call-apology-effectiveness-auditor

call-apology-effectiveness-auditor is a skill for Claude Code, Codex from CALLE-AI/awesome-phone-call-agents. It costs 87 tokens per session (1,250 once invoked), scanned A, original, MIT.

A transcript checker for support, collections, or clinic calls that finds customer complaints and assesses the agent’s apology against six parts, such as accepting responsibility and offering a repair.

In plain words
What is it for?
Use it after calls or in quality-review pipelines to identify missing or deflecting apologies and prepare a clearer recovery goal.
Why use it?
It helps reveal apologies that sound polite but do not address the problem. It separates a complete recovery from a brief, formulaic response.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it after calls or in quality-review pipelines to identify missing or deflecting apologies and prepare a clearer recovery goal.

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Install with agentmods
npx agentmods add skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-apology-effectiveness-auditor
Clone the repo
git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for call-apology-effectiveness-auditor

README.md
[![agentmods](https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor/github.svg)](https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor)
Your own site
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor/github.svg" alt="Measured on agentmods" height="20"></a>

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Your own site · 80×15
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 87 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,250 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00087 $0.01250
Opus 5.5 $0.00035 $0.00500
Sonnet 5.5 $0.00017 $0.00250
Haiku 4.5 $0.00009 $0.00125

Measured 16d ago against content hash ad8844d53cd4, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

call-apology-effectiveness-auditor scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 16d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/apology_effectiveness_auditor.py, scripts/test_apology_effectiveness_auditor.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/call-apology-effectiveness-auditor/SKILL.md · 107 lines

How it starts

The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.

call-apology-effectiveness-auditor

"I'm sorry you feel that way" is not an apology. It is a complaint about the person who had one.

Apology effectiveness is component-decomposable: research on real conflict apologies shows that a working apology is not one utterance but a stack of parts - acknowledgment of responsibility, concrete repair, brief explanation, expressed regret, forbearance, and (optionally) a request for forgiveness - and that acknowledgment carries the most weight. When a callee raises a grievance on a call, an agent that says only "I'm sorry about that" and moves on to scheduling has produced a rote apology: audible, and empty. This skill detects the grievance and grades the recovery response against that component stack.

When To Use

  • after support, collections, or clinic calls where the callee may have raised a grievance (delay, broken promise, repeated contact)
  • in QA pipelines, to find calls where the recovery was rote, deflecting, or missing entirely
  • before recovery callbacks, to craft an apology-protocol goal that names the miss and offers concrete repair

When Not To Use

  • as a measure of sincerity or relationship health; the components are surface-lexical markers - an agent can say "that's on us" with no feeling behind it
  • to force apologies on every failure; sometimes the right posture is no apology at all, because the agent cannot own fault it does not have
  • during a call; strictly post-call analysis plus pre-call goal crafting
  • on non-English transcripts; the lexicons are English-only

Workflow

Audit a finished call

python3 scripts/apology_effectiveness_auditor.py analyze \
  --transcript path/to/call-result.json

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

  • grievance_turn_index / grievances_total / grievance_evidence: where the first grievance was voiced (callee turns only), how many grievance-bearing callee turns exist, and the masked evidence sentence
  • apology_detected / apology_evidence: whether any agent turn from the grievance onward contains an apology, and the masked sentence
  • components_present: the six-component markers found in the graded window (the apology sentence plus the following agent turn, since explanations often arrive one turn late)
  • typology: rote / empathic / explanatory (empathic > explanatory

    rote)

  • verdict: EFFECTIVE_APOLOGY (acknowledgment plus repair or explanation) / PARTIAL_APOLOGY / NON_APOLOGY (deflection override) / GRIEVANCE_UNADDRESSED / NO_GRIEVANCE, plus unclear paths (empty transcript, fewer than two turns)

Read the full file on GitHub · 107 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 16d ago First seen · 107 lines · 87 tokens per session scan A ad8844d53cd4

Subscribe to this mod's changes

call-apology-effectiveness-auditor is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 87 tokens to every session and 1,250 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-04.

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