowed, ref, refs, release, releases, repositoryTopics, resourcePath, securityPolicyUrl, shortDescriptionHTML, squashMergeAllowed, sshUrl, stargazerCount, stargazers, submodules, tempCloneToken, templateRepository, updatedAt, url, usesCustomOpenGraphImage, viewerCanAdminister, viewerCanCreateProjects, viewerCanSubscribe, viewerCanUpdateTopics, viewerDefaultCommitEmail, viewerDefaultMergeMethod, viewerHasStarred, viewerPermission, viewerPossibleCommitEmails, viewerSubscription, visibility, vulnerabilityAlerts, watchers

query {
  __type(name: "RepositoryOwner") {
    fields {

avatarUrl, id, login, repositories, repository, resourcePath, url

Item in the bagging area

This one file is all you need to adapt to get your own self-service Python package template repo.

  1. Change Simon's source repo (simonw/python-lib) to yours (again, mine is lmmx/py-pkg-cc-template)

  2. Change the call signature to match the one defined by your cookiecutter.json (in the top level of your source repo). So mine is

  "lib_name": "",
  "description": "",
  "hyphenated": "{{ '-'.join(cookiecutter['lib_name'].lower().split()).replace('_', '-') }}",
  "underscored": "{{ cookiecutter.hyphenated.replace('-', '_') }}",
  "github_username": "",
  "author_name": "",
  "email": "",
  "year": ""

... which is the same approach as Simon's but with email and year (which I either need to hardcode or figure out how to get from the GitHub API). On this point, returning to the GraphQL API Explorer, I can see from the Explorer pane on the left hand side that the repositoryOwner (which I presume is the type of the owner attribute of the Repository)

Note: the Explorer lets you step 'down' into the object's nested structure, which I think actually corresponds to cross-referencing 'up' ? Or at least is signified by three dots which I interpret as being 'up a level', I don't know: GraphQL is more intuited than understood for me... Essentially you can find extra details in this view though while doing __type queries to view the 'layout' in GraphQL, and add them back in to your query commands to extract values.

year="$(echo $INFO | jq -r .repository.createdAt | cut -d\- -f 1)"

Debugging permissions

Unfortunately, this initial attempt fell flat at the final step of force pushing, since the GitHub Actions bot apparently lacks the permissions on my repos. I confirmed that the same happens if I try to use Simon's template too:

Run stefanzweifel/git-auto-commit-action@v4
Started: bash /home/runner/work/_actions/stefanzweifel/git-auto-commit-action/v4/
D   .github/workflows/setup.yml
Your branch is up to date with 'origin/main'.
INPUT_COMMIT_USER_NAME: github-actions[bot]
INPUT_COMMIT_USER_EMAIL: github-actions[bot]
INPUT_COMMIT_MESSAGE: Initial library structure
[main 564c03b] Initial library structure
 Author: lmmx <>
 7 files changed, 285 insertions(+), 98 deletions(-)
 delete mode 100644 .github/workflows/setup.yml
 create mode 100644 .gitignore
 create mode 100644 LICENSE
 rewrite (99%)
 create mode 100644
 create mode 100644 simon_w_template_demo_test/
 create mode 100644 tests/
No tagging message supplied. No tag will be added.
remote: Permission to lmmx/simon-w-template-demo-test.git denied to github-actions[bot].
fatal: unable to access '':
The requested URL returned error: 403

Reviewing the repo's /settings/actions, the section "Workflow permissions" seemed to indicate the default is read-only, not read and write permissions when using the GITHUB_TOKEN (which is what git-auto-commit-action must be doing internally). The settings had a 'Learn more' link pointing to docs on Modifying the permissions for the GITHUB_TOKEN:

You can modify the permissions for the GITHUB_TOKEN in individual workflow files. If the default permissions for the GITHUB_TOKEN are restrictive, you may have to elevate the permissions to allow some actions and commands to run successfully.

so we don't need to change the repo settings [which don't exist before repo creation anyway] if we change it for the workflow itself, by setting the permissions key:

You can use the permissions key in your workflow file to modify permissions for the GITHUB_TOKEN for an entire workflow or for individual jobs.

Except, it also says you can't do that:

You can use the permissions key to add and remove read permissions for forked repositories, but typically you can't grant write access. which case I'm stumped on how to get this to work automatically.

Spoiler alert - the rest of this section is devoted to the litany of failures that followed this. Skip to the next section if you just want to find out what works.

The Action in question mentions in its README that:

Note that the Action has to be used in a Job that runs on a UNIX system (eg. ubuntu-latest). If you don't use the default permission of the GITHUB_TOKEN, give the Job or Workflow at least the contents: write permission.

which suggests that you could set it at the workflow level...

I added the suggested permission to the YAML and got a new error (which is usually a good sign):

 ! [remote rejected] master -> master (refusing to allow a GitHub App to create or update workflow
`.github/workflows/master.yml` without `workflows` permission)
error: failed to push some refs to ''

which makes sense, as we are deliberately rewriting the CI workflow, but the key workflows is not listed on the permissions docs. Only actions, which is similar?

I'd put the write permission on the job (setup-repo) not the entire workflow, so I wondered if it meant that I needed to elevate it to the top level of the YAML file.

 ! [remote rejected] master -> master (refusing to allow a GitHub App to create or update workflow
`.github/workflows/master.yml` without `workflows` permission)

Failing that, my only guess was to instead change the condition from push to creation of the master branch, and that way you wouldn't need to bother with changing the workflow YAML, with the only downside that you'd have a leftover setup.yml and not get the cool self-deleting installer behaviour.

There's an example of this in the publish.yml workflow of Simon's python-lib-template-repository, which means "when a branch named release is created":

    types: [created]

So by changing the word release to master we can achieve the desired behaviour. This however also would mean giving up on moving the workflows into the workflow directory, and you'd need to do that manually, which is not so satisfyingly self-serviced.

TODO: this is wrong, "master" is not a valid event name apparently

I eventually realised that a template repo's settings will probably be copied when you generate from it, so I wouldn't need to worry about this after all. I can just change the settings on the template repo. Alas this wasn't true either.

Back to the drawing board

So this feature didn't work as advertised: in fact Simon documented it on his blog

This almost worked—but when I tried to run it for the first time I got this error:

![remote rejected] (refusing to allow an integration to create or update .github/workflows/publish.yml)

It turns out the credentials provided to GitHub Actions are forbidden from making modifications to their own workflow files!

I can understand why that limitation is in place, but it’s frustrating here. For the moment, my workaround is to do this just before pushing the final content back to the repository:

sh mv .github/workflows .github/rename-this-to-workflows

I leave it up to the user to rename that folder back again when they want to enable the workflows that have been generated for them.

Except I don't think this was actually done in the code.

This constraint just means we need to have the same .github/workflows/ directory as in the source repo. Maybe that's not actually such a hard constraint. I see 2 options now:

Note that this manual approach also allows you to pass in extra values! As documented over on the GitHub blog post announcing the feature in 2020,

In addition, you can optionally specify inputs, which GitHub will present as form elements in the UI. Workflow dispatch inputs are specified with the same format as action inputs.

This would let you fill in extra values like email address rather than hardcoding it privately, or any other customisation.

...still no dice. Changing the setting does not permit an Action to modify the workflows directory.

My only other idea would be to use GitLab, since I've already set up a personal access token for CI/CD import sync there, and with that I should be able to run custom workflows in a .gitlab-ci.yml file which might then be able to overwrite a repo.

But I do wonder at this point: surely we can keep it all within GitHub? If the way GitLab is going to push to GitHub is through using a Personal Access Token triggered by a webhook, then surely I can authenticate a GitHub workflow with a PAT and achieve the same effect without mixing providers?

So far we've got step 1 of this prototyped out: we can generate a repo from the self-serve-py-pkg template repo and have it run a CI workflow. The next step we need is to include a webhook callback, which is just a POST request which can be done with curl in the workflow script, but before that we need to set up a GitHub webhook.

GitLab provides so-called "pipeline triggers" which I don't think GitHub has a version of... This allows you to kick off a CI job on one repo from the CI job of another.

According to this forum post:

You’ll need to set up a personal access token as a secret in the library’s repository

by which it's meant the sender repository (not the receiver). This is not an acceptable solution, because the sender repository is going to be the one generated from the repo (and secrets are set manually, not copied over during template generation, so this sender repo cannot be expected to have secrets).

Unfortunately, if there's no way to accept an event on another repository as the trigger then this does in fact rule out GitHub, and GitLab is the only way to do this. Yay, awkward!

Reusable workflows and repository dispatches

While entering the part of the development cycle formally known as "desperation", I came across the workflow_call event, and its associated concept of reusable workflows

While I initially assumed workflow_call would be restricted to workflows in the same repo, thereby ruling out use to trigger one repo's from another's, it turned out that the requirements are actually:

A reusable workflow can be used by another workflow if either of the following is true:

  • Both workflows are in the same repository.
  • The called workflow is stored in a public repository, and your organization allows you to use public reusable workflows.

When presented with this constraint of using a public GitHub template repo, I realised that if you stored your email and any other personal values being provided to cookiecutter as a secret, you could make the repo public without a problem (this was already possible in hindsight).

I then came across the repository_dispatch event, which seems like an exact match for what GitLab called pipeline triggers. This appears to obviate my last resort of a GitHub-to-GitLab approach.

How to send a repository_dispatch event

The docs have a page on creating a repository dispatch event with both a curl and JS option. The JS approach is nicer to read, and I'm optimising for something nicely maintainable and adaptable (you could surely switch to curl to optimise speed instead).

The example given includes authentication, and is essentially in the form of:

await github.request(`POST /repos/${owner}/${repo}/dispatches`, {
  owner: owner,
  repo: repo,
  event_type: 'on-demand-test',

You can equivalently and more succinctly use the pre-authenticated API library as:

  owner: owner,
  repo: ss_repo_name,
  event_type: "on-demand-test"

However note that a PAT must be configured for this to succeed.

There's a good blog post on how a repository dispatch works in practice here, which notes that the event_type you specify in your payload becomes the value of context.payload.action when received (and is just a string, e.g. the author there uses "my-event"). Another good blog post here.

If, instead, I was to use the workflow call approach, I'd get this payload populated with the event context, but it's less clear to me whether that would remove the necessary distinction between sender/receiver repos which I need to preserve...

However, when I tried this, all I could achieve was the error:

Resource not accessible by integration

There's a catch-22 in regards to automation here:

So in short, you can't automate self-serve package templating with template repos and Cookiecutter within the constraint of a GitHub Actions-only solution.

Designing a GitHub-to-GitLab approach

As a last resort, I suspect that GitLab could achieve the desired outcome because you don't need elevated permissions to trigger a workflow on another repo's GitLab CI. In fact, it's encouraged:

You can trigger a pipeline for a branch or tag by generating a trigger token and using it to authenticate an API call. The token impersonates a user’s project access and permissions.

This is the killer feature of GitLab that led me to use it for generating my website, the way that tokens assume a user's project access and permissions.

Here's the architecture that this capability enables:

  1. When a new repo is created (and in the case of template repos, immediately pushed to), if it has a GitLab CI config file .gitlab-ci.yml, and if GitLab is set up as an app for the user's GitHub account,
  2. the repo will be 'mirrored' to the same user's GitLab account, and CI workflows will begin there.
  3. We can write a GitLab CI workflow job that sends a POST request to the 'pipeline trigger' endpoint for the GitLab CI workflow of another repo: the mirror of the 'parent' template repo.
  4. If we store a GitHub Personal Access Token as a GitLab CI/CD secret of this 'parent' repo's GitLab mirror, then when the pipeline trigger launches a CI runner, it will be authorised to write to the GitHub repo [as long as the PAT is appropriately scoped and the repo being written to is accessible, most simply if both repos are on the same account].

GitHub is fully integrated with this CI 'mirroring' to GitLab, so we can see the results of the GitLab CI checks within GitHub (ticks and crosses display pass/fail status for each job as for those in GitHub Actions).

This seems quite a daunting undertaking at first, but sketching out the prototype's architecture diagram and numbering the steps lets us start at the beginning, with GitLab CI config file in the template repo (self-serve-py-pkg).

We want this GitLab config to do just one thing: to overwrite the workflows directory of the repo which triggers it (which we receive as the event from the pipeline trigger).

I've written two types of GitLab CI before: super-simple pages jobs to generate websites, and more complex Debian-deployed jobs acting as serverless microservices.

The default runner image on GitLab is Ruby (as in it ships with the programming language Ruby), so if you want to run Python in your image you switch to another like Debian 11 Slim.

Debian 11 is a.k.a. "Bullseye", "Slim" means it's a minimal Linux build

Here's how you POST to a pipeline trigger (which we want to run in the repo generated from the template repo):

  stage: deploy
  - curl -X POST -F token=PIPELINE_TRIGGER_TOKEN_GOES_HERE -F ref=master

That's the entire file, not just a snippet!

Next, here's (roughly) how you write a file in a git-cloned GitHub repo then push the changes back (which we want to run in the GitHub template repo's GitLab mirror):

  - eval "$(ssh-agent -s)"
    # Following:
  - GITHUB_FINGERPRINT=$(ssh-keyscan -t ed25519 | tee github-key-temp | ssh-keygen -lf -)
  - GITHUB_EXPECTED_FINGERPRINT="256 SHA256:+DiY3wvvV6TuJJhbpZisF/zLDA0zPMSvHdkr4UvCOqU (ED25519)"
    # From:
  - if [ ! "$GITHUB_FINGERPRINT" = "$GITHUB_EXPECTED_FINGERPRINT" ]; then echo "Error - SSH fingerprint mismatch" 1>&2 && exit 25519; fi
  - mkdir -p ~/.ssh # This shouldn't be necessary but getting an error that "file/dir. doesn't exist" on next line
  - cat github-key-temp >> ~/.ssh/known_hosts && rm github-key-temp
  - ssh-add <(echo "${SSH_priv_key_GitHub}" | base64 --decode)
  - git config --global "${git_email}"
  - git config --global "${full_name}"


  stage: WRITE_TO_REPO
  - git clone
  - cd REPO_NAME
  - echo "foo
    baz" > .github/workflows/NEW_WORKFLOW_NAME.yml
  - git add .github
  - git commit -m "Added NEW_WORKFLOW_NAME.yml"
  - git push origin master

This example is adapted from a tried and tested config for my website engine quill

The CI/CD Variables (secrets) you need to set for the above are:

Here protected refers to running only on protected branches and tags, and masked refers to whether the string will be redacted with asterisks from CI logs

We're going to get around the first two of these variables by using environment variables and POST request payload info instead.

There's one aspect that we've forgotten though, which is that both of the GitLab workflows will have to be in the same repo. For GitHub Actions workflows that's easy enough as you put them in the .github/workflows/ directory with one workflow per YAML file (named however you like). For GitLab though the file sits in the top level of the repo, named .gitlab-ci.yml:

The unit of work is a job, and a job has a stage defined, and (optionally) you can list the order of stages.

In GitHub Actions, we would control the repos that a workflow job ran on with if:

    if: ${{ github.repository != 'lmmx/self-serve-py-pkg' }}

This would only run when a new repo was generated from the template repo, and not when the template repo was pushed to.

In GitLab, you use the rules keyword instead, which accepts

an array of rules defined with: - if to specify when to add a job to a pipeline - changes by checking for changes to specific files - exists to run a job when certain files exist in the repository - allow_failure to allow a job to fail without stopping the pipeline - variables to define variables for specific conditions - when to configure the conditions for when jobs run

The closest equivalent of github.repository in GitLab is probably CI_REPOSITORY_URL. To review all of them during development, we can just dump the os.environ dict to CI logs from Python:

  - python3.9 -c "from os import environ as env; from pprint import pprint as pp; pp(dict(env));"

So to start easy, let's create a new .gitlab-ci.yml in the template repo and build up from there:

image: "python:3.9"

  - python3.9 -c "from os import environ as env; from pprint import pprint as pp; pp(dict(env));"


  stage: WRITE_TO_REPO
  - echo "Testing..."
  - mkdir "test_dir"
  - echo "foo
    baz" > test_dir/my_new_file
  - cat test_dir/my_new_file

Upon trying this though I (re-)discovered that repository mirroring is not automatic. You need to manually create a GitLab mirror, which rules out the approach I outlined above (specifically step 2, where GitLab CI is spun up for the new repo automatically when a repo is generated from the template repo).

This is fine, and in fact highlights a redundancy in our approach: the generated repo's GitHub Actions CI serves no purpose. Maybe another way of visualising these architectures might've made that clearer at the drawing board.

This also solves any issue about handling GitLab CI rules, as we no longer have to constrain where the GitLab CI runs. Now the GitLab CI only runs on the template repo, and the GitHub CI only runs on the template-generated repo.

Preliminary steps


If you don't, it can potentially show up in the logs, and anyone who finds them will be able to p0wn your GitHub account.

Note that we won't ssh-add this on our local system, as it's only to be used on the GitLab CI.

# When prompted, save the file under ~/.ssh/id_ed25519_github_template_ci
ssh-keygen -t ed25519 -C ""
# Public key for GitHub account
xclip -selection clipboard < ~/.ssh/
# Private key for GitLab repo secret
cat ~/.ssh/id_ed25519_github_template_ci | base64 | tr -d "\n" | xclip -sel clip

See my notes on generating SSH keys on Linux or more detail in the official GitHub guide

The button to add a new trigger is accompanied by helpful usage examples, in particular this one for a YAML-friendly curl:

  - "curl -X POST --fail -F token=TOKEN -F ref=REF_NAME"

however note that the --fail flag will hide the error message (and just show the status code of the response), so remove this while debugging.

i.e. in GitHub Actions it'll be:

curl -X POST \
     --fail \
     -F token=TOKEN \
     -F ref=REF_NAME \
     -F "variables[CUSTOM_CI_VARIABLE_HERE]=true" \

Note that this token can be used to trigger your pipeline by anyone! It has to be public because the template repo is public, and it has to be stored in there as the repo we generate from the template repo has no secrets (requiring them would break the automation). We're going to add sufficient checks and balances that this won't be any sort of security risk, but used in the wrong way it could be: use with care.

Building a joint GitHub/GitLab CI solution

Our new solution goes:

  1. When a new repo is created from a template repo and therefore initially pushed to, the GitHub Actions workflow gated by a check on the github.repository variable will run.
  2. This workflow will send a POST request to the 'pipeline trigger' endpoint of the GitLab repo that was set up manually beforehand to mirror the 'parent' template repo that was just generated from. We can pass in a payload with the request with the repo name.
  3. Using the GitHub SSH private key secret, the GitLab CI pipeline triggered by the POST request can write to the GitHub repo that sent it the request. This corresponds to the self-deleting setup.yml workflow in Simon's attempt.

The GitLab CI/CD workflow in step 3 is always .gitlab-ci.yml and running the one drafted above (printing the environment variables from Python) shows that there are 3 environment variables we can use to register our GitHub username:

 'CI_PROJECT_NAME': 'self-serve-py-pkg',
 'CI_PROJECT_PATH': 'lmmx/self-serve-py-pkg',

Rather than hardcoding my own username, I can therefore refer to:

These are all potentially useful ingredients to note down.

We also need to add a condition to do with the payload from the POST request, so next let's look at the trigger.yml GitHub workflow (step 2). It's quite long so I'll add the output of each step in between:

name: Send a pipeline trigger back to the originating template repo's GitLab mirror


    if: ${{ github.repository != 'lmmx/self-serve-py-pkg' }}
    runs-on: ubuntu-latest
      - name: Greet
        run: |
          echo "Hello from $OWNER this is $REPO"
          OWNER: ${{ github.repository_owner }}
          REPO: ${{ github.repository }}

Hello from lmmx this is lmmx/self-serve-demo

      - uses: actions/github-script@v6
        id: fetch-repo-and-user-details
          script: |
            const query = `query($owner:String!, $name:String!) {
              repository(owner:$owner, name:$name) {
                owner {
                  ... on User {
                  ... on Organization {
            const variables = {
              owner: context.repo.owner,
              name: context.repo.repo
            const result = await github.graphql(query, variables)
            return result

{ repository: { createdAt: '2022-MM-DD-SS.XYZ', name: 'self-serve-demo', description: 'This is a test repo :-)', owner: { login: 'lmmx', name: 'Louis Maddox' } } }

      - name: Inform and trigger pipeline
        run: |
          export GH_REF=$(echo $GH_REF_HEAD | cut -d\/ -f 3) # e.g. refs/heads/master --> master
          export GH_REPO_NAME=$(echo $INFO | jq -r '')
          export GH_AUTHOR_NAME=$(echo $INFO | jq -r '')
          export GH_USERNAME=$(echo $INFO | jq -r '.repository.owner.login')
          export GH_DESCRIPTION=$(echo $INFO | jq -r '.repository.description')
          export GH_YEAR_REPO_CREATED=$(echo $INFO | jq -r '.repository.createdAt' | cut -d\- -f 1)
          echo "Hello from $GH_AUTHOR_NAME a.k.a. $GH_USERNAME"
          echo "This is $GH_REPO_NAME created in $GH_YEAR_REPO_CREATED on branch $GH_REF"
          echo "More details: $GH_DESCRIPTION"
          curl -X POST --fail -F token=$PIPELINE_TRIGGER \
                              -F ref=$GH_REF \
                              -F "variables[GH_OWNER]=$GH_OWNER" \
                              -F "variables[GH_REPO]=$GH_REPO" \
                              -F "variables[GH_REPO_URL]=$GH_REPO_URL" \
                              -F "variables[GH_REF]=$GH_REF" \
                              -F "variables[GH_REPO_NAME]=$GH_REPO_NAME" \
                              -F "variables[GH_AUTHOR_NAME]=$GH_AUTHOR_NAME" \
                              -F "variables[GH_USERNAME]=$GH_USERNAME" \
                              -F "variables[GH_DESCRIPTION]=$GH_DESCRIPTION" \
                              -F "variables[GH_YEAR_REPO_CREATED]=$GH_YEAR_REPO_CREATED" \
          INFO: ${{ steps.fetch-repo-and-user-details.outputs.result }}
          PIPELINE_TRIGGER: c608274746d1da1521ace78909c6e6
          PROJECT_ID: 36180497
          GH_OWNER: ${{ github.repository_owner }}
          GH_REPO: ${{ github.repository }}
          GH_REPO_URL: ${{ github.repositoryUrl }}
          GH_REF_HEAD: ${{ github.ref }}

Hello from Louis Maddox a.k.a. lmmx This is self-serve-demo created in 2022 on branch master More details: This is a test repo :-)

This also triggered the GitLab pipeline, which again dumped out the environment variables, within which were OWNER, REPO, REPO_NAME, DESCRIPTION, GITHUB_USERNAME and AUTHOR_NAME which had been passed through from the GitHub Actions trigger.yml workflow.

Once the GitHub side was figured out, the GitLab side came together pretty quickly. The GitLab CI config defined two jobs with mutually exclusive rules: one that only ran when the source was a trigger event and one that didn't run on a trigger event (required for the CI to run upon setup). The latter just printed out "Nothing to do...", but as mentioned it had to be there (this is known as a 'no-op').

I also changed the before_script section that I'd used to debug the GitHub SSH key setup into the script block before the main work of sending git commits and overwriting the workflows. This prevented it from running in the 'no-op' CI job (which'd be a waste of time).

As an added safeguard, since SSH keys are highly sensitive, I manually excluded files with the .pem extension from the git staging area.

  tags: [docker] # Runners will pick the job up faster (not leave it pending)
  image: "python:3.9"


    - if: $CI_PIPELINE_SOURCE != "trigger"
  - echo "Nothing to do ..." # Do not delete this job, needed to run initial pipeline

  stage: WRITE_TO_REPO
    - if: $CI_PIPELINE_SOURCE == "trigger"
    #- python3.9 -c "from os import environ as env; from pprint import pprint as pp; pp(dict(env));"
    # Uncomment to debug

    - python3.9 -c "assert '$GH_REPO_NAME' != '', 'Stopping early because env. var. \$GH_REPO_NAME is empty'"
    - python3.9 -c "assert '$GH_USERNAME' == 'lmmx', 'Stopping early because env. var. \$GH_USERNAME is not lmmx'"
      # Basic input validation

    - python3.9 -m pip install cookiecutter

    - eval "$(ssh-agent -s)"
      # Following:

    - GITHUB_FINGERPRINT=$(ssh-keyscan -t ed25519 | tee github-key-temp | ssh-keygen -lf -)
    - GITHUB_EXPECTED_FINGERPRINT="256 SHA256:+DiY3wvvV6TuJJhbpZisF/zLDA0zPMSvHdkr4UvCOqU (ED25519)"
      # From:

    - if [ ! "$GITHUB_FINGERPRINT" = "$GITHUB_EXPECTED_FINGERPRINT" ]; then echo "Error - SSH fingerprint mismatch" 1>&2 && exit 25519; fi
    - mkdir -p ~/.ssh # This shouldn't be necessary but getting an error that "file/dir. doesn't exist" on next line
    - cat github-key-temp >> ~/.ssh/known_hosts && rm github-key-temp
    - ssh-add <(echo "${SSH_priv_key_GitHub}" | base64 --decode | tr -d "\r") 2>&1 | cut -d\( -f 1
      # So that the key name isn't printed, in brackets, but we still see the message if an error occurs like

    - git show-ref
      # git refs in the initial working directory (the git repo of the runner)

    - git config --global "${GITLAB_USER_EMAIL}"
    - git config --global "${GITLAB_USER_NAME}"
      # Relies on email and name being the same as for GitHub

    - git clone -b $GH_REF --depth 1 $GIT_REPO_SSH_URL
      # --depth flag implies --single-branch (

      # Move out of repo
    - pushd /tmp
    - cookiecutter gh:lmmx/py-pkg-cc-template --no-input 
    - popd
      # Move back to repo

    - mv $GH_REPO_NAME/.git /tmp/$GH_REPO_NAME/
      # Move git subdirectory of template-generated repo into the generated package (so it becomes the new repo)

    - cd /tmp/$GH_REPO_NAME/
    - ls -a
    # - git remote set-url origin $GIT_REPO_SSH_URL
      # The pipeline is a git repo (for the commit that was last pushed),
      # so the clone is a submodule and therefore needs to have a remote added to be able to push
    - git add --all ':!*.pem'
    - git commit -m "Cookiecutter template auto-generated by $CI_PROJECT_PATH"
                 -m "CI job URL '$CI_JOB_URL'"
                 -m "CI job started at $CI_JOB_STARTED_AT"
    - git show-ref
      # git refs in the final working directory (the git repo of the new package, via the template-generated repo)
    - git push origin $GH_REF
      # The repo will now contain only what was generated from the cookiecutter template

et voilà!

As explained at the start, the decision of where to place the maintenance burden was to keep it out of this part, and in the template itself. That means I didn't want to manually add particular hidden files/directories like .pre-commit-config.yaml, .github workflows etc. but instead just pull in the template. Any changes to the template would otherwise require changes to this CI automation, which makes small changes require twice the number of files to edit and that kind of imposition immediately starts to put you off iterating on anything in fear of it breaking.

While developing I left a call just before the git push to:

    - python3.9 -c "raise ValueError('Halt early')"

which prevented a few near-misses with committing .pem SSH keys. Be very careful with this stuff! After reviewing the security principles involved and potential failure modes, I'm happy to use this myself and recommend it for others. My setup will only work for me because I hardcoded my template in (lmmx/py-pkg-cc-template). I didn't use a variable for this, as the endpoint is public so CI variables can be injected by anyone with the URL.

A common slip-up is passing in empty strings, which when used in paths give the parent directory. I validate that this isn't happening at the start, again for security.

Potentially over-cautiously, I also added a check that the username is mine, lmmx, as otherwise I can vaguely see a scenario where someone permits access to my account to edit their repo (I think that's doable?) and then uses my credentials to generate things for them, or worse to somehow leak my SSH key into their possession.

You can keep this endpoint private by adding the pipeline trigger ID to your secrets, but here I tried to keep the config simple: the only secret is the GitHub SSH key for write access.

Last but not least I erased the logs from runs where I'd dumped out the environment variables, just for good measure.

Pics or it didn't happen

Before: this is the template with the big green Use this template button

After: this is the result of passing in the name "self-serve-demo" as the package name and waiting a few seconds for the GitHub and GitLab CI workflows to run.

This post is the 3rd of Package templating, a series covering how to generate a skeleton for a Python package with minimal barrier to setup with best practices like linting, pre-commit hooks and tests on CI. Read on for discussion of Templating AWS Lambda functions rather than traditional Python packages