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 personamanagmentlayer/pcl --skill education-expertgit clone --depth 1 https://github.com/personamanagmentlayer/pclWrote 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/personamanagmentlayer/pcl/education-expert)<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/education-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/education-expert/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/personamanagmentlayer/pcl/education-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/education-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 | $0.00045 | $0.02580 |
| Opus 5 | $0.00023 | $0.01290 |
| Sonnet 5 | $0.00009 | $0.00516 |
| Haiku 4.5 | $0.00005 | $0.00258 |
Grade A, and why
education-expert 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 5d 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 — 414 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Education Expert
Expert guidance for education technology, learning management systems, online learning platforms, and educational software development.
Core Concepts
Educational Technology
- Learning Management Systems (LMS)
- Student Information Systems (SIS)
- Assessment and evaluation tools
- Adaptive learning platforms
- Virtual classrooms
- Content management
Standards
- SCORM (Sharable Content Object Reference Model)
- xAPI (Experience API / Tin Can API)
- LTI (Learning Tools Interoperability)
- QTI (Question and Test Interoperability)
- Accessibility (WCAG, Section 508)
Key Features
- Course management
- Grade tracking
- Student analytics
- Content delivery
- Collaborative tools
- Assessment engines
LMS Core Implementation
from dataclasses import dataclass
from typing import List, Optional
from datetime import datetime
from enum import Enum
class EnrollmentStatus(Enum):
ACTIVE = "active"
COMPLETED = "completed"
DROPPED = "dropped"
PENDING = "pending"
@dataclass
class Course:
course_id: str
title: str
description: str
instructor_id: str
start_date: datetime
end_date: datetime
credits: int
capacity: int
syllabus_url: str
prerequisites: List[str]
@dataclass
class Student:
student_id: str
first_name: str
last_name: str
email: str
enrolled_date: datetime
grade_level: str
gpa: float
@dataclass
class Enrollment:
enrollment_id: str
student_id: str
course_id: str
enrollment_date: datetime
status: EnrollmentStatus
final_grade: Optional[float]
class LMSPlatform:
"""Learning Management System core functionality"""
def __init__(self, db):
self.db = db
def enroll_student(self, student_id, course_id):
"""Enroll student in course"""
course = self.db.get_course(course_id)
current_enrollment = self.db.count_enrollments(course_id)
# Check capacity
if current_enrollment >= course.capacity:
raise Exception("Course is full")
# Check prerequisites
if course.prerequisites:
completed = self.get_completed_courses(student_id)
if not all(prereq in completed for prereq in course.prerequisites):
raise Exception("Prerequisites not met")
enrollment = Enrollment(
enrollment_id=generate_id(),
student_id=student_id,
course_id=course_id,
enrollment_date=datetime.now(),
status=EnrollmentStatus.ACTIVE,
final_grade=None
)
return self.db.save_enrollment(enrollment)
def get_student_transcript(self, student_id):
"""Generate student transcript"""
enrollments = self.db.get_student_enrollments(student_id)
transcript = []
for enrollment in enrollments:
if enrollment.status == EnrollmentStatus.COMPLETED:
course = self.db.get_course(enrollment.course_id)
transcript.append({
'course_code': course.course_id,
'course_name': course.title,
'credits': course.credits,
'grade': enrollment.final_grade,
'term': self.get_term(enrollment.enrollment_date)
})
return transcript
def calculate_gpa(self, student_id):
"""Calculate student GPA"""
transcript = self.get_student_transcript(student_id)
total_points = 0
total_credits = 0
for record in transcript:
if record['grade'] is not None:
total_points += record['grade'] * record['credits']
total_credits += record['credits']
return total_points / total_credits if total_credits > 0 else 0.0
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.
- 5d ago First seen · 414 lines · 45 tokens per session scan A 6cc9597434b1
education-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed 3d ago), licensed Apache-2.0. It adds 45 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 2026-09-05.
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