Commit Graph

4 Commits

Author SHA1 Message Date
6763fceb0b feat(profiler/behave_shell): emit motor.paste_burst_rate
BEHAVE-EXTRACTOR.md Phase A Step 3. Same paste-event ratio as
motor.input_modality but coarser-bucketed: this is the *habit*
signal (does the operator reach for paste at all?), where
input_modality is the dominant-channel signal.

* _features/motor.py:paste_burst_rate(ctx) emits one Observation
  per session in {none, occasional, habitual} with confidence
  0.70 / 0.70 / 0.80.
* Thresholds: PASTE_RATE_OCCASIONAL_MIN=0.10,
  PASTE_RATE_HABITUAL_MIN=0.50.

Splits YOU-sim from LW/CLAUDE-FF/CLAUDE-CL — LLM-driven sessions
paste habitually, real humans rarely paste.

Tests: pure-typed → none; 1-paste-in-10 → occasional;
paste-majority → habitual; output-only → no observation; habitual
confidence > occasional confidence.
2026-05-03 07:49:03 -04:00
879f5e731b feat(profiler/behave_shell): emit motor.input_modality
BEHAVE-EXTRACTOR.md Phase A Step 2. The first primitive — picked
first because it has the highest discriminative value (HUMAN vs
everyone) and the simplest implementation (paste-event ratio over
total inputs).

* _features/motor.py:input_modality(ctx) emits one Observation
  per session in {typed, pasted, mixed} with confidence 0.75 / 0.70.
* _features/_emit.py centralises the make_observation helper so
  every feature module gets the same Window/source/evidence_ref
  boilerplate without copy-paste.
* Thresholds inherited from the prototype's calibration history
  (MODALITY_PASTED_MIN=0.40, MODALITY_TYPED_MAX=0.05).
* Zero-input session skips emission — registry doesn't admit
  "unknown" here.

Tests: pure-typed → typed, pure-pasted → pasted, mixed → mixed,
output-only session → no observation, full envelope round-trip.
2026-05-03 07:47:38 -04:00
c9a81a23c2 feat(profiler/behave_shell): asciinema parser + paste-burst detection
BEHAVE-EXTRACTOR.md Phase A Step 1. Lays the shared primitives that
Steps 2-3 (motor.input_modality, motor.paste_burst_rate) will
consume:

* parse_shard_line / parse_shard turn a shard JSONL line/file into
  AsciinemaEvents, skipping headers and malformed records.
* PasteBurst dataclass + _detect_paste_bursts group consecutive
  paste-class input events (len(d) >= 4 chars per the prototype's
  empirical floor) into contiguous bursts, splitting on IAT gaps
  larger than PASTE_BURST_MAX_IAT_S (200ms).
* SessionContext now carries iats and paste_bursts derivations.
* Threshold constants harvested from
  BEHAVE/prototype_extractors/shell/extract.py — calibrated against
  the five 2026-05-02 shards.

Tests cover pure-typed, pure-pasted, mixed streams; close vs far
paste events; typed events breaking a burst; PasteBurst immutability;
and the JSON parser's junk handling.
2026-05-03 07:46:01 -04:00
f8eae04e5d feat(profiler/behave_shell): scaffold extract_session entry point
BEHAVE-EXTRACTOR.md Phase A Step 0. Lays the package skeleton
(__init__/extract/_parse/_ctx/_thresholds/_features) with empty
FEATURES = (), so the worker plumbing in BEHAVE-INTEGRATION Phase 4
has a stable import path before any primitive lands.

extract_session() builds a SessionContext once and fans the
registered feature functions across it; at Step 0 that fan-out is
empty and the function yields nothing. Step 1 (asciinema parser +
paste-burst detector) and Step 2 (motor.input_modality) land next.

Smoke suite asserts the empty contract: empty stream → no
observations, single event → t_start == t_end, multi-event → events
routed into input_events / output_events by kind, evidence_ref
defaults to "session:<sid>" or honours an explicit override.
2026-05-03 07:42:09 -04:00