Parallelize skill metadata stats

[jif-oai]

Summary

This switches skill discovery to the simpler same-connection scalar request shape.

After reading a skills directory, discovery now starts the existing fs/getMetadata calls for all visible entries in that directory before awaiting the results. There is no JSON-RPC batch frame and no new filesystem API; remote filesystems use the existing request-id multiplexing on the same exec-server connection.

This is the scoped alternative to the batch-frame approach in #29074 / #29075.

What changed

• Collect visible directory entries before processing them.
• Run their existing fs.get_metadata(...) calls with join_all.
• Process the results in the original directory order, so skill discovery behavior stays the same.

Benchmarks

Fresh local benchmark against generated skill trees over a real exec-server remote filesystem. The benchmark calls the actual load_skills_from_roots path, so this includes directory reads, metadata stats, SKILL.md reads, and parsing.

Times are p50 milliseconds from 5 samples after 1 warmup, using warmed runs.

Scenario	Legacy main	Batch frame stack (#29074 / #29075)	Same-connection scalar stack
100 flat skills	377.4	389.0	378.6
500 flat skills	1983.2	1856.6	1757.5

Takeaway: for the actual skill discovery path, same-connection scalar is tied with legacy at 100 skills and best at 500 skills. The batch-frame stack does not show enough win here to justify the extra protocol/API surface.

Benchmark command:

• just test -p codex-exec-server benchmark_remote_skill_discovery --run-ignored ignored-only --no-capture

Checked locally with:

• just test -p codex-core-skills
• just bazel-lock-update
• just bazel-lock-check

[jif-oai]

@codex review

[chatgpt-codex-connector]

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