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A framework for orchestrating forensic collection, processing and data export

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It's probably a good idea to have a github action that calculates unit test coverage. Raising this issue to track ideas and any implementation.

The instructions in [docs/recipe-list.md](https://github.com/log2timeline/dftimewolf/blob/main/docs/recipe-list.md) generated by [docs/generate_recipe_doc.py](https://github.com/log2timeline/dftimewolf/blob/main/docs/generate_recipe_doc.py) say to generate the recipe list by running: ``` poetry install -d python docs/generate_recipe_doc.py data/recipes ``` But neither of these commands work: ```...

When running the gcp_logging_collect recipe, the collection will fail silently if it exceeds the cloud logging API quota limit. Based on my testing, this will first issue a 'google_api_exceptions.TooManyRequests' exception....

ref: https://github.com/log2timeline/dftimewolf/pull/673#discussion_r1031905743

Adds a new workspace mobile/device recipe to pull these logs and put them into Timesketch.

Upgrading from 2.x to 3.x caused new errors to appear. Fix them.

Bumps [jinja2](https://github.com/pallets/jinja) from 3.1.3 to 3.1.4. Release notes Sourced from jinja2's releases. 3.1.4 This is the Jinja 3.1.4 security release, which fixes security issues and bugs but does not otherwise...

dependencies

Possible GCP logging API change (libcloudforensics?) breaking gcp-logging_collect recipe. ``` $ dftimewolf gcp_logging_collect glassbro-production 'resource.type="gce_instance" resource.labels.instance_id="5406509864760928785" log_name="projects/glassbro-production/logs/cloudaudit.googleapis.com%2Factivity" timestamp>="2023-09-25" timestamp