feat(github): CreatePR with dedicated token. This should allow the other GHActions to be triggered at PR creation

[chouetz]

What does this PR do?

Change the token used to create the CWS-BTFHUB automatic PR

Motivation

Since migration of some (required) jobs to github, there are not generated when the PR is created via an automation using the default GITHUB_TOKEN (as per documentation) I try to use a github app to generate a token to have them generated

Additional Notes

Possible Drawbacks / Trade-offs

Describe how to test/QA your changes

Reviewer's Checklist

• [ ] If known, an appropriate milestone has been selected; otherwise the Triage milestone is set.
• [ ] Use the major_change label if your change either has a major impact on the code base, is impacting multiple teams or is changing important well-established internals of the Agent. This label will be use during QA to make sure each team pay extra attention to the changed behavior. For any customer facing change use a releasenote.
• [ ] A release note has been added or the changelog/no-changelog label has been applied.
• [ ] Changed code has automated tests for its functionality.
• [ ] Adequate QA/testing plan information is provided. Except if the qa/skip-qa label, with required either qa/done or qa/no-code-change labels, are applied.
• [ ] At least one team/.. label has been applied, indicating the team(s) that should QA this change.
• [ ] If applicable, docs team has been notified or an issue has been opened on the documentation repo.
• [ ] If applicable, the need-change/operator and need-change/helm labels have been applied.
• [ ] If applicable, the k8s/<min-version> label, indicating the lowest Kubernetes version compatible with this feature.
• [ ] If applicable, the config template has been updated.

[pr-commenter[bot]]

Bloop Bleep... Dogbot Here

Regression Detector Results

Run ID: fc9a7eaa-257f-4226-934f-8d367bf585bd Baseline: d130ef8004c9ce2591b86b97b6816ee1118cb7b6 Comparison: 6557d540e5d48a22b1b69f5dcedcfb8a8c6265ce Total CPUs: 7

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Experiments ignored for regressions

Regressions in experiments with settings containing erratic: true are ignored.

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	idle	memory utilization	-0.03	[-0.05, +0.00]
➖	file_to_blackhole	% cpu utilization	-0.10	[-6.65, +6.45]
➖	file_tree	memory utilization	-0.80	[-0.86, -0.73]

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.98	[+0.55, +3.41]
➖	process_agent_standard_check	memory utilization	+0.13	[+0.09, +0.17]
➖	trace_agent_json	ingress throughput	+0.05	[+0.02, +0.08]
➖	tcp_syslog_to_blackhole	ingress throughput	+0.04	[-0.02, +0.10]
➖	tcp_dd_logs_filter_exclude	ingress throughput	-0.00	[-0.05, +0.05]
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.03, +0.03]
➖	trace_agent_msgpack	ingress throughput	-0.02	[-0.05, +0.01]
➖	idle	memory utilization	-0.03	[-0.05, +0.00]
➖	file_to_blackhole	% cpu utilization	-0.10	[-6.65, +6.45]
➖	process_agent_real_time_mode	memory utilization	-0.10	[-0.14, -0.07]
➖	process_agent_standard_check_with_stats	memory utilization	-0.11	[-0.15, -0.07]
➖	otel_to_otel_logs	ingress throughput	-0.37	[-1.09, +0.35]
➖	file_tree	memory utilization	-0.80	[-0.86, -0.73]

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".

[agent-platform-auto-pr[bot]]

[Fast Unit Tests Report]

On pipeline 38420038 (CI Visibility). The following jobs did not run any unit tests:

Jobs:

• tests_deb-arm64-py3
• tests_deb-x64-py3
• tests_flavor_dogstatsd_deb-x64
• tests_flavor_heroku_deb-x64
• tests_flavor_iot_deb-x64
• tests_rpm-arm64-py3
• tests_rpm-x64-py3
• tests_windows-x64

If you modified Go files and expected unit tests to run in these jobs, please double check the job logs. If you think tests should have been executed reach out to #agent-devx-help

[pr-commenter[bot]]

Regression Detector

Regression Detector Results

Run ID: cb9acb5d-62fd-489d-a1d6-77a04f4c31db Metrics dashboard Target profiles

Baseline: ac1a2ac3575b907e47fd9e391052c7890c2c962f Comparison: 86aa7a9f101bf28bac6da878500c0131ad913c60

Performance changes are noted in the perf column of each table:

• ✅ = significantly better comparison variant performance
• ❌ = significantly worse comparison variant performance
• ➖ = no significant change in performance

No significant changes in experiment optimization goals

Confidence level: 90.00% Effect size tolerance: |Δ mean %| ≥ 5.00%

There were no significant changes in experiment optimization goals at this confidence level and effect size tolerance.

Fine details of change detection per experiment

perf	experiment	goal	Δ mean %	Δ mean % CI	links
➖	pycheck_1000_100byte_tags	% cpu utilization	+3.24	[-1.68, +8.15]	Logs
➖	uds_dogstatsd_to_api_cpu	% cpu utilization	+1.58	[+0.69, +2.47]	Logs
➖	idle	memory utilization	+0.71	[+0.66, +0.77]	Logs
➖	basic_py_check	% cpu utilization	+0.49	[-2.17, +3.15]	Logs
➖	tcp_dd_logs_filter_exclude	ingress throughput	+0.00	[-0.01, +0.01]	Logs
➖	uds_dogstatsd_to_api	ingress throughput	-0.00	[-0.00, +0.00]	Logs
➖	file_tree	memory utilization	-0.02	[-0.09, +0.05]	Logs
➖	otel_to_otel_logs	ingress throughput	-0.96	[-1.77, -0.16]	Logs
➖	tcp_syslog_to_blackhole	ingress throughput	-2.12	[-14.75, +10.51]	Logs

Explanation

A regression test is an A/B test of target performance in a repeatable rig, where "performance" is measured as "comparison variant minus baseline variant" for an optimization goal (e.g., ingress throughput). Due to intrinsic variability in measuring that goal, we can only estimate its mean value for each experiment; we report uncertainty in that value as a 90.00% confidence interval denoted "Δ mean % CI".

For each experiment, we decide whether a change in performance is a "regression" -- a change worth investigating further -- if all of the following criteria are true:

1. Its estimated |Δ mean %| ≥ 5.00%, indicating the change is big enough to merit a closer look.

2. Its 90.00% confidence interval "Δ mean % CI" does not contain zero, indicating that if our statistical model is accurate, there is at least a 90.00% chance there is a difference in performance between baseline and comparison variants.

3. Its configuration does not mark it "erratic".