Gud Boi
6df9ee11bc
A `trio.Nursery.start()`-style wrapper around
`trio.run_process()` that surfaces rc!=0 errors
deterministically, ALWAYS isolates the parent
controlling-tty, and optionally live-relays the child's
std-streams to `log.<level>` per-line. Suits both
short-lived test-runners + long-lived daemons.
`supervise_run_process()`,
- Deterministic rc!=0: pass `check=False` to `trio`
and do our OWN post-drain rc-check from the
supervisor coro body AFTER `own_tn.__aexit__` — NOT
inside the internal nursery, since that would
race-cancel the still-draining relay reader and lose
stderr lines. (Re)build + raise a BARE
`subprocess.CalledProcessError`: `.stderr=` for
programmatic callers + an `add_note()`'d
`|_.stderr:` block for human teardown logs. No
nursery-eg-wrapped CPE to `collapse_eg` around.
- Parent controlling-tty isolation: `stdin=DEVNULL`
always, `stdout=DEVNULL` unless relayed/overridden
(via `stdout=` kwarg w/ `_UNSET` sentinel so explicit
`None` = inherit still works). Prevents a spawned
program from clobbering the launching tty's scrollback
w/ control-seqs.
- Live per-line relay: `relay_stdout=True`/
`relay_stderr=True` → relayed to `log.<relay_level>`
(default `'io'`, our custom level 21). Picked to sort
just above stdlib `INFO`=20 so it shows at usual
`info`/`devx` levels yet stays separately filterable;
`runtime`=15 was REJECTED as a default since it'd be
silently filtered at usual verbosity — footgun for
daemon supervisors whose whole point is visibility.
STREAMED, not buffered-until-exit.
- Non-blocking `tn.start()` semantics: live
`trio.Process` handed up via
`task_status.started()` immediately (else
`tn.start()` would block till child exit, losing
the long-lived-daemon use case). Supervise/relay bg
tasks run to completion in this coro.
- `**run_process_kwargs` forwarded verbatim (env, shell,
cwd, start_new_session, executable, ...); MANAGED keys
(`stdin`/`stdout`/`stderr`/`check`) win on conflict.
- Crash-handling layer intentionally NOT baked in —
compose `maybe_open_crash_handler()` ON TOP at the
call-site.
`_relay_stream_lines()` helper,
- Concurrent pipe-drain reader. MANDATORY whenever piping
w/o `capture_*` since nothing else drains the OS pipe —
child blocks on `write()` once kernel buf (~64KiB) fills
→ deadlock.
- Modes (combine freely): `emit`-only live relay,
`accum`-only silent drain+capture (for the CPE note),
or both. Per-line splitting handles cross-chunk
residuals + flushes any trailing un-newline-term'd line
at EOF.
`_add_stderr_note()` helper,
- Attaches an indented `|_.stderr:` note to a CPE via
`add_note()` for legible rc!=0 reporting at teardown.
Tests (`tests/trionics/test_subproc.py`),
- Hermetic `trio`-only (no actor-runtime).
- `test_stdout_relayed_per_line`: per-line stdout relay.
- `test_parent_tty_isolated`: child fd1 is OUR pipe (no
`/dev/pts/*`), fd0 pinned to `/dev/null`.
- `test_no_deadlock_on_big_unnewlined_output`: 200KiB
no-newline output completes under `fail_after(2)` —
exercises the concurrent drain (without it, the child
blocks at ~64KiB).
- `test_stderr_relay_and_cpe_rebuild`: rc!=0 w/
`relay_stderr=True` → bare `CalledProcessError` w/ the
`.stderr` note + per-line live relay.
- `test_nonrelay_cpe_note`: rc!=0 w/o relay → same
deterministic post-drain CPE w/ `.stderr` note (silent
drain+capture path).
Re-export `supervise_run_process` from `tractor.trionics`.
Prompt-IO: ai/prompt-io/claude/20260601T231429Z_0e3e008b_prompt_io.md
(this patch was generated in some part by [`claude-code`][claude-code-gh])
[claude-code-gh]: https://github.com/anthropics/claude-code
(cherry picked from commit
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README.md
AI Prompt I/O Log — claude
This directory tracks prompt inputs and model outputs for AI-assisted development using claude (Claude Code).
Policy
Prompt logging follows the NLNet generative AI policy. All substantive AI contributions are logged with: - Model name and version - Timestamps - The prompts that produced the output - Unedited model output (.raw.md files)
Usage
Entries are created by the /prompt-io skill or automatically via /commit-msg integration.
Human contributors remain accountable for all code decisions. AI-generated content is never presented as human-authored work.