external-gitcode-ascend-msverl-daily-regression-triage

name	external-gitcode-ascend-msverl-daily-regression-triage
description	Triage a daily msverl regression run by reading the baseline comparison log, stopping on success, extracting the most relevant training failure evidence from the daily training log when needed, collecting recent commits from verl main and MindSpeed master, and ranking the most likely culprit commits with concise fix-direction guidance.
original-name	msverl-daily-regression-triage
synced-from	https://gitcode.com/Ascend/agent-skills
synced-date	2026-05-26
synced-commit	1f7666e7768a0ceb21bb1d40ce4b5179fcb6f1d6
license	UNKNOWN

MSVerl Daily Regression Triage

Use this skill when a fixed daily verl + MindSpeed training job has run and Codex needs to decide whether the result is healthy, whether there is a training failure or an accuracy regression, and which recent commit is the most likely cause.

Defaults

• Baseline comparison log: /home/st_daily_verl/msverl.log
• Training log pattern: /home/st_daily_verl/logs/msverl_YYYYMMDD.log
• verl repo: https://github.com/verl-project/verl.git on main
• MindSpeed repo: https://gitcode.com/Ascend/MindSpeed.git on master
• Cache root for temporary clones: /tmp/msverl-skill-cache
• Time window: from local previous day 00:00:00 to the task execution time

Hard Stop Rules

• Read the comparison log first.
• If it contains mean abs diff: and the parsed value is exactly 0, stop and report success.
• If it contains mean abs diff: and the value is non-zero, classify as accuracy_regression.
• If it contains error, please check log, classify as train_error.
• If the comparison log is ambiguous, report unknown and explain what evidence is missing before doing expensive work.

Workflow

1. Run parse_result_log.py on the comparison log.
2. Stop immediately on pass.
3. For train_error, run extract_failure_tail.py against the daily training log and keep only the final high-signal error block.
4. For accuracy_regression, use the parsed reward lists and mean abs diff as the primary evidence.
5. Sync lightweight local clones with sync_repos.py.
6. Collect recent commits with list_recent_commits.py for both repositories inside the default time window unless the user gives a different one.
7. Rank suspects with rank_candidate_commits.py.
8. Inspect diffs only for the top few commits when titles and touched files are not enough to explain a plausible fix direction.

Cost Controls

• Never load the whole training log unless the tail-based extractor fails twice.
• Start with the log tail only; prefer the last traceback or last ERROR block.
• Rank commits using title and touched files before reading diffs.
• Limit deep diff reading to the top 3 candidates per repository unless the evidence is still weak.

Expected Output

Return a compact report with:

• status: pass, train_error, accuracy_regression, or unknown
• time_window
• evidence_summary
• candidate_repo
• candidate_commits
• confidence: high, medium, or low
• fix_direction

When evidence is weak, say so clearly instead of forcing a single-commit claim.

References

• Run triage_msverl_regression.py for an end-to-end local workflow.
• Use parse_result_log.py and extract_failure_tail.py separately when validating logs by hand.
• Use list_recent_commits.py when you need a raw recent-commit inventory without ranking.