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call-agent-commitment-tracker

call-agent-commitment-tracker is a skill for Claude Code, Codex from CALLE-AI/awesome-phone-call-agents. It costs 82 tokens per session (1,294 once invoked), scanned A, original, MIT.

An offline transcript analyser that extracts promises and planned follow-up actions made by a phone agent. It sorts them by whether they have a deadline, lack a deadline, or depend on a condition, then creates a goal for a later verification call.

In plain words
What is it for?
Use it after calls where the agent may have promised an email, callback, referral, or other action. It tracks commitments made by the agent, not promises made by the caller.
Why use it?
It prevents future promises from disappearing after the call and makes them easier to check. It is a tracking aid, not proof that a promise was broken.

Skill for Claude CodeCodex

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

not rated 109repo +2 yesterday A scan 82 tokens original MIT

Good fit Use it after calls where the agent may have promised an email, callback, referral, or other action. It tracks commitments made by the agent, not promises made by the caller.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/calle-ai/awesome-phone-call-agents/call-agent-commitment-tracker
View source ↗ CALLE-AI/awesome-phone-call-agents
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-agent-commitment-tracker
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-agent-commitment-tracker

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

Your own site · 80×15
<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-agent-commitment-tracker"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-agent-commitment-tracker.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 82 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,294 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 $0.00082 $0.01294
Opus 5.5 $0.00033 $0.00518
Sonnet 5.5 $0.00016 $0.00259
Haiku 4.5 $0.00008 $0.00129

Measured 25d ago against content hash b353d8400519, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

call-agent-commitment-tracker 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 25d ago.

The scan reads SKILL.md. This mod also ships 2 executable files (scripts/commitment_tracker.py, scripts/test_commitment_tracker.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-agent-commitment-tracker/SKILL.md · 105 lines

How it starts

The opening of the file, as written. The whole thing — 105 lines — stays where its author put it; the contents beside it link to each section on GitHub.

call-agent-commitment-tracker

An agent that says "I'll send that right away" and doesn't — is worse than one that said nothing.

When an automated agent makes a call, it often commits to future actions: sending a confirmation email, having a specialist call back, filing a referral. Those commitments are audible, remembered by the person, and completely untracked unless this skill reads the transcript.

This is the post-call layer: it reads the finished get_call_run transcript, extracts every agent commitment, classifies it by urgency, and hands you a ready-to-use follow-up call goal to verify fulfillment.

When To Use

  • after any CALL-E call where the agent may have made forward-looking promises
  • as part of a quality-assurance pipeline to prevent "ghost commitments"
  • before a follow-up call to understand what the previous call committed to
  • in collection, healthcare, or support workflows where broken commitments carry legal or reputational risk

When Not To Use

  • to audit the callee's promises (this skill only tracks agent turns)
  • during a call; strictly post-call analysis plus pre-call goal crafting
  • as a definitive legal record; it is heuristic and advisory only
  • to replace CRM or ticketing systems for obligation tracking

Workflow

Audit a finished call

python3 scripts/commitment_tracker.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 skills. Emits a commitment card:

  • commitments[]: each detected commitment with turn_index, classification, and evidence (PII-masked, capped at 200 chars)
  • commitment_count: counts by WITH_DEADLINE, WITHOUT_DEADLINE, CONDITIONAL
  • verdict: COMMITMENTS_FOUND / NONE_FOUND, plus unclear paths (empty transcript, no agent turns)
  • recommended_action: schedule_followup_call with guidance, or no_followup_required
  • disclaimer: heuristic advisory disclaimer on every card

Read the full file on GitHub · 105 lines

Files

What ships with it

6 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. 25d ago First seen · 105 lines · 82 tokens per session scan A b353d8400519

Subscribe to this mod's changes

call-agent-commitment-tracker is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 82 tokens to every session and 1,294 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 2026-09-25.

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