## 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
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