calle-ai/awesome-phone-call-agents/call-cognitive-load-monitor

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Offline experimental CALL-E transcript heuristics for repetition, confusion phrases and conversational proxies, with advisory scores and script suggestions for human review; not a cognitive assessment or consent determination.

Install

npx vskill@latest install calle-ai/awesome-phone-call-agents/call-cognitive-load-monitor

SKILL.md

call-cognitive-load-monitor

Detect when your caller is overwhelmed — before it costs you the call.

This skill analyzes supplied text for heuristic confusion markers and conversational proxies. It does not measure acoustics, administer a cognitive assessment, or establish consent validity. Reports and suggested script changes require human review; no live integration, message sending or follow-up call is performed.


Why This Skill Exists

Most call-centre quality tools measure whether the agent performed well. This skill measures whether the caller was able to process what was said. When cognitive load peaks — especially at closing, where consent and commitment are given — the validity of that interaction is at risk.

Advisory scope: A CONSENT_AT_RISK label is a suggestion for human review, not a regulatory finding or authorization for a follow-up call. No label establishes that consent is valid or invalid.


Research Scope

The weights and thresholds are unvalidated demonstration choices, not a reproduction of a published acoustic model. Background and claim limitations: `references/research-papers.md`.


Quick Start

bash
python3 scripts/monitor_cognitive_load.py \
--transcript path/to/transcript.json \
--dry-run \
--out /tmp/load_report.json
Validate output schema
bash
python3 scripts/validate_load_report.py --report /tmp/load_report.json

Input

Standard CALL-E transcript — same two formats as other skills:

Format A — array of turns:

json
[
{"role": "agent", "text": "Under the terms of sub-clause 4(b)(ii) of the agreement..."},
{"role": "callee", "text": "Sorry, could you say that again? I'm not following."},
{"role": "agent", "text": "Of course. Regarding the billing section specifically..."},
{"role": "callee", "text": "I still don't understand. What does that mean for me?"}
]

Output — Overload Report

json
{
"call_id": "calle-001",
"analysis_timestamp": "2026-09-17T08:00:00Z",
"overall_cognitive_load": "HIGH",
"load_score": 0.78,
"load_by_phase": {
"opening": 0.12,
"middle": 0.45,
"closing": 0.78
},
"peak_phase": "closing",
"overload_signals": [
{
"type": "repetition_request",
"evidence": "Sorry, could you say that again?",
"turn": 4,
"phase": "middle",
"weight": 0.30
},
{
"type": "confusion_phrase",
"evidence": "I still don't understand. What does that mean for me?",
"turn": 6,
"phase": "middle",
"weight": 0.25
},
{
"type": "jargon_density_spike",
"evidence": "sub-clause 4(b)(ii) of the agreement",
"turn": 3,
"phase": "middle",
"weight": 0.18
}
],
"interaction_dynamics": {
"turn_imbalance_score": 0.62,
"avg_silence_gap_ms": 2800,
"overlap_count": 0,
"participation_ratio": {"agent": 0.74, "callee": 0.26}
},
"script_patches": [
{
"original": "Under the terms of sub-clause 4(b)(ii) of the agreement",
"suggested": "According to our billing rules",
"rationale": "Legal jargon removed; Flesch-Kincaid grade reduced from 18 to 6"
}
],
"consent_validity_flag": "AT_RISK",
"consent_validity_reason": "Load score 0.78 exceeded threshold 0.65 during closing phase where commitment was recorded.",
"recommended_action": "SEND_WRITTEN_CONFIRMATION",
"false_positive_disclaimer": "Cognitive load inference is probabilistic. A flagged call does not constitute a legal finding. Human review is required before any adverse action.",
"flags": ["REQUIRES_HUMAN_REVIEW", "JARGON_DENSITY_HIGH"],
"analysis_mode": "heuristic",
"schema_version": "1.0"
}

Cognitive Load Signals

Linguistic Markers
Signal TypeExampleWeight
repetition_request"Could you repeat that?" / "Say that again?"0.30
confusion_phrase"I don't understand" / "I'm lost" / "What does that mean?"0.25
self_correction"Wait, I mean... actually..."0.15
clarification_request"So what you're saying is...?"0.20
jargon_density_spikeTechnical / legal terminology per 50-word window0.18
hedge_word_cluster"I think", "maybe", "I'm not sure" (3+ in one turn)0.12
Interaction Dynamic Markers
Signal TypeThresholdSignificance
turn_imbalanceAgent:Callee ratio > 3:1Agent dominating — callee cannot process
long_silence_gap> 2 500 msCallee processing delay — indicates high extraneous load
participation_dropCallee turns drop ≥ 40% in closing phaseCognitive withdrawal
Load Levels
Load ScoreLevelRecommended Action
< 0.35LOWPROCEED
0.35–0.64MEDIUMFLAG_FOR_REVIEW
0.65–0.84HIGHSEND_WRITTEN_CONFIRMATION
≥ 0.85CRITICALREPEAT_CALL_WITH_SIMPLER_SCRIPT

Consent Validity Flags

FlagTriggerMeaning
CONSENT_VALIDLoad < 0.65 during closingCaller was processing normally when consenting
CONSENT_AT_RISKLoad ≥ 0.65 during closingCaller may have consented under cognitive strain
CONSENT_UNKNOWNClosing phase not identifiableTranscript structure too short to assess
[!CAUTION]
CONSENT_AT_RISK does not mean consent is invalid. It flags the call for human review and recommends sending written confirmation. Never use this flag as a standalone legal determination.

Command-Line Reference

usage: monitor_cognitive_load.py [-h] --transcript TRANSCRIPT
[--threshold THRESHOLD]
[--dry-run]
[--out OUT]
options:
--transcript Path to the transcript JSON file (required)
--threshold Load score above which level is HIGH (default: 0.65)
--dry-run Analyse without side effects
--out Write report JSON to this path (default: stdout)

Integration with CALL-E

Insert as a post-call QA step in any pipeline where consent or commitment is recorded:

[call ends] → [transcribe] → [monitor_cognitive_load.py]
↓
load=HIGH → send written confirmation email
load=CRITICAL → schedule follow-up call with simplified script

Combine with client-persona-profiler: Analytical (C) and Steady (S) callers have lower jargon tolerance — flag their calls at a lower threshold.


Privacy & Safety

  • Reports contain copied transcript evidence and script-patch text and may contain private data. Keep real-input reports local and access-controlled; use synthetic inputs for examples and tests. Do not publish reports without review.
  • The separate validate_load_report.py check is not automatically run before writing and is not a complete PII redactor or privacy guarantee.
  • The consent-validity flag is advisory only. A human must review before any regulatory or legal action.

Full safety reference: `references/safety.md`

Expected Outcomes & Metrics

The following numbers are unvalidated design targets, not measured results.

MetricExpected TargetNotes
Latency< 50ms per transcriptEvaluates locally without LLM dependencies.
TPR (True Positive Rate)> 85%Identifies cognitive strain in human-annotated datasets.
FPR (False Positive Rate)< 10%Some benign clarification requests may be flagged.

Limitations & Known Constraints

  • Text-Only Modality: Cannot detect sighs, actual silence duration, or exasperated tone. Silence and load fields are text-based proxies, not acoustic measurements.
  • Prototype options: --threshold is currently accepted but unused; reported levels use built-in constants. Short transcripts and a CONSENT_VALID label do not prove that a closing phase or consent occurred.
  • ASR Dependency: If the ASR mistranscribes "I'm lost" as "I boss", the signal is missed.
  • Language Bias: Heuristics are currently calibrated exclusively for English.

Files

skills/call-cognitive-load-monitor/
├── SKILL.md
├── scripts/
│ ├── monitor_cognitive_load.py ← Main analysis runner
│ ├── validate_load_report.py ← Output schema validator
│ └── test_cognitive_load.py ← Test suite (50+ assertions)
└── references/
├── example-transcript.json ← High-load example transcript
├── examples.md ← Usage examples
├── research-papers.md ← Full citations
└── safety.md ← Consent-validity ethics guide

Security

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  • Model reviewPassed

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