Alex Kim

From asking about code to implementing and verifying real changes.

Implemented changes and ran tests across separate tasks. The final output and last round of changes were not fully verified in a second independent task.

Using connected tools and checking what actually happened.

Updated a spreadsheet, read the same cells again, and compared the saved values while checking that existing rows were preserved.

Applying reusable procedures and improving them through use.

Applied a skill, reviewed and revised its procedure, then checked the outcome in another execution. Failed results remained marked as failed.

Running automatic checks at the moments that matter.

Stop hooks executed and recorded timing and errors. Their checking, blocking, and exception-handling effects were not established.

Giving an agent the right information, and correcting it when needed.

Compared an evaluator’s interpretation with the original material, corrected a mistaken context judgment, and carried that correction into the final report.

Carrying useful knowledge across sessions and applying it again.

Saved rules and a direction for future work. No verified link showed the same material being applied in a different session.

Designing feedback, retries, and clear stopping conditions.

Implemented and ran attribution checks, a stop-on-pass rule, and a maximum of two regeneration attempts. Resume behavior across executions was not verified.

Connecting stages with explicit inputs, outputs, and dependencies.

Connected task material to generated notes, evaluation results, and a final aggregation. Input checks and dependency recovery across the whole flow were not established.

Dividing work between agents and checking the combined result.

Separated writing and evaluation roles, integrated returned results, and resolved conflicting judgments against original material. Complete coverage checks were not verified.

Keeping work separate and delivering it without unrelated changes.

Edited in a separate worktree, reviewed the change scope, and delivered a PR. Preservation of the original checkout and final delivery consistency were not fully verified.

Choosing models for a reason and checking the effect of that choice.

Explicitly assigned a model to evaluation work, with the execution model visible in the records. Comparable quality, cost, or speed evidence was missing.

Evaluating AI outputs against criteria, then checking the evaluation itself.

Defined AI tasks and success criteria, evaluated multiple executions, corrected grading errors, and retained failed outcomes in the final aggregation.