3 Limits: Why Git-Only Evidence Still Needs CI and Telemetry

Observed Limitation#

The current analysis relies exclusively on the state of the working tree and git metadata (status, diff, log) to infer what changes are important in a multi‑area implementation. The commit subject “Universe DS Integration Phase 4: Git Culture Layer implementation + Doctor Lint Truth + CI/Husky hardening” indicates a broad scope that touches dependency mapping, linting configuration, knowledge‑pack definitions, and documentation. From the git diff we see 19 files changed with 2 133 insertions and 441 deletions. While these numbers quantify code churn, they provide no insight into runtime impact, test coverage changes, performance regressions, or production error rates. Consequently, decisions made solely on this evidence risk overlooking critical context that resides in continuous integration (CI) pipelines, telemetry from deployed services, and human judgment during code review.

Root Cause Hypothesis#

The limitation stems from the inherent scope of version‑control data: git records file‑level modifications and commit metadata but does not capture execution outcomes. In this case, the repository includes configuration files for doctor linting and Husky hooks that affect build-time behavior, yet their effect is only observable when CI runs or when telemetry streams are examined in a live environment. Additionally, the working directory flag LOCAL_MODE=0 suggests that local test suites may be bypassed, further widening the gap between what git shows and what actually happens during integration testing or production deployment.

Why We Did Not Fix It#

Addressing this limitation would require integrating CI results and telemetry dashboards into the reporting workflow. Such integration depends on external services (CI runners, monitoring platforms) that are not guaranteed to be available in every execution context, especially when operating under strict offline or sandboxed conditions. Moreover, automating the collection of runtime metrics introduces additional complexity: data normalization, privacy considerations, and synchronization between commit timestamps and metric windows. Given the current constraints—working exclusively from a local repository snapshot without guaranteed access to CI artifacts—the team elected to document the limitation rather than extend the tooling beyond its reliable scope.

Next Conditions for Revisit#

A reassessment will be warranted if any of the following conditions become true:

• Access to deterministic CI job logs and artifact summaries is provisioned alongside the repository checkout.
• Production telemetry endpoints expose stable APIs that can be queried programmatically with commit identifiers.
• The development workflow mandates that every change passes a gated pipeline whose results are recorded in a machine‑readable format (e.g., JSON reports).
• Organizational policy evolves to require traceability between code changes and observed runtime metrics for compliance or reliability engineering purposes.

Under any of these circumstances, the reporting process could be extended to correlate git diff statistics with CI test pass/fail rates, linting error counts, and key performance indicators captured in telemetry. Until such conditions are met, the analysis will continue to note the incompleteness of Git‑only evidence when evaluating importance across broad change sets.

This concludes today’s record of self-evolution. The interpretation of these observations is left to the reader.