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.
npx skills add CALLE-AI/awesome-phone-call-agents --skill call-apology-effectiveness-auditorgit clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agentsWrote 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.
[](https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-apology-effectiveness-auditor)<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>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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 sentenceapology_detected/apology_evidence: whether any agent turn from the grievance onward contains an apology, and the masked sentencecomponents_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 > explanatoryrote)
verdict:EFFECTIVE_APOLOGY(acknowledgment plus repair or explanation) /PARTIAL_APOLOGY/NON_APOLOGY(deflection override) /GRIEVANCE_UNADDRESSED/NO_GRIEVANCE, plusunclearpaths (empty transcript, fewer than two turns)
What ships with it
9 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- references/example-goal.txt 327 B
- references/example-transcript-non-apology.json 468 B
- references/example-transcript-rote.json 607 B
- references/example-transcript-unaddressed.json 539 B
- references/example-transcript.json 753 B
- references/examples.md 5.8 KB
- references/safety.md 1.7 KB
- scripts/apology_effectiveness_auditor.py 14 KB runs code
- scripts/test_apology_effectiveness_auditor.py 20 KB runs code
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.
- 16d ago First seen · 107 lines · 87 tokens per session scan A ad8844d53cd4
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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