Hermes Agent Skill Authoring
NousResearch/hermes-agent
Author in-repo SKILL.md files: frontmatter and structure. An agent skill from NousResearch/hermes-agent.
Conducts an authorized phone-based active recall session with a learner, asks adaptive questions about a previously studied topic, identifies knowledge gaps and misconceptions, and returns a…
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents learning-recall-call --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/learning-recall-call .claude/skills/learning-recall-call && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "learning-recall-call" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-call into .claude/skills/learning-recall-call/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "learning-recall-call", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-callType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents learning-recall-call --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/learning-recall-call .agents/skills/learning-recall-call && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "learning-recall-call" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-call into .agents/skills/learning-recall-call/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "learning-recall-call", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents learning-recall-call --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/learning-recall-call .cursor/skills/learning-recall-call && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "learning-recall-call" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-call into .cursor/skills/learning-recall-call/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "learning-recall-call", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/CALLE-AI/awesome-phone-call-agents.git --path skills/learning-recall-call--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents learning-recall-call --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/learning-recall-call .gemini/skills/learning-recall-call && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "learning-recall-call" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-call into .gemini/skills/learning-recall-call/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "learning-recall-call", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install CALLE-AI/awesome-phone-call-agents learning-recall-callInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/learning-recall-call .github/skills/learning-recall-call && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "learning-recall-call" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-call into .github/skills/learning-recall-call/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "learning-recall-call", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install CALLE-AI/awesome-phone-call-agents learning-recall-call --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/CALLE-AI/awesome-phone-call-agents.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/learning-recall-call .opencode/skills/learning-recall-call && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "learning-recall-call" agent skill from https://github.com/CALLE-AI/awesome-phone-call-agents/tree/main/skills/learning-recall-call into .opencode/skills/learning-recall-call/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "learning-recall-call", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
learning-recall-callConducts an authorized phone-based active recall session with a learner, asks adaptive questions about a previously studied topic, identifies knowledge gaps and misconceptions, and returns a…
Learning Recall Call is an agent skill from CALLE-AI/awesome-phone-call-agents. Conducts an authorized phone-based active recall session with a learner, asks adaptive questions about a previously studied topic, identifies knowledge gaps and misconceptions, and returns a structured learning assessment.
Its SKILL.md is about 3.2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `references/examples.md`, `references/safety.md` and `scripts/evaluator.py`).
The repository describes itself as: Portable phone-call Agent Skills, apps, examples, adapters, and scheduler recipes for AI agents. The licence is MIT.
8 steps, taken from the step headings in SKILL.md.
Read from SKILL.md and the folder at commit 38d4118. It shows what the files ask for, not the result of running them.
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Ships 3 files in scripts/ (Python), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Learning Recall Call loads about 3.2k tokens when it runs, and up to ~5.3k if it reads all its reference files. Until then it costs about 61 tokens; SKILL.md has 1,549 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
The full file from CALLE-AI/awesome-phone-call-agents at commit 38d4118, republished under its MIT licence (© CALLE-AI). 1,549 words, ~3,249 tokens.
.claude/skills/learning-recall-call/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.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:
This skill is designed for educational learning workflows.
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.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.
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.
Evaluate the learner's answer for:
Do not interrupt unnecessarily.
Allow the learner enough time to complete their explanation.
Use the learner's previous answer to determine the next question.
If the answer is strong:
If the answer is weak:
If a possible misconception appears:
Record a misconception when the learner expresses a confidently incorrect or conceptually incorrect belief.
Do not label an answer as a misconception simply because the learner says they are unsure or cannot remember.
For each detected misconception, capture:
conceptstudent_beliefcorrect_understandingevidence_from_responseExample:
Student belief:
"Hashing encrypts data so that we can decrypt it later."
Correct understanding:
"Hashing is generally a one-way transformation, while encryption is designed to allow data to be recovered using the appropriate key."
Evidence:
"The learner described hashing as reversible encryption."
A misconception should be based on evidence from the learner's response.
Do not invent misconceptions that were not expressed or reasonably supported by the conversation.
After the conversation, produce a recall assessment.
Evaluate:
Generate a score from 0–100.
Suggested interpretation:
90–100: Strong recall75–89: Good recall50–74: Partial recall0–49: Weak recallThe score is an educational assessment and should not be presented as a scientifically exact measurement of memory.
Use the current performance and detected misconceptions to recommend the next review interval.
General guidance:
Example intervals may include:
1 day3 days7 days14 days30 daysThese intervals are adaptive recommendations, not guaranteed optimal memory schedules.
Give the learner a short summary.
Example:
"You remembered the main idea well, but you were unsure about key generation. I'll recommend reviewing that part again soon."
Keep the final explanation concise.
Do not overwhelm the learner with a long lecture during the call.
The study_context is the primary source of truth for the learner's studied material.
When evaluating a response:
If the study context is insufficient to determine whether an answer is correct, mark the result as uncertain rather than inventing an evaluation.
If previous_score or weak_areas are available, use them to personalize the session.
For example:
Do not assume that a previous weakness is still present without testing it.
Return a structured assessment containing:
{
"topic": "",
"recall_score": 0,
"status": "strong|good|partial|weak|misconception_detected",
"concepts_remembered": [],
"weak_areas": [],
"misconceptions": [
{
"concept": "",
"student_belief": "",
"correct_understanding": "",
"evidence_from_response": ""
}
],
"confidence": "high|medium|low",
"summary": "",
"recommended_next_review": ""
}The output should contain valid JSON when structured output is requested by the host application.
Only place an outbound learning-recall call when the learner has explicitly requested or authorized the call.
Do not initiate an unexpected call.
Before scheduling a recurring recall routine, clearly tell the learner:
Do not assume that permission for one call automatically authorizes recurring calls.
Phone numbers must be provided in E.164 format.
Example of a fictional valid format:
+15550100123
Do not accept or normalize ambiguous phone numbers silently.
Never expose a learner's full phone number in logs, summaries, examples, or generated reports.
Mask phone numbers when displaying them.
Example:
+15550100123 → +155*****0123
Never request, store, or expose:
Credentials must be supplied through the host application's secure configuration.
Never place credentials inside SKILL.md, examples, logs, or generated assessments.
Do not create recurring calls unless the learner explicitly requests them.
Before creating a recurring schedule:
Do not create duplicate schedules for the same learner, topic, and time window.
The learner must be able to cancel a scheduled recall call.
Cancellation should:
Implementations should provide a dry-run or preview mode whenever possible.
In dry-run mode:
Dry-run mode should make it obvious that no real call will occur.
An outbound phone call is an external side effect.
The implementation must make the call action visible to the learner and should provide enough information to understand:
Do not silently place calls or silently create recurring schedules.
This skill is intended for educational recall.
It must not present itself as a substitute for:
If a learner introduces a sensitive or emergency situation, the call should not attempt to provide professional or emergency intervention.
If the learner appears to require emergency assistance, encourage them to contact the appropriate local emergency service or qualified professional rather than attempting to handle the situation through this educational skill.
If required learning information is missing:
If the learner cannot answer a question:
If the phone call cannot be completed:
Input:
topic: Hashing vs Encryption
study_context:
Hashing is generally a one-way transformation used to produce a fixed-length
digest. Encryption is designed to allow data to be recovered using the
appropriate key.
previous_score: 70
weak_areas:
- Hashing vs encryption
review_number: 2Learner response:
"Hashing encrypts the password so that we can decrypt it later."
Assessment:
{
"topic": "Hashing vs Encryption",
"recall_score": 55,
"status": "misconception_detected",
"concepts_remembered": [
"Hashing can be used in password-related systems"
],
"weak_areas": [
"Hashing vs encryption"
],
"misconceptions": [
{
"concept": "Hashing vs encryption",
"student_belief": "Hashing is reversible encryption.",
"correct_understanding": "Hashing is generally one-way, while encryption is designed to allow data to be recovered using the appropriate key.",
"evidence_from_response": "The learner described hashing as something that can be decrypted later."
}
],
"confidence": "medium",
"summary": "The learner understands that hashing is relevant to password protection but is confusing hashing with reversible encryption.",
"recommended_next_review": "1 day"
}This skill defines the learning and conversation behavior.
The host application is responsible for:
The skill itself must not assume a specific phone-call provider.
Provider-specific authentication and call execution should remain in the host application.
© CALLE-AI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 5 other files (scripts, references) in skills/learning-recall-call of CALLE-AI/awesome-phone-call-agents.
Open the folder on GitHubat commit 38d4118
Learning Recall Call next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Learning Recall Call this skillCALLE-AI/awesome-phone-call-agents | 107 | — | ~3.2k | Automated safety check: Pass | MIT | |
| Hermes Agent Skill AuthoringNousResearch/hermes-agent | 252k | — | ~3.6k | Automated safety check: Pass | MIT | |
| Configuring Oauth2 Authorization Flowmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~1.7k | Automated safety check: Pass | Apache-2.0 | |
| Authoring Skillsvercel/next.js | 143k | — | ~1k | Automated safety check: Pass | MIT | |
| PhoneBlockRunAI/ClawRouter | 6.6k | — | ~2.4k | Automated safety check: Pass | MIT | |
| Containing Active Breachmukul975/Anthropic-Cybersecurity-Skills | 34k | — | ~2.7k | Automated safety check: Pass | Apache-2.0 |
NousResearch/hermes-agent
Author in-repo SKILL.md files: frontmatter and structure. An agent skill from NousResearch/hermes-agent.
mukul975/Anthropic-Cybersecurity-Skills
Configures secure OAuth 2.0 authorization flows, including Authorization Code with PKCE, Client Credentials, and Device Authorization Grant, covering flow selection, PKCE implementation, token…
vercel/next.js
How to create and maintain agent skills in .agents/skills/. An agent skill from vercel/next.js.
BlockRunAI/ClawRouter
Verify phone numbers (carrier + SIM-swap fraud signals) and place AI-powered outbound voice calls via BlockRun's gateway (Twilio + Bland.ai).
mukul975/Anthropic-Cybersecurity-Skills
Executes containment strategies to stop active adversary operations and prevent lateral movement during a confirmed security breach.
abpframework/abp
ABP permission system - PermissionDefinitionProvider, [Authorize] attribute, CheckPolicyAsync, IsGrantedAsync, ICurrentUser, IPermissionManager, multi-tenancy side.
CALLE-AI/awesome-phone-call-agents
Demonstrates advisory accessibility-planning checks with offline fixtures and a proposed bounded CALL-E workflow; use for exploring unknown or qualified venue claims without making calls.
CALLE-AI/awesome-phone-call-agents
A skill your agent uses when an agent holds some evidence for a physical-world claim but the evidence is broader, narrower, or older than the exact question asked, and it must first decide whether a…
CALLE-AI/awesome-phone-call-agents
Call a venue and ask the accessibility questions that matter to one specific person — step-free entry, hearing loop, guide dogs, quiet hours, changing places — then return a per-need verdict backed…
CALLE-AI/awesome-phone-call-agents
Turns a pre-written, building-level location config into a CALL-E outbound phone-call task that guides a delivery driver through the last few hundred metres to a specific building using landmarks…
CALLE-AI/awesome-phone-call-agents
Turn cited business research into a bounded, approval-gated phone-call plan that asks only unresolved factual questions, then reconcile CALL-E-compatible results without treating voicemail, refusal…
CALLE-AI/awesome-phone-call-agents
Place a goal-driven CALL-E call that collects specific structured answers, score those answers against a deterministic rubric you supply, and conditionally trigger a follow-up action — all runnable…
Conducts an authorized phone-based active recall session with a learner, asks adaptive questions about a previously studied topic, identifies knowledge gaps and misconceptions, and returns a…. Learning Recall Call is an agent skill from CALLE-AI/awesome-phone-call-agents. Conducts an authorized phone-based active recall session with a learner, asks adaptive questions about a previously studied topic, identifies knowledge gaps and misconceptions, and returns a structured learning assessment.
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a claude-code`. Or copy the skill folder (skills/learning-recall-call in CALLE-AI/awesome-phone-call-agents) into .claude/skills/learning-recall-call in your project. Claude Code loads it when a task matches its description.
Run `npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a codex`. Or copy the skill folder (skills/learning-recall-call in CALLE-AI/awesome-phone-call-agents) into .agents/skills/learning-recall-call in your project. Codex loads it when a task matches its description.
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add CALLE-AI/awesome-phone-call-agents --skill learning-recall-call -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/learning-recall-call, .gemini/skills/learning-recall-call, .github/skills/learning-recall-call and .opencode/skills/learning-recall-call in your project.
Going by SKILL.md and its folder, Learning Recall Call needs Python for the scripts in its folder. Our summary lists: Python 3.
SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
Learning Recall Call is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 3.2k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.
Skills that share tags, products or a category with Learning Recall Call: Hermes Agent Skill Authoring (NousResearch/hermes-agent, 252k stars), Configuring Oauth2 Authorization Flow (mukul975/Anthropic-Cybersecurity-Skills, 34k stars), Authoring Skills (vercel/next.js, 143k stars) and Phone (BlockRunAI/ClawRouter, 6.6k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
CALLE-AI (a GitHub organization) maintains it in CALLE-AI/awesome-phone-call-agents, which has 107 GitHub stars. The repository holds 101 skills in this directory. The repository was last updated on October 10, 2026.
Source: CALLE-AI/awesome-phone-call-agents on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.