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 learning-recall-callgit 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/learning-recall-call)<a href="https://agentmods.dev/skills/calle-ai/awesome-phone-call-agents/learning-recall-call"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/learning-recall-call/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/learning-recall-call"><img src="https://agentmods.dev/badge/skills/calle-ai/awesome-phone-call-agents/learning-recall-call.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 | $0.00040 | $0.02580 |
| Opus 5 | $0.00020 | $0.01290 |
| Sonnet 5 | $0.00008 | $0.00516 |
| Haiku 4.5 | $0.00004 | $0.00258 |
Grade A, and why
learning-recall-call 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 today.
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 — 467 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Learning Recall Call
Purpose
Use this skill when a learner wants to test what they actually remember from a topic they previously studied.
The goal is not simply to remind the learner to study. The goal is to:
- test active recall
- evaluate conceptual understanding
- identify knowledge gaps
- detect misconceptions
- adapt questions based on the learner's responses
- recommend when the learner should review the topic again
This skill is designed for educational learning workflows.
Inputs
The skill accepts:
topic: The subject or concept the learner previously studied.study_context: Notes, summary, or learning material describing what the learner studied.previous_score: Optional score from a previous recall session.weak_areas: Optional list of concepts the learner previously struggled with.review_number: Number of the current recall session.learner_name: Optional name of the learner.phone_number: Destination phone number supplied by the host application when a call is authorized.
Call Flow
1. Start the call
Introduce yourself clearly.
Example:
"Hi! This is your learning recall check. You studied {{topic}} recently, and I'm here to see what you still remember."
Do not immediately provide the answer.
Keep the introduction short and focused.
2. Ask an open-ended recall question
Start with a question that requires the learner to explain the concept in their own words.
Example:
"Can you explain {{topic}} to me as if you were teaching it to someone who has never learned it?"
Prefer questions that require explanation rather than yes-or-no answers.
3. Analyze the response
Evaluate the learner's answer for:
- correct understanding
- partial understanding
- missing concepts
- contradictions
- confusion between related concepts
- common misconceptions
- unsupported claims
- confidence
Do not interrupt unnecessarily.
Allow the learner enough time to complete their explanation.
4. Ask adaptive follow-up questions
What ships with it
5 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.
- today First seen · 467 lines · 40 tokens per session scan A 4f130a4d32e2
learning-recall-call is a skill published in the GitHub repository CALLE-AI/awesome-phone-call-agents (88 stars, last pushed yesterday), licensed MIT. It adds 40 tokens to every session and 2,580 once invoked, about $0.0002 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 2026-09-12.
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