feat(profiler/behave_shell): emit motor.command_chunking

BEHAVE-EXTRACTOR.md Phase B Step B.4. First implementation —
prototype doesn't ship this primitive.

* SessionContext gains intra_command_iats: per-command tuple of
  IATs between consecutive input events whose timestamps fall
  inside [cmd.start_ts, cmd.end_ts). Excludes the terminator IAT.
  Built by _per_command_iats.
* _features/motor.py:command_chunking(ctx) emits one Observation
  in {fluent, fragmented, single_command}.
  - 0 commands → skip emit
  - 1 command → single_command (registry-allowed point)
  - ≥2 commands → median CV across per-command typed-IATs;
    < CMD_CHUNKING_FLUENT_CV_MAX (0.50) → fluent, else fragmented
  - paste-only sessions (no command has ≥3 typed IATs) → skip emit
    (no honest within-command rhythm to measure)
  Confidence 0.80 / 0.65 / 0.60.
* Calibration grid widened to include motor.command_chunking;
  green across all five shards. Phase B primitive set complete.

Tests: no commands → skip, 1 command → single_command, uniform
typing → fluent, alternating fast/slow → fragmented, paste-only
multi-command → skip emit.
This commit is contained in:
2026-05-03 21:29:31 -04:00
parent d04f91cd8c
commit 8161c67ec5
6 changed files with 158 additions and 0 deletions

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@@ -56,6 +56,9 @@ class SessionContext:
backspace_iats: tuple[float, ...] = field(default_factory=tuple)
kill_line_count: int = 0
# Step B.4 derivations — per-command intra-typing IATs
intra_command_iats: tuple[tuple[float, ...], ...] = field(default_factory=tuple)
def _detect_paste_bursts(
inputs: list[AsciinemaEvent],
@@ -191,6 +194,30 @@ def _segment_commands(inputs: list[AsciinemaEvent]) -> tuple[Command, ...]:
return tuple(cmds)
def _per_command_iats(
commands: tuple[Command, ...],
inputs: list[AsciinemaEvent],
) -> tuple[tuple[float, ...], ...]:
"""Per-command IATs between consecutive input events whose
timestamps fall in ``[cmd.start_ts, cmd.end_ts)``.
Excludes the terminator IAT (the last event at ``cmd.end_ts`` is
the ``\\r``/``\\n`` itself). Returns one tuple per command.
"""
out: list[tuple[float, ...]] = []
for cmd in commands:
prev_t: float | None = None
cmd_iats: list[float] = []
for t, _kind, _data in inputs:
if t < cmd.start_ts or t >= cmd.end_ts:
continue
if prev_t is not None:
cmd_iats.append(max(0.0, t - prev_t))
prev_t = t
out.append(tuple(cmd_iats))
return tuple(out)
def _output_bytes_between(
outputs: list[AsciinemaEvent],
start: float,
@@ -246,6 +273,7 @@ def build_session_context(
_output_bytes_between(outputs, commands[i].end_ts, commands[i + 1].start_ts)
for i in range(len(commands) - 1)
)
intra_command_iats = _per_command_iats(commands, inputs)
return SessionContext(
sid=sid,
@@ -266,4 +294,5 @@ def build_session_context(
backspace_count=backspace_count,
backspace_iats=backspace_iats,
kill_line_count=kill_line_count,
intra_command_iats=intra_command_iats,
)

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@@ -18,6 +18,7 @@ from decnet.profiler.behave_shell._features.cognitive import (
inter_command_latency_class,
)
from decnet.profiler.behave_shell._features.motor import (
command_chunking,
error_correction,
input_modality,
keystroke_cadence,
@@ -33,6 +34,7 @@ FEATURES: tuple[FeatureFn, ...] = (
keystroke_cadence,
motor_stability,
error_correction,
command_chunking,
inter_command_latency_class,
command_branch_diversity,
feedback_loop_engagement,

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@@ -16,6 +16,7 @@ from decnet.profiler.behave_shell._ctx import SessionContext
from decnet.profiler.behave_shell._features._emit import make_observation
from decnet.profiler.behave_shell._thresholds import (
BACKSPACE_IMMEDIATE_MAX_S,
CMD_CHUNKING_FLUENT_CV_MAX,
CV_BURSTY_MAX,
CV_MACHINE_MAX,
CV_STEADY_MAX,
@@ -205,3 +206,49 @@ def error_correction(ctx: SessionContext) -> Iterator[Observation]:
value=value,
confidence=confidence,
)
def command_chunking(ctx: SessionContext) -> Iterator[Observation]:
"""Emit ``motor.command_chunking`` ∈ {fluent, fragmented, single_command}.
* 0 commands → skip (no honest answer).
* 1 command → ``single_command`` (registry-allowed, distinct from
the fluent/fragmented continuum that needs multiple commands).
* ≥2 commands → median CV across per-command intra-typing IATs;
below ``CMD_CHUNKING_FLUENT_CV_MAX`` → fluent, else fragmented.
Skips emission if no command has ≥3 typed IATs to compute a CV
over (paste-driven sessions where every command arrived as one
bulk write — no honest within-command rhythm to measure).
"""
n = len(ctx.commands)
if n == 0:
return
if n == 1:
yield make_observation(
ctx,
primitive="motor.command_chunking",
value="single_command",
confidence=0.80,
)
return
cvs: list[float] = []
for iats in ctx.intra_command_iats:
if len(iats) < 3:
continue
m = statistics.fmean(iats)
if m > 0:
cvs.append(statistics.pstdev(iats) / m)
if not cvs:
return
cv = statistics.median(cvs)
if cv < CMD_CHUNKING_FLUENT_CV_MAX:
value, confidence = "fluent", 0.65
else:
value, confidence = "fragmented", 0.60
yield make_observation(
ctx,
primitive="motor.command_chunking",
value=value,
confidence=confidence,
)

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@@ -104,3 +104,8 @@ TREMOR_RATE_MIN: float = 0.10 # ≥10% sub-floor → tremor
# typo mid-keystroke" (immediate). Beyond this = the operator paused,
# noticed, then went back (deferred).
BACKSPACE_IMMEDIATE_MAX_S: float = 0.50
# ── motor.command_chunking (Step B.4) ───────────────────────────────────────
# Median CV of within-command IATs. Below this → fluent (steady within
# each command); above → fragmented (operator pauses mid-command).
CMD_CHUNKING_FLUENT_CV_MAX: float = 0.50