customer-feedback-callback

customer-feedback-callback is a skill for Claude Code, Codex from CALLE-AI/awesome-phone-call-agents. It costs 62 tokens per session (1,602 once invoked), scanned A, original, MIT.

A post-delivery phone-call workflow that collects structured customer feedback for each completed order or service visit. It turns the conversation into results such as sentiment and missing-item information, then sends recommendations for human approval.

In plain words
What is it for?
It helps check customer satisfaction, identify missing items, investigate problems, and spot feedback trends after delivery.
Why use it?
It replaces inconsistent follow-up notes with repeatable results while preventing an automated system from applying urgent business notifications on its own.

Skill for Claude CodeCodex

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

Good fit It helps check customer satisfaction, identify missing items, investigate problems, and spot feedback trends after delivery.

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Install with agentmods
npx agentmods add skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback
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 customer-feedback-callback
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 customer-feedback-callback

README.md
[![agentmods](https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback/github.svg)](https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback)
Your own site
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback/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.

agentmods 80×15 button for customer-feedback-callback

Your own site · 80×15
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/customer-feedback-callback.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,602 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 $0.00062 $0.01602
Opus 5.5 $0.00025 $0.00641
Sonnet 5.5 $0.00012 $0.00320
Haiku 4.5 $0.00006 $0.00160

Measured 1mo ago against content hash 61997949fe64, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

customer-feedback-callback 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 1mo 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.

skills/customer-feedback-callback/SKILL.md · 84 lines

How it starts

The opening of the file, as written. The whole thing — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Customer Feedback Callback

Use this skill when an agent needs to turn "call the customer after their order and see how it went" into a safe, repeatable workflow: one outbound CALL-E call per order, a structured result instead of a raw transcript, and a downstream decision step that a human still approves.

This is a post-delivery feedback callback, not a reminder, a sales call, or a collections call. It assumes the order/service already completed and the business wants a short, low-pressure check-in call.

Setup

  • A configured CALL-E client (API key) with permission to create calls and read call results.
  • A publicly reachable webhook URL the host registers with CALL-E for call-result delivery; without one, poll calls.get(id) instead of relying on handle_webhook.
  • Nothing to install for the skill itself — references/examples.md shows the exact calls.create(...) call shape and a fake-client dry-run path with no live dependency.

When To Use

Use this skill for:

  • one feedback call per completed order or service visit, dispatched automatically (for example 30 minutes after delivery)
  • extracting a structured result (satisfaction score, sentiment, missing items, free-text summary) from the call instead of hand-parsing a transcript downstream
  • distinguishing "customer did not pick up" from "customer answered and had nothing wrong" so only real signal reaches a human
  • feeding call results into a small agent pipeline that cross-checks a new complaint against order history and recent trend before flagging it as urgent
  • keeping the final "notify the business now" decision as a recommendation, never an automatic action

When Not To Use

Do not use this skill to:

  • place the call before the order/service is actually complete
  • retry a call that already reached the customer (completed, voicemail) — only a verified, terminal no_answer is retry-eligible, once; never retry on failed or any ambiguous/unresolved outcome — hold those for reconciliation instead (see Core Workflow)
  • call about anything other than the specific completed order named in the task (no upsell, no collections, no marketing)
  • auto-apply a discount, refund, or other compensation based on the call result — that always stays a separate, explicitly human-approved action
  • store or forward the raw transcript to a human channel; only the structured result and a short generated summary should leave this workflow

Read the full file on GitHub · 84 lines

Files

What ships with it

2 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.

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. 1mo ago First seen · 84 lines · 62 tokens per session scan A 61997949fe64

Subscribe to this mod's changes

customer-feedback-callback is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 62 tokens to every session and 1,602 once invoked, about $0.0002 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 2026-09-16.

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