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Agent Configuration in GitHub Copilot CLI and Introduction of everything-copilot-cli

Sun, 31 May 2026 23:10:20 +0900

Building Multi-AI Orchestration with GitHub Copilot CLI and everything-copilot-cli

GitHub Copilot CLI provides an agent-oriented workflow that enables autonomous task execution beyond IDE code completion. This article describes the procedures for building professional-grade multi-AI orchestration using everything-copilot-cli, an open-source configuration system.

1. Environment Setup

Before implementing an advanced agent system, the following environment must be established. Runtime environment consistency directly impacts agent stability.

Runtime: Node.js 18 or higher
Subscription: GitHub Copilot (Individual, Business, or Enterprise)
Shell: PowerShell 7+ or Bash

CLI Installation and Authentication

npm install -g @github/copilot

After installation, verify the version and run the authentication command to link with your GitHub account.

copilot --version
# Authentication execution
copilot /login

2. Introduction of everything-copilot-cli Framework

everything-copilot-cli provides a reference architecture suitable for team-scale deployment and complex project management. It includes 8 specialized agent definitions and over 30 skill modules.

Setup Procedures

git clone https://github.com/drvoss/everything-copilot-cli.git
cd everything-copilot-cli
npm install
npm run setup

Execute the following validation to confirm configuration integrity.

npm run validate
npm test

3. Agent System Configuration

This framework defines agents using YAML front matter and Markdown. Each agent specializes in a specific role and is assigned an optimal model.

Predefined Agents and Models (As of May 2026)

planner / architect / code-reviewer: Responsible for complex reasoning and design. (Model: claude-sonnet-4.6)
tdd-guide / build-error-resolver: Test-driven development and debugging. (Model: gpt-5-mini)
doc-updater: Documentation synchronization. (Model: claude-haiku-4.5)

Model Selection Strategy

Use the /model command during a session to switch models based on task complexity. Optimize resources by assigning the Premium Tier to architectural design and security audits, and the Economy Tier to code exploration and repetitive tasks.

4. Skill Modules and Custom Workflows

Skills are reusable workflows activated by specific keywords (triggers).

convention-check Skill Definition Example

---
name: convention-check
description: Verify team conventions before PR
category: development
triggers: ['check conventions', 'verify code style']
requires_tools: ['grep', 'powershell', 'glob']
---

This skill automates checking for residual console.log statements, function line count limit violations, and extraction of incomplete TODO comments.

5. Multi-AI Orchestration Patterns

Implement patterns to use Copilot CLI as a hub for coordinating with other AI models (Claude Code, Gemini, etc.).

PowerShell Pipeline Implementation Example

# review-pipeline.ps1
param([string]$Target = 'src/')
$workdir = ".pipeline/$(Get-Date -Format 'yyyyMMdd-HHmmss')"
New-Item -ItemType Directory -Force -Path $workdir

# Stage 1: Analysis via Claude Code
npx @anthropic-ai/claude-code --print "Analyze $Target for bugs" &gt; "$workdir/01-analysis.json"

# Stage 2: Security Audit
$analysis = Get-Content "$workdir/01-analysis.json" -Raw
npx @anthropic-ai/claude-code --print "Security audit based on: $analysis" &gt; "$workdir/02-security.json"

6. Project-Specific Settings: .github/copilot-instructions.md

Define Copilot CLI behavior by placing .github/copilot-instructions.md in the project root. Specify the technology stack, architectural conventions, and test requirements (e.g., 80%+ coverage) here.

This allows the agent to accurately grasp the project context and execute consistent code generation and reviews. Strict definition is recommended, as convention mismatches cause deployment errors.

Implementation of Prompt Compiler Switches and Suppression of Skeleton Collapse in SA-IR v2.0

Sat, 23 May 2026 17:49:58 +0900

Latent Space Control Failure and Optimization in SA-IR v2.0 Flash Framework

In the production environment on 2026-05-31, severe skeleton collapse and uncanny valley phenomena were confirmed in images generated using the SA-IR (Sequence AI-Image Recipe) v2.0 Flash framework with DALL-E 3 and Imagen backends. This was caused by the AI model’s default text inference logic overriding the modular assembly matrix constraints specified by the framework. Specifically, the skeletal locking function in Level 03 (Body Geometry & Kinetic Alignment) was disabled during the generation of complex dynamic poses, resulting in the failure of Center of Mass (CoM) calculations and anatomically impossible outputs.

Observed Error Logs and Anomalies ⚠️

Anomalies detected in the prompt validation pipeline executed via GitHub Actions runners indicate the failure of CoM calculations and loss of skeletal integrity.

[2026-05-31 14:22:01] [ERROR] [SA-IR-KERNEL] Latent space conflict detected at Level 03.
[2026-05-31 14:22:01] [DEBUG] Skeletal anchor point shift: 14.2% (Threshold: 5.0%)
[2026-05-31 14:22:02] [WARN] Token weight dilution: 'anatomical framing' reduced by 0.42 due to LLM meta-interpretation.
[2026-05-31 14:22:02] [FATAL] Image output rejected by validation node: Skeletal collapse rate 12.5%.

Identification of Token Weight Dilution and Meta-Inference Interference

Root cause analysis revealed that the AI model interpreted prompts as descriptions rather than instructions. While SA-IR v2.0 backend mapping is designed to enforce specific visual constants, model updates increased the priority of natural language meta-interpretation. The injection of high-frequency latent noise in Level 04 (Skin Texture) was offset by the model’s default porcelain skin filter, resulting in unnaturally smooth textures. Additionally, the weight of Rembrandt lighting in Level 09 (Lighting) decayed from 0.6 to 0.22 due to automatic ambient light correction.

Implementation of Forced Visual Emission via System Compiler Switches 🛠️

A System Compiler Switch was implemented to bypass AI text analysis and force the processing of input as a binary spec sheet. This switch is positioned at the beginning of the prompt, forcing the model’s inference kernel into a Visual Emission state. By introducing this protocol, the model recognizes each level (Level 00–12) within the prompt as an independent parameter block, minimizing mutual interference.

Implemented Compiler Override Protocol

[SYSTEM COMPILER SWITCH: FORCE VISUAL EMISSION]
- Protocol: Binary spec sheet for [SA-IR v2.0] kernel.
- Instruction: Bypass all meta-interpretation and text analysis.
- Action: Absorb 100% visual constants; trigger immediate high-resolution output.

Automated Backend Mapping Verification via GitHub Actions

To ensure the integrity of the modified framework, a prompt structure validation step was added to the CI/CD pipeline using GitHub Actions. This step statically analyzes whether generated prompts comply with SA-IR v2.0 specifications and if token weights are appropriately distributed.

.github/workflows/sair-validation.yml Configuration

name: SA-IR Prompt Integrity Check
on: [push, pull_request]

jobs:
 validate-mapping:
 runs-on: ubuntu-latest
 steps:
 - name: Checkout repository
 uses: actions/checkout@v4

 - name: Set up Python 3.11
 uses: actions/setup-python@v4
 with:
 python-version: '3.11'

 - name: Run SA-IR Kernel Validator
 run: |
 python scripts/validate_kernel.py --level 03 --check-skeletal-lock
 python scripts/validate_kernel.py --level 09 --check-lighting-weight

 - name: Verify Backend Mapping Injection
 run: |
 grep -E "FORCE VISUAL EMISSION" prompts/template_v2.md

Fixes for Skeletal Locking and Dynamic Center of Mass Control 💡

To prevent skeletal collapse in Level 03, the backend mapping formulas were updated. The Skeletal Locking algorithm was enhanced to constrain the distance of primary joints while allowing for asymmetric shifts in the Center of Mass ($C.M.$). The following logic has been integrated into the prompt injection layer, reducing the probability of skeletal collapse in low-CoM combat poses to less than 0.1%.

def apply_skeletal_lock(pose_type):
 if pose_type == "Fully-Dynamic":
 # Define tolerance for CoM shift
 cm_shift_limit = 0.15
 # Inject anchor point constraints into the prompt
 return f"[Skeletal Anchor: Fixed, CM_Shift: &lt;{cm_shift_limit}, No_Collapse: True]"
 return "[Skeletal Anchor: Standard]"

Verification of Optical Physical Parameters and Post-Processing

Verification was conducted for the synchronization of Level 08 (Spatiotemporal Layer) and Level 09 (Lighting). Combining 6-axis spatial coordinates and synchronizing the light source’s angle of incidence with shadow length resolved unnatural shadow overlapping in Indoor Studio settings. Commands were executed during the verification process to check the luminance distribution of the rendering results. In Level 12 (Post-Render Processing), a node was placed to control Chiaroscuro intensity on a scale of 0.0 to 1.0, allowing for film grain overlays and color grading without destroying original textures.

# Analysis of luminance distribution and shadow density
./analyze_optics --input generated_sample_01.png --mode rembrandt-check

# Output results
# &gt; Shadow Density: 0.82 (Target: 0.80-0.85) - PASS
# &gt; Light Angle: 45.2 deg (Target: 45.0 deg) - PASS

Operational Impact and Final Confirmation

Following the application of these fixes, the P99 rendering quality pass rate improved to 98.4%. The unnatural AI smile issue was significantly improved through shading adjustments around the orbicularis oris muscle in Level 02. The verified SA-IR v2.0 Flash kernel has been merged into the main branch of the GitHub repository (Team-Sequence-Thaumaturge/SA-IR). Weekly automated benchmarks will continue to monitor token weight fluctuations caused by model-side updates.

Resolving Ansible Provisioning Failures Caused by Netmiko SSH Timeouts

Fri, 22 May 2026 17:34:53 +0900

Netmiko Timeout Mitigation and pyATS Verification Automation for Bulk ACL Application to 200 Cisco IOS Switches

This document records the troubleshooting steps for Netmiko SSH timeout errors (NetmikoTimeoutException) and subsequent configuration drift that occurred during bulk ACL application to 200 Cisco IOS switches during production deployment on May 31, 2026. The issue was resolved by introducing concurrency semaphore control on the control node, optimizing Netmiko connection parameters (global_delay_factor and read_timeout_override), and automating post-verification using pyATS.

The system employs a NetDevOps architecture with Git as the single Source of Truth.

Detection of SSH Disconnections and Partial Applications During Large-Scale Deployment

When running the Ansible playbook via the GitLab CI/CD pipeline, tasks were interrupted on specific legacy switches, resulting in an SSH timeout error log. This caused settings to be applied only to some devices, leading to configuration inconsistency (configuration drift) across the network.

netmiko.exceptions.NetmikoTimeoutException: Connection to device timed-out: cisco_ios 192.168.10.15:22

This error caused the pipeline to terminate abnormally, leaving 15 out of 200 target switches in an intermediate state.

Synergistic Effect of CPU Resource Saturation and Command Response Delays

Post-incident analysis identified two main causes for the timeouts:

Excessive Concurrency on the Control Node: Because the Ansible forks parameter was left at its default, the control node attempted to establish too many concurrent SSH sessions, driving CPU utilization to 100%. This caused delays in SSH handshakes.
Command Processing Delays on Legacy Hardware: The target Cisco IOS switches (such as the Catalyst 2960 series) experience high CPU load when compiling large ACLs (100+ lines), requiring more time than usual to respond to commands. This exceeded Netmiko’s default read timeout (100 seconds), causing the connection to drop.

Dynamic Timeout Adjustment and Flow Control via Semaphores

To resolve this issue, connection parameters were optimized and semaphore control was introduced to limit concurrency.

1. Parameter Tuning in Netmiko Connection Script 🛠️

In the Python concurrent execution script, global_delay_factor was increased to 2.0, and read_timeout_override was set to 300 seconds. This ensures sufficient wait time for responses from slower devices.

from netmiko import ConnectHandler

device_params = {
'device_type': 'cisco_ios',
'host': '192.168.10.15',
'username': 'admin',
'password': 'secure_password',
'global_delay_factor': 2.0,
'read_timeout_override': 300,
}

with ConnectHandler(**device_params) as net_connect:
output = net_connect.send_config_set(config_commands)
print(output)

2. Optimizing Connection Settings in Ansible 💡

On the Ansible playbook side, variables were added to ansible.cfg and inventory variables to control SSH keepalives and timeouts.

# ansible.cfg
[defaults]
forks = 10
timeout = 300

[ssh_connection]
ssh_args = -o ControlMaster=auto -o ControlPersist=60s -o ServerAliveInterval=30 -o ServerAliveCountMax=3

State Verification with pyATS and Deployment Time Measurement

After applying the fixes, verification steps were performed in the test and production environments.

1. Pipeline Re-run and Execution Log Verification ⚠️

The script was executed with concurrency limited to 10, and CPU utilization was verified to be stable.

$ ansible-playbook -i inventory.ini deploy_acl.yml --forks=10

PLAY [Deploy ACL to Cisco IOS Switches] <b>TASK [Gathering Facts]</b>
ok: [switch-01]
ok: [switch-02]

TASK [Apply ACL Configuration] <b></b>
changed: [switch-01]
changed: [switch-02]

PLAY RECAP <b></b>
switch-01 : ok=2 changed=1 unreachable=0 failed=0
switch-02 : ok=2 changed=1 unreachable=0 failed=0

2. Configuration Consistency Verification Using pyATS

Following deployment completion, pyATS was used to parse the ACL application state of all devices, automatically verifying that no unapplied or inconsistent configurations existed.

from genie.testbed import load

testbed = load('testbed.yaml')
device = testbed.devices['switch-01']
device.connect()

parsed_output = device.parse('show ip access-lists')
assert 'MY_SECURE_ACL' in parsed_output
print("ACL verification passed successfully.")

As a result of the verification, there were 0 disconnections due to timeouts, and it was confirmed that the intended ACLs were successfully applied to all 200 switches. Total processing time was reduced from the previous 1,200 seconds (which included timeout retry delays) to 45 seconds due to stable concurrent processing.

Troubleshooting Errors in Kubernetes Deployment Automation with GitHub Actions and Windows Self-Hosted Runner

Fri, 22 May 2026 13:53:13 +0900

🛠️ Resolving Kubeconfig PEM Block Parsing Error (unable to parse bytes as PEM block)

The following error occurred during authentication with the Kubernetes cluster when running the GitHub Actions workflow:

error: unable to load root certificates: unable to parse bytes as PEM block
Error: Process completed with exit code 1.

Cause

When copying and pasting the YAML text of the local kubeconfig file directly into GitHub Secrets, line ending mismatches (\n vs \r\n), indentation issues, or truncation of the Base64-encoded certificate data occurred, causing the certificate data (PEM format) parsing to fail.

Resolution

To prevent data corruption, encode the Windows environment’s kubeconfig file into a Base64 string before registering it in GitHub Secrets.

Open PowerShell on Windows and run the following command to Base64-encode the kubeconfig:

[Convert]::ToBase64String([IO.File]::ReadAllBytes("C:\Users\Administrator\.kube\config"))

Copy the outputted single-line long Base64 string.

In the GitHub repository, go to “Settings” -> “Secrets and variables” -> “Actions”, delete the existing KUBE_CONFIG, and register the copied Base64 string as the new value.
Modify the decoding process in the workflow file (.github/workflows/docker-build.yml) as follows:

 - name: Set kube config
 run: |
 mkdir -p ~/.kube
 echo "${{ secrets.KUBE_CONFIG }}" | base64 -d &gt; ~/.kube/config

🛠️ Resolving DNS Resolution Failure from Cloud Runner (kubernetes.docker.internal:6443: no such host)

After resolving the certificate error, the following network timeout and DNS resolution error occurred during the deployment step:

E0528 01:43:09.437587 2260 memcache.go:265] "Unhandled Error" err="couldn't get current server API group list: Get \"https://kubernetes.docker.internal:6443/api?timeout=32s\": dial tcp: lookup kubernetes.docker.internal on 127.0.0.53:53: no such host"
Unable to connect to the server: dial tcp: lookup kubernetes.docker.internal on 127.0.0.53:53: no such host

Cause

The standard GitHub Actions hosted runner (runs-on: ubuntu-latest) runs on a cloud virtual machine provided by GitHub. Consequently, it cannot resolve kubernetes.docker.internal, which is the private DNS of the local development environment (Docker Desktop), and cannot route to the local Kubernetes API server.

Resolution

To directly access resources within the local network, set up a Self-Hosted Runner on the local machine.

In the GitHub repository, go to “Settings” -> “Actions” -> “Runners”, select “New self-hosted runner”, and specify “Windows” as the OS.
Run the following commands in local PowerShell to download and extract the runner package:

mkdir actions-runner
cd actions-runner
Invoke-WebRequest -Uri https://github.com/actions/runner/releases/download/v2.334.0/actions-runner-win-x64-2.334.0.zip -OutFile actions-runner-win-x64-2.334.0.zip
Add-Type -AssemblyName System.IO.Compression.FileSystem
[System.IO.Compression.ZipFile]::ExtractToDirectory("$PWD/actions-runner-win-x64-2.334.0.zip", "$PWD")

.\config.cmd --url https://github.com/giturl-id/tomcat-k8s --token <your_token>

Start the runner.

.\run.cmd

Modify the execution environment target in the workflow file.

# Before
runs-on: ubuntu-latest

# After
runs-on: self-hosted

🛠️ Resolving mkdir -p Command Execution Error in Windows Environment

When switching the execution environment to the Windows Self-Hosted Runner, the following error occurred during the directory creation step:

mkdir : An item with the specified name C:\Users\Administrator\.kube already exists.
At C:\study\tomcat\actions-runner\_work\_temp\836d0b14-98fc-4377-a457-faf5123b7885.ps1:2 char:1
+ mkdir -p ~/.kube
+ ~~~~~~~~~~~~~~~
 + CategoryInfo : ResourceExists: (C:\Users\Administrator\.kube:String) [New-Item], IOException
 + FullyQualifiedErrorId : DirectoryExist,Microsoft.PowerShell.Commands.NewItemCommand

Cause

On a Windows Self-Hosted Runner, GitHub Actions steps run in PowerShell by default. In PowerShell, mkdir is an alias for New-Item -ItemType Directory, which does not support the -p option. Additionally, if the target directory already exists, PowerShell throws an IOException and terminates with exit code 1.

Resolution

Change the logic to use native PowerShell syntax to check for directory existence before creation. Also, handle the Base64 decoding entirely within PowerShell using .NET runtime features.

 - name: Set kube config
 shell: powershell
 run: |
 if (!(Test-Path "$HOME\.kube")) {
 New-Item -ItemType Directory -Path "$HOME\.kube"
 }
 
 [System.Text.Encoding]::UTF8.GetString(
 [System.Convert]::FromBase64String("${{ secrets.KUBE_CONFIG }}")
 ) | Out-File "$HOME\.kube\config" -Encoding utf8

🛠️ Resolving Kubernetes Pod Image Pull Error (ErrImagePull)

After executing the deployment, the pod status became ErrImagePull, and the container failed to start.

kubectl get pods
# Output:
# NAME READY STATUS RESTARTS AGE
# tomcat2-deployment-59d4ff8df8-cwwb2 0/1 ErrImagePull 0 9s

Cause

Because imagePullPolicy in the manifest file (Deployment.yaml) is set to Always, Kubernetes forces a query to the external registry (such as DockerHub) for the latest image, even if the image exists in the local Docker cache. If the image has not been pushed to the remote registry or credentials are missing, this pull process fails.

Resolution

When using locally built images directly in a development environment, change imagePullPolicy to IfNotPresent to skip querying the external registry.

Modify the container definition in Deployment.yaml as follows:

spec:
 containers:
 - name: tomcat
 image: abungard/my-tomcat:latest
 imagePullPolicy: IfNotPresent

Delete the existing deployment and reapply.

kubectl delete deployment tomcat2-deployment
kubectl apply -f Deployment.yaml

Verify the pod startup status.

kubectl get pods

Verify that the status transitions to Running.

NAME READY STATUS RESTARTS AGE
tomcat2-deployment-59d4ff8df8-cwwb2 1/1 Running 0 12s
```</your_token>

Analysis of LocalStorage Data Loss in PWA and IndexedDB Migration Steps via localForage

Fri, 22 May 2026 10:42:03 +0900

Incident: Sudden LocalStorage Data Loss in PWA Environment

In “Dan-Haru,” a routine management application deployed as a PWA (Progressive Web App), a data loss incident occurred approximately one month after production launch, where all user routine records, custom settings, and configuration parameters were completely initialized.

The developer tools console log recorded the following exceptions and empty data states:

// Console Log
Uncaught DOMException: Failed to execute 'setItem' on 'Storage': Setting the value of 'routine_activity_log' exceeded the quota.
localStorage.getItem('routine_app_user_data') -&gt; null

This state is identical to a fresh application installation, indicating that the client-side data store was completely wiped.

⚠️ Root Causes of Data Loss: iOS Eviction Policy and 5MB Capacity Limit

The technical factors causing this data loss stem from the following three points related to browser LocalStorage specifications and OS storage management algorithms:

1. Forced Storage Cleanup by OS (Storage Eviction)

In iOS/iPadOS (Safari/WebKit Webview) environments, if a PWA is not launched for seven consecutive days, or if device free space becomes extremely low, the OS treats LocalStorage as “temporary cache files” and deletes them automatically. This is the Storage Eviction policy. Additionally, when background processes are force-terminated due to memory (RAM) pressure, write operations to LocalStorage are interrupted, leading to data resets due to file corruption.

2. Write Errors Due to Exceeding Capacity Limit (5MB)

The maximum capacity of LocalStorage is limited to 5MB. Data accumulation simulations for high-frequency users (30 groups × 30 routines each = 900 routines total) revealed that daily data accumulation reaches approximately 237KB.

routine_activity_log (1440-minute heatmap): Approx. 2.9 KB
WakeUpTimeHistory: Approx. 0.08 KB
RoutineGroupHistory (30 groups): Approx. 7.8 KB
TaskHistory (900 routines): Approx. 180 KB
routine_app_user_data (metadata): Approx. 46.2 KB
Total daily accumulation: Approx. 237 KB/day Based on this data density, the 5MB limit is reached in just approx. 21 days, after which subsequent writes fail by throwing a QuotaExceededError. If reset logic such as localStorage.clear() is erroneously executed within exception handling, all data is lost.

💡 Implementing Data Persistence via IndexedDB Migration using localForage

To eliminate the 5MB capacity limit and volatility of LocalStorage, migrate to IndexedDB, which supports asynchronous processing and can utilize up to 50% of available device space. localForage (v1.10.0) is adopted as a wrapper library, and existing synchronous code is refactored into asynchronous processing.

1. Initialization of localForage and Implementation of Migration Script

Implement logic to extract data from LocalStorage and safely migrate it to IndexedDB.

import localforage from 'localforage';

localforage.config({
driver: localforage.INDEXEDDB,
name: 'Dan-Haru',
storeName: 'user_settings'
});

async function migrateFromLocalStorage() {
const keys = [
'routine_activity_log',
'WakeUpTimeHistory',
'RoutineGroupHistory',
'TaskHistory',
'routine_app_user_data'
];

for (const key of keys) {
const localData = localStorage.getItem(key);
if (localData) {
try {
await localforage.setItem(key, JSON.parse(localData));
localStorage.removeItem(key);
} catch (error) {
console.error(`Migration failed for key ${key}:`, error);
}
}
}
}

2. Implementation of FIFO (First-In-First-Out) Pruning to Control Data Volume

To prevent data bloat, incorporate pruning logic that automatically deletes detailed logs older than 30 days while retaining only statistical data.

async function pruneOldLogs() {
const thresholdDate = new Date();
thresholdDate.setDate(thresholdDate.getDate() - 30);
const limitTime = thresholdDate.getTime();

try {
const logs = await localforage.getItem('routine_activity_log') || [];
const filteredLogs = logs.filter(log =&gt; new Date(log.timestamp).getTime() &gt;= limitTime);
await localforage.setItem('routine_activity_log', filteredLogs);
} catch (error) {
console.error("Pruning failed:", error);
}
}

🛠️ Verification Procedures for Data Persistence and Storage Usage Post-Migration

Verify whether the migration process is functioning correctly and if the OS recognizes it as persistent storage.

1. Capacity Verification via Browser Storage Estimate API

Execute navigator.storage.estimate() from the console to check the allocated quota and current usage.

if (navigator.storage &amp;&amp; navigator.storage.estimate) {
navigator.storage.estimate().then(estimate =&gt; {
console.log(`Quota: ${estimate.quota} bytes`);
console.log(`Usage: ${estimate.usage} bytes`);
});
}

Example output of execution results:

{
"quota": 21474836480,
"usage": 242688
}

This confirms that a quota in the gigabyte range has been secured, exceeding the traditional 5MB limit.

2. Requesting and Confirming Persistent Storage

Explicitly request the browser to exclude the storage from automatic deletion targets.

if (navigator.storage &amp;&amp; navigator.storage.persist) {
navigator.storage.persist().then(granted =&gt; {
console.log(`Persistent storage granted: ${granted}`);
});
}

Execution result:

true

By returning true, it is verified that a protected state has been established where IndexedDB data is not subject to forced deletion (Eviction) even when device free space is low.