call-verbal-irony-detector

What was said is not always what was meant.

call-summarizer reports caller sentiment - what was said. This skill detects verbal irony - the mismatch between the two - and turns it into the next call's strategy. A callee who answers "Oh, great. Just perfect." after a complaint is not happy, and an agent that acts on the literal words will make it worse.

When To Use

• after any CALL-E call where the outcome hinges on a positive-sounding answer inside a complaint discussion
• to decide whether a follow-up call should use a de-escalation goal
• to generate that de-escalation goal for plan_call directly

When Not To Use

• to detect sentiment in general; use call-summarizer for that
• to detect fraud or social engineering; use call-fraud-shield
• during a call; this is strictly post-call analysis plus pre-call goal crafting, because CALL-E exposes transcripts, not live audio
• as proof of the callee's emotional state; text-only irony cues are ambiguous and the card says so

Workflow

Analyze a finished call

python3 scripts/irony_detector.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:

• irony_detected + confidence (high / medium / low / none)
• evidence: turn index, masked span, matched rules
• irony_assessment: "unclear" with a reason when the callee never spoke
• recommended_action: retry_with_deescalation_goal (with the goal text), verify_literal_intent_prompt, or continue

Confidence labels are fixed heuristic score buckets, not empirically calibrated probabilities. Action names are review suggestions only; confirm the person's intent and separately approve any follow-up call or consequential action.

Craft the follow-up goal

python3 scripts/irony_detector.py craft --scenario complaint-followup --language en

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

Scientific Foundation

Research	Relevance
SarcNet: A Multilingual Multimodal Sarcasm Detection Dataset (LREC-COLING 2024)	Motivates marker + context mismatch detection; we use the text modality only
Sarcasm in Sight and Sound / MUStARD++ (arXiv 2310.01430)	Multimodal sarcasm benchmark incl. speech; our contrast rule is a text-only approximation

Both papers study multimodal sarcasm; CALL-E exposes transcripts without prosody, so this skill deliberately implements the text-side approximation and labels every output analysis_mode: "heuristic".

Differences from sibling skills

• call-summarizer reports stated sentiment; this skill reports the stated-vs-meant mismatch and changes the next call's goal.
• call-review audits call compliance; this skill tunes conversational strategy.