call-repair-sequence-auditor

"Sorry, what?" is data. An agent that plows past it manufactures a failed call.

Conversation analysis calls it other-initiated repair: the moments one speaker signals trouble in hearing or understanding. Human conversation runs about one repair every 1.4 minutes across languages. A phone agent that ignores a repair does not save time - it ends the call with a person who never understood the ask, which is exactly how a "confirmed" outcome turns out wrong later.

When To Use

• after any CALL-E call where the callee asked to repeat, slow down, or confirm which value was meant
• to decide whether a follow-up call should use a chunked, slower goal
• to generate that goal for plan_call directly
• to profile which agent wording keeps causing the trouble (digit-dense, long sentences, long words)

When Not To Use

• to detect sentiment or frustration; use call-summarizer or call-semantic-barge-in-analyzer for pacing and cooperation
• to repair an ambiguous email thread; that is conversation-clarify, which decides whether to call - this skill audits what happened in a call already made
• during a call; this is strictly post-call analysis plus pre-call goal crafting, because CALL-E exposes transcripts, not live audio
• as proof the person failed to understand; absent repairs can mean a clean call or an unengaged callee, and the card says so

Workflow

Audit a finished call

python3 scripts/repair_sequence_auditor.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:

• repair_events[]: turn index, masked span, repair_type (open_class - "huh", "sorry?", "what?"; repetition_request - "can you repeat that"; candidate_understanding - "did you say X or Y"; partial_repeat - quoting a fragment back with a question; specification_request - "which one", "can you slow down"), trouble_source_index + trouble_profile (digit_dense, long_words, long_sentence), and resolution (ADDRESSED / IGNORED / END_OF_CALL)
• repairs_initiated, unresolved_repairs, dominant_trouble_type
• comprehension_trouble: LOW / MODERATE / HIGH (HIGH when 2+ repairs are ignored or 4+ repairs fire in one call)
• recommended_action: continue, verify_understanding_prompt, or redial_with_simplified_goal (with the goal text)

A repair is ADDRESSED when the next agent turn uses a re-delivery marker, commits to one option, restates enough of the trouble source, or gives a short digit-bearing restatement; a pivot to a new topic is IGNORED.

Craft the follow-up goal

python3 scripts/repair_sequence_auditor.py craft --scenario high-trouble-redial

Emits the plan_call inputs JSON whose goal is the same chunked template the card recommends: one fact per sentence, numbers digit by digit, explicit permission to interrupt and ask for repeats.

Scientific Foundation

Research	Relevance
Universal Principles in the Repair of Communication Problems (Dingemanse et al., PLoS ONE 10(9):e0136100, 2015)	The twelve-language CA study our taxonomy and the illustrative 1-repair-per-1.4-minutes baseline come from
An analysis of dialogue repair in virtual assistants (Galbraith, Frontiers in Robotics and AI 11:1356847, 2024)	Replicates the repair framework on Siri and Google Assistant; grounds applying CA repair categories to voice agents
You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification (Addlesee and Eshghi, Frontiers in Dementia, 2024, doi:10.3389/frdem.2024.1343052)	Grounds the craft mode: incremental clarification requests as the assistant-side answer to repair trouble

Citation notes recorded during verification: the Dingemanse study is often miscited to PNAS - it is PLoS ONE; the Addlesee paper is in Frontiers in Dementia, not Frontiers in Computer Science. CALL-E exposes transcripts without prosody or turn offsets, so this skill implements the lexical, text-side approximation, reports counts instead of rates, and labels every output analysis_mode: "heuristic".

Differences from sibling skills

• conversation-clarify detects ambiguity in written threads and decides whether one clarifying call is warranted; this skill audits repair inside a call that already happened and tunes the next one.
• call-semantic-barge-in-analyzer classifies how the callee's turns cooperate with pacing; this skill measures whether they understood at all, and whether the agent noticed.
• call-review checks disclosure and claim support; it does not count repair sequences or profile their causes.