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-right-party-gatekeepergit 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-right-party-gatekeeper)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/call-right-party-gatekeeper"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-right-party-gatekeeper/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-right-party-gatekeeper"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/call-right-party-gatekeeper.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 | $0.00058 | $0.00864 |
| Opus 5.5 | $0.00023 | $0.00346 |
| Sonnet 5.5 | $0.00012 | $0.00173 |
| Haiku 4.5 | $0.00006 | $0.00086 |
Grade A, and why
call-right-party-gatekeeper 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 29d 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 — 92 lines — stays where its author put it; the contents beside it link to each section on GitHub.
call-right-party-gatekeeper
Verify who is listening before saying what matters.
In collections, healthcare, and any sensitive outreach, disclosing account or medical details to the wrong listener is a compliance failure even when the call otherwise goes well. This skill audits finished CALL-E calls for the ordering question: did the agent confirm the recipient before the first sensitive disclosure keyword? This is an advisory English phrase check, not identity verification or legal-compliance certification.
When To Use
- after an authorized CALL-E call, to flag recognized confirmation/disclosure ordering for human review, not to prove identity or permission to disclose
- to detect wrong-party answers ("wrong number", "can I take a message") and third-party answers ("who is this calling?", "he is busy right now")
- to generate a verification-first goal for the next
plan_callthat parks politely and reveals nothing until identity is confirmed
When Not To Use
- to detect fraud or scam patterns in the conversation; use
call-fraud-shield - to audit general call quality, disclosure of being an AI agent, or
result-vs-transcript consistency; use
call-review - during a call; CALL-E exposes transcripts, not live audio, so this is a post-call audit plus a pre-call goal template
- as proof of who actually answered; regex-level signals miss many phrasings and the card says so
Workflow
Analyze a finished call
python3 scripts/gatekeeper.py analyze --transcript path/to/call-result.json
Reads the real get_call_run result shape ({status, result: {transcript}})
or the flat shape used by sibling skill fixtures. Emits a card:
right_party_status: CONFIRMED / WRONG_PARTY / THIRD_PARTY_PRESENT / UNVERIFIEDverification_before_disclosure: ordering flag (false when disclosure preceded the first recognized callee confirmation or no confirmation was recognized; only detected phrases are compared)evidence: turn index, masked span, signal type for every detected verification question, identity confirmation, wrong-party signal, third-party signal, and sensitive disclosuregate_assessment: "unclear"with a reason when one side never spokerecommended_action:proceed,stop_and_retry_with_script(with the verification-first goal text), orhuman_review(with review guidance)
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.
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.
- 29d ago First seen · 92 lines · 58 tokens per session scan A d7c9d4f1ca90
call-right-party-gatekeeper is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (109 stars, last pushed yesterday), licensed MIT. It adds 58 tokens to every session and 864 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 2026-09-21.
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