call-cross-call-consistency-checker
SkillAI & modelsOffline 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.
Available today. Use it from your connected AI after setup.
No other account needed.
Add ahel to your AI once: Claude, ChatGPT, Cursor, Claude Code or Codex. Then ask it to use this.
Then ask your AI: use the call-cross-call-consistency-checker skill
What this skill tells your AI
The instructions your AI receives, as published by calle-ai/awesome-phone-call-agents in skills/call-cross-call-consistency-checker/SKILL.md and read by ahel’s review.
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
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):CONSISTENTwith the shared values when the calls overlapCONTRADICTEDwhen both calls state the kind and share no valueONLY_STATEDwhen 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, plusunclearpaths 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
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
| Research | Relevance |
|---|---|
| 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-reconcilerreconciles platform signals (status, task_completed, confidence) within one call; this skill reconciles human-readable facts across two calls.adherence-memory-callbackremembers callers to make its own calls smarter; this skill audits any two transcripts for organizational consistency and stores nothing.call-sycophancy-guardcatches the agent folding within a call; this skill catches the organization drifting between calls.
Signals
- GitHub stars
- 104
- Forks
- 527
- Last commit
- Sep 2026
Advanced
- Item type
- skill
- Key
call-cross-call-consistency-checker- Source
- github.com/calle-ai/awesome-phone-call-agents