ci: shorten stale PR timeline to 30 days stale + 7 days close

[Yicong-Huang]

What changes were proposed in this PR?

Shorten the auto-stale timeline for pull requests in .github/workflows/stale.yml: PRs are now marked stale after 30 days of inactivity (was 60) and closed 7 days later (was 30), so the total inactivity window drops from 90 to 37 days. The stale/close messages and header comment are updated to match. Escape hatches (pinned label, milestone, draft state, any activity) are unchanged.

Any related issues, documentation, discussions?

Fixes #6865

How was this PR tested?

• YAML-only config change to actions/stale inputs; no code paths affected.
• Verified the new values and messages are consistent throughout the file.
• The workflow runs on its weekly schedule and can be exercised via workflow_dispatch.

Was this PR authored or co-authored using generative AI tooling?

Generated-by: Claude Code (Fable 5)

🤖 Generated with Claude Code

[github-actions]

Automated Reviewer Suggestions

Based on the git blame history of the changed files, we recommend the following reviewers:

• No candidates found from git blame history.

[codecov-commenter]

Codecov Report

✅ All modified and coverable lines are covered by tests.
✅ Project coverage is 77.30%. Comparing base (39db110) to head (fa7d5bf).
⚠️ Report is 159 commits behind head on main.

Additional details and impacted files

@@             Coverage Diff              @@
##               main    #6868      +/-   ##
============================================
+ Coverage     70.57%   77.30%   +6.73%     
- Complexity     3425     3523      +98     
============================================
  Files          1146     1161      +15     
  Lines         45062    45922     +860     
  Branches       4960     5101     +141     
============================================
+ Hits          31802    35501    +3699     
+ Misses        11613     8839    -2774     
+ Partials       1647     1582      -65     

Flag	Coverage Δ
access-control-service	70.00% <ø> (ø)
agent-service	76.76% <ø> (ø)
amber	69.09% <ø> (+2.37%)	⬆️
computing-unit-managing-service	20.49% <ø> (+2.77%)	⬆️
config-service	66.66% <ø> (+14.35%)	⬆️
file-service	67.21% <ø> (+0.40%)	⬆️
frontend	82.58% <ø> (+13.21%)	⬆️
notebook-migration-service	78.94% <ø> (ø)
pyamber	92.15% <ø> (+0.41%)	⬆️
workflow-compiling-service	55.14% <ø> (ø)

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[github-actions]

⚠️ Benchmark changes need a look

🟢 0 better · 🔴 7 worse · ⚪ 8 noise (<±5%) · 0 without baseline

Compared against main f02dd2f benchmarked on this same runner, so the delta is largely free of cross-runner hardware noise. The "7d avg" column still reflects the gh-pages dashboard. Treat <±5% as noise unless repeated.

Dashboard · Run

	config	throughput	MB/s	latency	max Δ latest / 7d
🔴	bs=10 sw=10 sl=64	363	0.221	26,533/39,422/39,422 us	🔴 +21.9% / 🔴 +137.6%
⚪	bs=100 sw=10 sl=64	786	0.48	126,402/153,171/153,171 us	⚪ within ±5% / 🔴 +40.4%
🔴	bs=1000 sw=10 sl=64	880	0.537	1,134,597/1,227,642/1,227,642 us	🔴 +5.1% / 🔴 +17.3%

Baseline details

Latest main f02dd2f from same runner

config	metric	PR	latest main	7d avg	Δ latest	Δ 7d
bs=10 sw=10 sl=64	throughput	363 tuples/sec	412 tuples/sec	754.55 tuples/sec	-11.9%	-51.9%
bs=10 sw=10 sl=64	MB/s	0.221 MB/s	0.251 MB/s	0.461 MB/s	-12.0%	-52.0%
bs=10 sw=10 sl=64	p50	26,533 us	23,438 us	12,816 us	+13.2%	+107.0%
bs=10 sw=10 sl=64	p95	39,422 us	32,347 us	16,594 us	+21.9%	+137.6%
bs=10 sw=10 sl=64	p99	39,422 us	32,347 us	19,806 us	+21.9%	+99.0%
bs=100 sw=10 sl=64	throughput	786 tuples/sec	810 tuples/sec	969.38 tuples/sec	-3.0%	-18.9%
bs=100 sw=10 sl=64	MB/s	0.48 MB/s	0.494 MB/s	0.592 MB/s	-2.8%	-18.9%
bs=100 sw=10 sl=64	p50	126,402 us	123,870 us	103,584 us	+2.0%	+22.0%
bs=100 sw=10 sl=64	p95	153,171 us	158,888 us	109,097 us	-3.6%	+40.4%
bs=100 sw=10 sl=64	p99	153,171 us	158,888 us	117,304 us	-3.6%	+30.6%
bs=1000 sw=10 sl=64	throughput	880 tuples/sec	918 tuples/sec	1,004 tuples/sec	-4.1%	-12.3%
bs=1000 sw=10 sl=64	MB/s	0.537 MB/s	0.56 MB/s	0.613 MB/s	-4.1%	-12.3%
bs=1000 sw=10 sl=64	p50	1,134,597 us	1,085,772 us	1,002,357 us	+4.5%	+13.2%
bs=1000 sw=10 sl=64	p95	1,227,642 us	1,168,099 us	1,046,463 us	+5.1%	+17.3%
bs=1000 sw=10 sl=64	p99	1,227,642 us	1,168,099 us	1,073,661 us	+5.1%	+14.3%

Raw CSV

config_idx,batch_size,schema_width,string_len,num_batches,total_ms,total_tuples,total_bytes,tuples_per_sec,mb_per_sec,lat_p50_us,lat_p95_us,lat_p99_us
0,10,10,64,20,551.58,200,128000,363,0.221,26532.66,39422.21,39422.21
1,100,10,64,20,2544.69,2000,1280000,786,0.480,126402.42,153170.57,153170.57
2,1000,10,64,20,22728.69,20000,12800000,880,0.537,1134597.35,1227642.04,1227642.04

[mengw15]

LGTM