Wednesday, November 20, background execution was working. But I was doing everything manually.
Run workflow in background. Wait for it to finish. Hope it worked. Manually create commit. Manually create PR.
No visibility into what actually happened. No logs. No confirmation. No timing data.
This worked for one repo. It didn’t scale to testing across more repos.
The Missing Pieces
Background execution solved timing. But it created new problems:
- No confirmation: Did the workflow actually run? Did it finish? How do I know?
- Manual git operations: I had to manually commit changes and create PRs every single time.
- No timing data: How long did it take? Was it consistent with other runs?
- No way to understand what happened: Output was logged, but the logs were raw and unstructured. Hard to parse what actually occurred.
I needed automation. The full workflow: run, log, time, commit, create PR.
The First Script
I asked Claude to help me solve this. Claude came up with a bash script that would automate the testing for my first workflow.
The script would:
- Run the workflow in background mode
- Log output to a timestamped JSON file
- Track execution time
- Commit the changes and the log file
- Create a PR with all the results
Claude generated a bash script that did exactly this.
Analyzing the Logs
Once I had logs from multiple repos, I would need a way to analyze them.
I came up with the idea of two reviewers. The first reviewer would focus on a single repo. The second would look at all repos together to get a more comprehensive view of issues and verify the first reviewer.
This two-stage review process would help identify patterns across repos and catch issues that weren’t obvious when looking at just one repo in isolation.
Understanding Log Structure
As I ran the script, I realized the reviewers would need help understanding what was actually happening in the logs.
The key question: Was Claude running a command, or was it an invoked agent?
I had Claude analyze the log files and determine the key things about the log structure. With that analysis, Claude created a file documenting how to understand the logs – particularly how to distinguish between Claude running commands versus invoked agents running commands.
That documentation file would then be used by the reviewers to help them correctly analyze the log files.
The Foundation for Systematic Testing
Now I had the infrastructure to test systematically:
- Script to run workflows in background
- Structured JSON logs
- Documentation for understanding log structure
- Timing data for each run
- Automated git operations
- Two-stage review process for analysis
Everything I needed to test my first workflow across multiple repositories.
What This Enabled
Batch scripts transformed testing from manual process to automated system.
Before scripts:
- Run workflow manually
- Try to remember what happened
- Manually create commit
- Manually create PR
- One repo at a time
- No timing data
- No systematic logs
After scripts:
- One command per repo
- Complete logs automatically saved
- Timing tracked for every run
- Git operations automated
- Can run multiple repos in parallel
This made systematic testing across many repos actually feasible.
Wednesday Evening
Wednesday, November 20, I had a batch script working for my first workflow.
Background execution for speed and consistency. Shell scripts for automation and logging. JSON logs for data persistence and analysis.
But the script automated a single workflow. I had four workflows that needed to run in series on each group of repos. I needed a master script to handle that.