aerospace-expert

aerospace-expert is a skill for Claude Code from personamanagmentlayer/pcl. It costs 61 tokens per session (3,089 once invoked), scanned A, original, Apache-2.0.

An expert reference for aerospace systems and aviation software, including flight management, maintenance, safety, air traffic control, and aircraft monitoring.

In plain words
What is it for?
Use it when working on flight systems, maintenance tracking, aircraft health data, flight operations, avionics, or related aerospace standards and regulations.
Why use it?
It provides domain context for software and system decisions where aviation operations, safety, and industry standards matter.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Good fit Use it when working on flight systems, maintenance tracking, aircraft health data, flight operations, avionics, or related aerospace standards and regulations.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/personamanagmentlayer/pcl/aerospace-expert
Install

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.

Any agent
npx skills add personamanagmentlayer/pcl --skill aerospace-expert
Clone the repo
git clone --depth 1 https://github.com/personamanagmentlayer/pcl

Made for: Claude Code.

Wrote 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.

agentmods badge for aerospace-expert

README.md
[![agentmods](https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/aerospace-expert/github.svg)](https://agentmods.dev/skills/personamanagmentlayer/pcl/aerospace-expert)
Your own site
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/aerospace-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/aerospace-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.

agentmods 80×15 button for aerospace-expert

Your own site · 80×15
<a href="https://agentmods.dev/skills/personamanagmentlayer/pcl/aerospace-expert"><img src="https://agentmods.dev/badge/skills/personamanagmentlayer/pcl/aerospace-expert.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 61 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,089 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00061 $0.03089
Opus 5 $0.00030 $0.01545
Sonnet 5 $0.00012 $0.00618
Haiku 4.5 $0.00006 $0.00309

Measured 5d ago against content hash 7b71ef7f1455, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

aerospace-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.

stdlib/domains/aerospace-expert/SKILL.md · 423 lines

How it starts

The opening of the file, as written. The whole thing — 423 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Aerospace Expert

Expert guidance for aerospace systems, flight management, maintenance tracking, aviation safety, air traffic control systems, and aerospace software development.

Core Concepts

Aerospace Systems

  • Flight Management Systems (FMS)
  • Maintenance, Repair, and Overhaul (MRO)
  • Air Traffic Control (ATC) systems
  • Aircraft Health Monitoring
  • Flight Operations Quality Assurance (FOQA)
  • Crew resource management
  • Ground handling systems

Aviation Technologies

  • Avionics systems
  • ACARS (Aircraft Communications Addressing and Reporting System)
  • ADS-B (Automatic Dependent Surveillance-Broadcast)
  • Flight data recorders (black boxes)
  • Weather radar systems
  • Autopilot and fly-by-wire
  • Satellite communications

Standards and Regulations

  • FAA regulations (Federal Aviation Administration)
  • EASA standards (European Union Aviation Safety Agency)
  • ICAO standards (International Civil Aviation Organization)
  • DO-178C (software airworthiness)
  • DO-254 (hardware airworthiness)
  • SPEC-42 (maintenance tracking)
  • ATA chapters (maintenance organization)

Aircraft Maintenance System

from enum import Enum

class MaintenanceType(Enum):
    A_CHECK = "a_check"  # Every 400-600 flight hours
    B_CHECK = "b_check"  # Every 6-8 months
    C_CHECK = "c_check"  # Every 18-24 months
    D_CHECK = "d_check"  # Every 6-10 years
    LINE_MAINTENANCE = "line_maintenance"
    UNSCHEDULED = "unscheduled"

@dataclass
class Aircraft:
    """Aircraft information"""
    aircraft_id: str
    registration: str
    aircraft_type: str
    manufacturer: str
    model: str
    serial_number: str
    manufacture_date: datetime
    total_flight_hours: float
    total_cycles: int  # Takeoff/landing cycles
    last_a_check: datetime
    last_c_check: datetime
    airworthiness_certificate: str
    next_major_inspection: datetime

@dataclass
class MaintenanceRecord:
    """Maintenance work record"""
    record_id: str
    aircraft_id: str
    maintenance_type: MaintenanceType
    work_performed: str
    components_replaced: List[str]
    performed_by: str
    performed_at: datetime
    flight_hours_at_maintenance: float
    cycles_at_maintenance: int
    next_due_hours: Optional[float]
    next_due_date: Optional[datetime]

class AircraftMaintenanceSystem:
    """MRO (Maintenance, Repair, Overhaul) system"""

    def __init__(self):
        self.aircraft = {}
        self.maintenance_records = []
        self.component_tracking = {}

    def check_maintenance_due(self, aircraft_id: str) -> dict:
        """Check if maintenance is due for aircraft"""
        aircraft = self.aircraft.get(aircraft_id)
        if not aircraft:
            return {'error': 'Aircraft not found'}

        due_items = []

        # Check A-check (every 500 hours)
        hours_since_a_check = aircraft.total_flight_hours - self._get_last_check_hours(
            aircraft_id, MaintenanceType.A_CHECK
        )

        if hours_since_a_check >= 500:
            due_items.append({
                'type': 'A-check',
                'urgency': 'high' if hours_since_a_check >= 550 else 'medium',
                'hours_overdue': max(0, hours_since_a_check - 500)
            })

        # Check calendar-based C-check
        days_since_c_check = (datetime.now() - aircraft.last_c_check).days

        if days_since_c_check >= 540:  # 18 months
            due_items.append({
                'type': 'C-check',
                'urgency': 'critical' if days_since_c_check >= 600 else 'high',
                'days_overdue': max(0, days_since_c_check - 540)
            })

        # Check component life limits
        component_items = self._check_component_life_limits(aircraft_id)
        due_items.extend(component_items)

        return {
            'aircraft_id': aircraft_id,
            'registration': aircraft.registration,
            'maintenance_required': len(due_items) > 0,
            'due_items': due_items,
            'airworthy': len([item for item in due_items if item['urgency'] == 'critical']) == 0
        }

    def _get_last_check_hours(self, aircraft_id: str, check_type: MaintenanceType) -> float:
        """Get flight hours at last check"""
        records = [
            r for r in self.maintenance_records
            if r.aircraft_id == aircraft_id and r.maintenance_type == check_type
        ]

        if records:
            latest = max(records, key=lambda r: r.performed_at)
            return latest.flight_hours_at_maintenance

        return 0.0

    def _check_component_life_limits(self, aircraft_id: str) -> List[dict]:
        """Check component life limits"""
        due_items = []

        components = self.component_tracking.get(aircraft_id, {})

        for component_name, component_data in components.items():
            if component_data['life_limit_hours']:
                hours_used = component_data['hours_since_new']
                life_limit = component_data['life_limit_hours']

                if hours_used >= life_limit * 0.9:  # Within 90% of life limit
                    due_items.append({
                        'type': 'component_replacement',
                        'component': component_name,
                        'urgency': 'critical' if hours_used >= life_limit else 'high',
                        'hours_remaining': max(0, life_limit - hours_used)
                    })

        return due_items

    def record_maintenance(self,
                          aircraft_id: str,
                          maintenance_data: dict) -> MaintenanceRecord:
        """Record completed maintenance"""
        aircraft = self.aircraft.get(aircraft_id)
        if not aircraft:
            raise ValueError("Aircraft not found")

        record = MaintenanceRecord(
            record_id=self._generate_record_id(),
            aircraft_id=aircraft_id,
            maintenance_type=MaintenanceType(maintenance_data['type']),
            work_performed=maintenance_data['work_performed'],
            components_replaced=maintenance_data.get('components_replaced', []),
            performed_by=maintenance_data['technician_id'],
            performed_at=datetime.now(),
            flight_hours_at_maintenance=aircraft.total_flight_hours,
            cycles_at_maintenance=aircraft.total_cycles,
            next_due_hours=maintenance_data.get('next_due_hours'),
            next_due_date=maintenance_data.get('next_due_date')
        )

        self.maintenance_records.append(record)

        # Update aircraft maintenance dates
        if record.maintenance_type == MaintenanceType.A_CHECK:
            aircraft.last_a_check = datetime.now()
        elif record.maintenance_type == MaintenanceType.C_CHECK:
            aircraft.last_c_check = datetime.now()

        return record

    def predict_maintenance_cost(self,
                                aircraft_type: str,
                                flight_hours_per_year: float) -> dict:
        """Predict annual maintenance costs"""
        # Base maintenance costs per aircraft type
        base_costs = {
            'B737': {
                'hourly_rate': 800,  # $ per flight hour
                'a_check': 25000,
                'c_check': 500000,
                'd_check': 5000000
            },
            'B777': {
                'hourly_rate': 1500,
                'a_check': 50000,
                'c_check': 1000000,
                'd_check': 10000000
            }
        }

        costs = base_costs.get(aircraft_type, base_costs['B737'])

        # Calculate annual costs
        hourly_maintenance = flight_hours_per_year * costs['hourly_rate']

        # A-checks (assume 2 per year for 1000 hours/year)
        a_checks_per_year = flight_hours_per_year / 500
        a_check_costs = a_checks_per_year * costs['a_check']

        # C-check (amortized over 18 months)
        c_check_annual = costs['c_check'] / 1.5

        # D-check (amortized over 8 years)
        d_check_annual = costs['d_check'] / 8

        total_annual = hourly_maintenance + a_check_costs + c_check_annual + d_check_annual

        return {
            'aircraft_type': aircraft_type,
            'flight_hours_per_year': flight_hours_per_year,
            'maintenance_costs': {
                'hourly_maintenance': hourly_maintenance,
                'a_checks': a_check_costs,
                'c_check_amortized': c_check_annual,
                'd_check_amortized': d_check_annual,
                'total_annual': total_annual
            },
            'cost_per_flight_hour': total_annual / flight_hours_per_year
        }

    def _generate_record_id(self) -> str:
        import uuid
        return f"MX-{uuid.uuid4().hex[:10].upper()}"

Read the full file on GitHub · 423 lines

Files

What ships with it

1 file 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.

Changes

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.

  1. 5d ago Changed · -337 lines · +38 tokens per session 7b71ef7f1455
  2. 11d ago First seen · 760 lines · 23 tokens per session scan A 8162616729d2

Subscribe to this mod's changes

aerospace-expert is a skill published in the GitHub repository personamanagmentlayer/pcl (40 stars, last pushed yesterday), licensed Apache-2.0. It adds 61 tokens to every session and 3,089 once invoked, about $0.0003 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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