calle-ai/awesome-phone-call-agents/call-cross-call-consistency-checker

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Offline experimental CALL-E helper that compares amounts, dates, and times stated by the agent across two finished calls to the same destination and grades each fact kind CONSISTENT, CONTRADICTED, or ONLY_STATED, plus a consistency-guarded goal template. It is not proof either call lied, a record of truth, or authorization for another call.

Install

npx vskill@latest install calle-ai/awesome-phone-call-agents/call-cross-call-consistency-checker

SKILL.md

call-cross-call-consistency-checker

**Call someone twice and say two different things, and you were never
really trusted the first time.**

Every other skill in this repository analyzes one call. This one analyzes the space BETWEEN two calls to the same person: did the organization tell them the same price, the same date, the same time it told them before? Contradictions across calls are how campaigns lose people - each new call quietly rewrites reality, and the person on the line is the only one who notices.

When To Use

  • when a destination has received two or more calls and any result from

the latest one will be written somewhere

  • before drafting the NEXT call to a person whose earlier call had

disputed or confusing values

  • to generate a consistency-guarded goal for the follow-up plan_call

When Not To Use

  • to decide which call's value is true; the record (or a human with it)

decides - this skill only routes to verification

  • on calls to different destinations; comparing unrelated people's facts

is meaningless

  • as a memory system; it compares two transcripts you hand it, it stores

nothing

  • during a call; strictly post-call analysis plus pre-call goal crafting

Workflow

Compare two finished calls
bash
python3 scripts/cross_call_consistency_checker.py analyze \
--transcript-a path/to/call-a.json --transcript-b path/to/call-b.json

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

  • comparisons[] per fact kind (amount / date_weekday /

date_day / time; weekdays and day-of-months are separate sub-kinds so "Tuesday the 15th" vs "Wednesday the 15th" contradicts on the weekday instead of hiding behind the shared day number):

  • CONSISTENT with the shared values when the calls overlap
  • CONTRADICTED when both calls state the kind and share no value
  • ONLY_STATED when just one call mentions the kind
  • Only AGENT turns are extracted: the organization's statements must stay

consistent; the callee mentioning a different value is a correction, not a record

  • Legitimate reschedules do not self-contradict: "we moved you from the

12th to the 15th" shares a value with a later call that says "the 15th"

  • verdict: CONTRADICTIONS_FOUND / CONSISTENT / NOTHING_TO_COMPARE,

plus unclear paths for empty or agent-less inputs

  • Values are normalized ($45 = "45 dollars"; Tuesday the 15th = weekday +

ordinal; 2 p.m. = 1400) and masked in output

Craft the consistency-guarded goal
bash
python3 scripts/cross_call_consistency_checker.py craft --scenario consistency-guarded-callback

Emits the plan_call inputs JSON whose goal makes the agent state values with their source, acknowledge discrepancies instead of silently picking a side, and never close a call with two unreconciled values.

Scientific Foundation

ResearchRelevance
In Prospect and Retrospect: Reflective Memory Management for Long-term Personalized Dialogue Agents (Tan et al., ACL 2025, arXiv 2502.00299)Long-term dialogue agents must keep personalized facts stable across sessions; our cross-call comparison is the transcript-side audit of exactly that property
Truth-Maintained Memory Agent: Proactive Quality Control for Reliable Long-Context Dialogue (Phadke, Guo, Koch et al., NeurIPS 2025 Workshop on Socially Responsible and Trustworthy Foundation Models, OpenReview)Write-time quality control against false and contradictory memory; our CONTRADICTED routing is the call-records analogue

Citation notes recorded during verification: the RMM paper's full title begins "In Prospect and Retrospect:"; the TMMA paper is a NeurIPS 2025 WORKSHOP paper (not main conference), stated as such. This skill compares two transcripts lexically and labels every output analysis_mode: "heuristic".

Differences from sibling skills

  • call-state-reconciler reconciles platform signals (status,

task_completed, confidence) within one call; this skill reconciles human-readable facts across two calls.

  • adherence-memory-callback remembers callers to make its own calls

smarter; this skill audits any two transcripts for organizational consistency and stores nothing.

  • call-sycophancy-guard catches the agent folding within a call; this

skill catches the organization drifting between calls.

Security

  • Security scanPassed, 90 of 100Pattern checks on the skill's files.
  • Model reviewPassed

Quality evaluation

  • Task evaluationNot runNo evaluation yet. Run one in Skill Studio.

Source

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