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 mustergit 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/muster)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/muster"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/muster/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/muster"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/muster.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 | $0.00042 | $0.01461 |
| Opus 5.5 | $0.00017 | $0.00584 |
| Sonnet 5.5 | $0.00008 | $0.00292 |
| Haiku 4.5 | $0.00004 | $0.00146 |
Grade A, and why
muster 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.
How it starts
The opening of the file, as written. The whole thing — 142 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Muster — Directory Ghost-Rate Auditor (CALL-E Agent Skill)
Audits directories before patients and consumers suffer from ghost networks.
Muster automates offline batch phone verification of contact directories that institutions claim are active:
- Insurer in-network provider rolls (primary care, therapists, clinics, specialists)
- Marketplace verified merchants (vendors, retail store fronts, inventory fulfillment)
By placing non-invasive, consent-first qualifying calls via CALL-E, Muster asks ONE benign verification question, extracts structured operational signals, cites verbatim transcript quotes, and computes the verified Ghost Rate (e.g. "7 of 12 audited = GHOST, 58.3%").
Prerequisites & Engine Setup
This skill orchestrates the open-source Muster directory auditor engine. To run audits locally:
# Clone and install the Muster engine
git clone https://github.com/omshukla24/Muster.git
cd Muster
pip install -e .
Note: For lightweight workflow exploration, the skill operates in mock mode by default, allowing zero-cost simulated audits with bundled sample schemas without needing live telephony credits.
When to Use This Skill
Activate Muster when an agent or auditor needs to:
- Audit a directory file (
.csvor.json) to flag defunct, disconnected, or left-network listings. - Calculate headline Ghost Rate and real-world consumer access metrics (e.g. "A patient must call ~2.4 listings to reach 1 real provider").
- Generate cited evidence reports containing verbatim spoken receptionist quotes for compliance or enforcement.
- Invoke via FastMCP tool
audit_directoryfrom any agent workflow.
Inputs & Parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
directory |
File Path | Required | Path to CSV or JSON directory file (must contain entity name and phone number). |
sector |
String | US_INSURER |
Sector archetype: US_INSURER (healthcare in-network) or MARKETPLACE_SELLER (merchants). |
mode |
String | mock |
mock (zero-cost simulated rehearsal) or live (places real phone calls via CALL-E). |
goal |
String | Auto-selected | Custom verification question. If omitted, uses sector default. |
concurrency |
Integer | 2 |
Number of simultaneous verification calls (max recommended: 3). |
What ships with it
3 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.
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.
- 1mo ago First seen · 142 lines · 42 tokens per session scan A c46e8b8150a2
muster is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 42 tokens to every session and 1,461 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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