call-semantic-barge-in-analyzer

Was the person on the other end with you, against you, or barely there?

call-review audits call compliance, including ignored stop requests. This skill answers a different question: how cooperative was the callee, and how should the NEXT call be paced? A callee who answers "Mm-hmm." while you explain is with you; a callee who says "Wait, slow down." three times is telling you your pacing failed - and the fix is a shorter-turn script, not more repetition.

When To Use

• after any CALL-E call where the agent delivered multi-part information, to check whether the callee could keep up
• to decide whether the next call should use a pacing goal (short turns, explicit confirmation points)
• to generate that pacing goal for plan_call directly

When Not To Use

• to audit compliance or ignored stop requests; use call-review
• to detect fraud; use call-fraud-shield
• during a call; CALL-E exposes transcripts, not live audio, so interruptions are read post-hoc from turn text, not from overlapping speech timing
• as a judgment about the person; profiles are pacing advice only and the card says so

Workflow

Analyze a finished call

python3 scripts/barge_in_analyzer.py analyze --transcript path/to/call-result.json

Reads the real get_call_run result shape ({status, result: {transcript}}) or the flat shape used by sibling skill fixtures. Emits a card:

• cooperation_profile: ENGAGED_COOPERATIVE / NEUTRAL / FRUSTRATED_INTERRUPTING / DISENGAGED
• metrics: callee turn counts by class, backchannel density, average agent turn length, and whether the agent shortened its turns after the first barge-in (adaptation)
• evidence: turn index, masked span, classification for every backchannel and barge-in
• pacing_assessment: "unclear" with a reason when the callee never spoke
• pacing_recommendation: shorten_turns (with the pacing goal text), maintain_pacing, or pause_and_confirm

Classification rules: a turn is a backchannel when it is at most 4 words of listening vocabulary after an agent STATEMENT; the same words answering a trailing agent QUESTION count as substantive answers. Bare "look" is deliberately not a barge-in marker (too many benign uses: "I will look into that").

Craft the pacing goal

python3 scripts/barge_in_analyzer.py craft --scenario pacing-followup --language en

Emits the plan_call inputs JSON whose goal is the same template the card recommends on shorten_turns, so analysis and next call stay consistent.

Scientific Foundation

Research	Relevance
Moshi: a speech-text foundation model for real-time dialogue (Kyutai, 2024, arXiv 2410.00037)	Full-duplex turn-taking with backchannels; this skill is the post-call, transcript-only approximation
DuplexGen: Adaptive Synthesis of Human-AI Turn-Taking Dialogues (2026, arXiv 2607.26178)	Turn-taking dialogue synthesis; motivates the backchannel-vs-barge-in distinction

Both papers model real-time full-duplex behavior; CALL-E exposes transcripts without timing, so this skill deliberately implements the text-side approximation and labels every output analysis_mode: "heuristic".

Differences from sibling skills

• call-review flags ignored stop requests (a compliance failure by the agent); this skill profiles callee cooperation and tunes pacing for the next call.
• call-verbal-irony-detector reads what the callee meant versus what they said; this skill reads how they participated.