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 research-gap-call-verifiergit 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/research-gap-call-verifier)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/research-gap-call-verifier"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/research-gap-call-verifier/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/research-gap-call-verifier"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/research-gap-call-verifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00074 | $0.01382 |
| Opus 5 | $0.00037 | $0.00691 |
| Sonnet 5 | $0.00015 | $0.00276 |
| Haiku 4.5 | $0.00007 | $0.00138 |
Grade A, and why
research-gap-call-verifier 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 12d 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 — 100 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Research Gap Call Verifier
Use this skill after research, not instead of it. It separates facts already supported by cited sources from material gaps that a business can answer, produces an exact no-call preview, and reconciles returned evidence with honest status labels.
The bundled scripts use only the Python standard library. They never contact CALL-E, a phone provider, or the network. A host may execute an approved plan with CALL-E, but approval and execution remain outside this skill.
Compatibility
The no-call plan builder and result reconciler run on Python 3.9 or newer in any Agent Skills-compatible host. Live execution supports the CALL-E Python SDK or another CALL-E integration that preserves the frozen recipient, task, and idempotency key. Read references/calle-handoff.md before implementing that adapter. The bundled path is deliberately runnable without a provider account or credentials.
When To Use
Use this skill when all of these are true:
- the user has a concrete research goal and constraints;
- cited research has identified one or more businesses;
- a small number of material facts remain unresolved, such as current availability, a price range, a policy, or a scheduling window;
- each recipient is a published organizational number in E.164 format; and
- the user can review the exact recipient, purpose, and questions before any call.
Do not use it for marketing, lead generation, surveys, political outreach, emergencies, deceptive pretexts, personal or wireless numbers, high-impact eligibility decisions, or requests for credentials, payment-card data, health details, government identifiers, or other sensitive account information.
Workflow
- Separate evidence from gaps. Keep cited facts in
established_facts. Add only unresolved, decision-relevant questions togaps. Readreferences/input-contract.md. - Apply the safety boundary. Reject prohibited purposes and sensitive questions. Confirm every recipient is a published business line. Read
references/safety.md. - Build the preview. Run
scripts/build_call_plan.py. The output is deterministic, masks numbers for display, binds each recipient and question set to an idempotency key, and always says that no call was placed. - Ask for explicit approval. Show the complete preview: organization, masked recipient, opening disclosure, purpose, questions, and total expected calls. Do not infer approval from the original research request.
- Execute outside this skill. Only an approved host integration may translate the frozen plan into CALL-E calls. Make at most one attempt per call-plan item unless the user separately approves a retry. Do not allow recipient, purpose, or questions to change after approval.
- Reconcile results. Save the provider-neutral result envelope and run
scripts/validate_results.py. A completed call still needs a direct callee quote for a fact to becomeconfirmed_by_phone. Voicemail, refusal, ambiguity, timeout, and provider failure remain unresolved. - Report honestly. Present
sourced,confirmed_by_phone,not_established, andnot_reachedseparately. Never describe an attempted call as a verified answer.
What ships with it
11 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.
- assets/expected-call-plan.json 2.6 KB
- assets/fictional-research.json 1.3 KB
- assets/fictional-results.json 1.1 KB
- references/calle-handoff.md 2.6 KB
- references/examples.md 1.6 KB
- references/input-contract.md 2.4 KB
- references/result-contract.md 1.8 KB
- references/safety.md 2.2 KB
- scripts/build_call_plan.py 11 KB runs code
- scripts/self_test.py 2.9 KB runs code
- scripts/validate_results.py 8.2 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.
- 12d ago First seen · 100 lines · 74 tokens per session scan A 6b26f1c05a42
research-gap-call-verifier is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (88 stars, last pushed yesterday), licensed MIT. It adds 74 tokens to every session and 1,382 once invoked, about $0.0004 per session on Opus 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 2026-08-30.
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