Initial commit: Claude Code config and K8s agent orchestrator design

- Add .gitignore for logs, caches, credentials, and history
- Add K8s agent orchestrator design document
- Include existing Claude Code settings and plugin configs

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
OpenCode Test
2025-12-26 11:16:07 -08:00
commit 216a95cec4
9 changed files with 1116 additions and 0 deletions

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# Logs
logs/
# Secrets and credentials
*.key
*.pem
*.secret
credentials/
.credentials.json
# Cache and state
.cache/
stats-cache.json
statsig/
# History and debug
history.jsonl
file-history/
shell-snapshots/
debug/
# Plugin caches (contain embedded git repos)
plugins/cache/
plugins/marketplaces/
# Local settings
settings.local.json
# Conversation history (private/large)
projects/
# Temporary files
*.tmp
*.swp
# Todos (managed by Claude Code)
todos/

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# Agent Orchestrator System - Brainstorming Notes
## Overview
User-level Claude Code agent system with orchestrator + specialized subagents + workflows.
Location: `~/.claude/`
## Target Domains (for future expansion)
- **A) DevOps/Infrastructure** - PRIMARY - Raspberry Pi K8s cluster management
- B) Software development - Code generation, refactoring, testing
- C) Research & analysis - Information gathering, summarizing
- D) Personal productivity - Files, notes, tasks, schedules
- E) Multi-domain - General-purpose tasks
## Primary Use Case
- Raspberry Pi Kubernetes cluster management
- App deployment to the cluster
- K8s distribution: **k0s**
- Deployment method: **GitOps with ArgoCD**
## Cluster Hardware
| Node | Hardware | RAM | Role |
|------|----------|-----|------|
| Node 1 | Raspberry Pi 5 | 8GB | Control plane + Worker |
| Node 2 | Raspberry Pi 5 | 8GB | Worker |
| Node 3 | Raspberry Pi 3B+ | 1GB | Worker (tainted, tolerations required) |
**Pi 3 node**: Reserved for lightweight workloads only. Good candidate for dashboard deployment.
**Architecture**: All nodes run arm64 (64-bit OS).
## Workloads
- Self-hosted services (home automation, media, personal tools)
- Development/testing environments
- Infrastructure services (monitoring, logging, databases)
## Agent Tasks (priority order)
1. **Cluster health monitoring** - Detect issues, diagnose, suggest/apply fixes (TOP PRIORITY)
2. Deployment management - Create/update deployments, ArgoCD sync, rollbacks
3. Resource management - Scaling, allocation, cleanup
4. App lifecycle - End-to-end "I want to run X" to deployed
5. Incident response - Alerting, investigation, remediation
## Autonomy Model
- **Tiered autonomy**: Safe actions auto-apply, risky actions require confirmation
- Safe: restart pod, scale replicas, clear completed jobs
- Risky: delete PVC, modify configs, node operations
## Interaction Methods
- **Terminal/CLI** - Primary interaction via Claude Code (also fallback when cluster is down)
- **Dashboard/UI** - Web interface deployed on cluster via ArgoCD
- **Push notifications** - Future consideration (Discord/Slack/Telegram)
## Infrastructure Stack
- Monitoring: **Prometheus + Alertmanager + Grafana**
- GitOps repo: **Self-hosted Gitea/Forgejo**
- Workflow triggers: **Scheduled + Event-driven (Alertmanager webhooks)**
## Implementation Approach
**Phase 1**: Claude Code skills + custom subagent types in `~/.claude/`
**Phase 2 (later)**: Add SDK-based daemon for background automation
## Subagents
1. **k8s-diagnostician** - Cluster health, pod/node status, resource utilization, log analysis
2. **argocd-operator** - App sync, deployments, rollbacks, GitOps operations
3. **prometheus-analyst** - Query metrics, analyze trends, interpret alerts
4. **git-operator** - Commit manifests, create PRs in Gitea, manage GitOps repo
## Workflow Definitions
- **YAML** - Complex workflows with branching, conditions, multi-step
- **Markdown** - Simple workflows, prose-like descriptions
## CLI Tools Available
- kubectl
- argocd CLI
- k0sctl
## Model Assignment
- **Default**: Orchestrator = Opus, Subagents = Sonnet
- **Override levels**:
1. Per-workflow: specify model in workflow YAML
2. Per-step: specify model for individual workflow steps
3. Dynamic: Orchestrator can downgrade/upgrade model per-delegation based on task complexity
- **Cost optimization**: Orchestrator evaluates task complexity and selects appropriate model
- Simple queries (get status, list) → Haiku
- Standard operations (analyze, diagnose) → Sonnet
- Complex reasoning (root cause, multi-factor decisions) → Opus

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# K8s Agent Orchestrator System - Design Document
## Overview
A user-level Claude Code agent system for autonomous Kubernetes cluster management. The system consists of an orchestrator agent that delegates to specialized subagents, with workflow definitions for common operations and a tiered autonomy model.
**Location**: `~/.claude/`
**Primary Domain**: DevOps/Infrastructure
**Target**: Raspberry Pi k0s cluster
---
## Cluster Environment
### Hardware
| Node | Hardware | RAM | Role |
|------|----------|-----|------|
| Node 1 | Raspberry Pi 5 | 8GB | Control plane + Worker |
| Node 2 | Raspberry Pi 5 | 8GB | Worker |
| Node 3 | Raspberry Pi 3B+ | 1GB | Worker (tainted, tolerations required) |
- **Architecture**: All nodes run arm64 (64-bit OS)
- **Pi 3 node**: Reserved for lightweight workloads only
### Stack
| Component | Technology |
|-----------|------------|
| K8s Distribution | k0s |
| GitOps | ArgoCD |
| Git Hosting | Self-hosted Gitea/Forgejo |
| Monitoring | Prometheus + Alertmanager + Grafana |
### CLI Tools Available
- `kubectl`
- `argocd`
- `k0sctl`
---
## Architecture
### Three-Layer Design
```
┌─────────────────────────────────────────────────────────────┐
│ User Interface │
│ Terminal (CLI) | Dashboard (Web) │
└─────────────────────┬───────────────────┬───────────────────┘
│ │
┌─────────────────────▼───────────────────▼───────────────────┐
│ Orchestrator Layer │
│ k8s-orchestrator │
│ (Opus - complex reasoning, task delegation) │
└─────────────────────┬───────────────────────────────────────┘
│ delegates to
┌─────────────────────▼───────────────────────────────────────┐
│ Specialist Layer │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────┐│
│ │k8s- │ │argocd- │ │prometheus- │ │git- ││
│ │diagnostician│ │operator │ │analyst │ │operator ││
│ │(Sonnet) │ │(Sonnet) │ │(Sonnet) │ │(Sonnet) ││
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────┘│
└─────────────────────────────────────────────────────────────┘
│ defined by
┌─────────────────────▼───────────────────────────────────────┐
│ Workflow Layer │
│ YAML (complex) | Markdown (simple) │
└─────────────────────────────────────────────────────────────┘
```
### Directory Structure
```
~/.claude/
├── settings.json # Agent definitions, autonomy rules
├── agents/
│ ├── k8s-orchestrator.md # Orchestrator prompt
│ ├── k8s-diagnostician.md # Cluster diagnostics specialist
│ ├── argocd-operator.md # GitOps operations specialist
│ ├── prometheus-analyst.md # Metrics analysis specialist
│ └── git-operator.md # Git/Gitea operations specialist
├── workflows/
│ ├── health/
│ │ ├── cluster-health-check.yaml
│ │ └── node-pressure-response.yaml
│ ├── deploy/
│ │ ├── deploy-app.md
│ │ └── rollback-app.yaml
│ └── incidents/
│ └── pod-crashloop.yaml
├── skills/
│ ├── cluster-status.md
│ ├── deploy.md
│ ├── diagnose.md
│ ├── rollback.md
│ └── workflow.md
├── logs/
│ ├── actions/ # Action audit trail
│ └── workflows/ # Workflow execution logs
└── docs/plans/
```
---
## Subagent Definitions
### settings.json
```json
{
"agents": {
"k8s-orchestrator": {
"model": "opus",
"promptFile": "agents/k8s-orchestrator.md"
},
"k8s-diagnostician": {
"model": "sonnet",
"promptFile": "agents/k8s-diagnostician.md"
},
"argocd-operator": {
"model": "sonnet",
"promptFile": "agents/argocd-operator.md"
},
"prometheus-analyst": {
"model": "sonnet",
"promptFile": "agents/prometheus-analyst.md"
},
"git-operator": {
"model": "sonnet",
"promptFile": "agents/git-operator.md"
}
},
"autonomy": {
"safe_actions": ["get", "describe", "logs", "list", "top", "diff"],
"confirm_actions": ["delete", "patch", "edit", "scale", "rollout", "apply"],
"forbidden_actions": ["drain", "cordon", "delete node", "reset"]
}
}
```
### Subagent Responsibilities
| Agent | Scope | Tools |
|-------|-------|-------|
| **k8s-orchestrator** | Task analysis, delegation, decision making | All (via delegation) |
| **k8s-diagnostician** | Cluster health, pod/node status, logs | kubectl, log tools |
| **argocd-operator** | App sync, deployments, rollbacks | argocd CLI, kubectl |
| **prometheus-analyst** | Metrics, alerts, trends | PromQL, Prometheus API |
| **git-operator** | Manifest commits, PRs, GitOps repo | git, Gitea API |
---
## Model Assignment
### Defaults
- **Orchestrator**: Opus (complex reasoning, task delegation)
- **Subagents**: Sonnet (standard operations)
### Override Levels
1. **Per-workflow**: Specify in workflow YAML
2. **Per-step**: Specify for individual workflow steps
3. **Dynamic**: Orchestrator selects based on task complexity
### Dynamic Model Selection (Orchestrator Logic)
| Task Complexity | Model | Examples |
|-----------------|-------|----------|
| Simple | Haiku | Get status, list resources, log tail |
| Standard | Sonnet | Analyze logs, diagnose issues, sync apps |
| Complex | Opus | Root cause analysis, cascading failures, trade-off decisions |
**Delegation syntax:**
```markdown
Delegate to k8s-diagnostician (haiku):
Task: Get current node status
Delegate to prometheus-analyst (sonnet):
Task: Analyze memory trends for namespace "prod" over last 24h
Delegate to k8s-diagnostician (opus):
Task: Investigate cascading failure across multiple services
```
---
## Workflow Definitions
### YAML Workflows (Complex)
```yaml
name: cluster-health-check
description: Comprehensive cluster health assessment
model: sonnet # optional default override
trigger:
- schedule: "0 */6 * * *" # every 6 hours
- manual: true
steps:
- agent: k8s-diagnostician
model: haiku # simple status check
task: Check node status and resource pressure
- agent: prometheus-analyst
task: Query for anomalies in last 6 hours
- agent: argocd-operator
model: haiku
task: Check all apps sync status
- agent: k8s-orchestrator
task: Summarize findings and recommend actions
confirm_if: actions_proposed
```
### Markdown Workflows (Simple)
```markdown
# Deploy New App
When asked to deploy a new application:
1. Ask git-operator to create the manifest structure in the GitOps repo
2. Ask argocd-operator to create and sync the ArgoCD application
3. Ask k8s-diagnostician to verify pods are running
4. Report deployment status
```
### Incident Response Workflow Example
```yaml
name: pod-crashloop-remediation
trigger:
type: alert
match:
alertname: KubePodCrashLooping
steps:
- name: diagnose
agent: k8s-diagnostician
action: get-pod-status
inputs:
namespace: "{{ alert.labels.namespace }}"
pod: "{{ alert.labels.pod }}"
- name: check-logs
agent: k8s-diagnostician
action: analyze-logs
inputs:
pod: "{{ steps.diagnose.pod }}"
lines: 100
- name: decide-action
condition: "{{ steps.check-logs.cause == 'oom' }}"
branches:
true:
agent: argocd-operator
action: update-resources
confirm: true # risky action
false:
agent: k8s-diagnostician
action: restart-pod
confirm: false # safe action
- name: notify
action: report
outputs:
- summary
- actions-taken
```
---
## Autonomy Model
### Tiered Autonomy
| Action Type | Behavior | Examples |
|-------------|----------|----------|
| **Safe** | Auto-execute, log action | get, describe, logs, list, restart pod |
| **Confirm** | Require user approval | delete, patch, scale, apply, modify config |
| **Forbidden** | Reject with explanation | drain, cordon, delete node |
### Confirmation Flow
```
1. Agent proposes action with rationale
2. System checks action against autonomy rules
3. If safe → execute immediately, log action
4. If confirm → present to user (CLI prompt or dashboard queue)
5. If forbidden → reject with explanation
```
### Per-Workflow Overrides
```yaml
name: emergency-pod-restart
autonomy:
auto_approve:
- restart_pod
- scale_replicas
always_confirm:
- delete_pvc
```
### Action Logging
```
~/.claude/logs/actions/2025-12-26-actions.jsonl
```
Each entry includes:
- Timestamp
- Agent
- Action
- Inputs
- Outcome
- Approval type (auto/user-confirmed)
---
## Skills (User Entry Points)
| Skill | Command | Purpose |
|-------|---------|---------|
| cluster-status | `/cluster-status` | Quick health overview |
| deploy | `/deploy <app>` | Deploy or update an app |
| diagnose | `/diagnose <issue>` | Investigate a problem |
| rollback | `/rollback <app>` | Revert to previous version |
| workflow | `/workflow <name>` | Run a named workflow |
### Example Skill: cluster-status.md
```markdown
# Cluster Status
Invoke the k8s-orchestrator to provide a quick health overview.
## Steps
1. Delegate to k8s-diagnostician: get node status
2. Delegate to prometheus-analyst: check for active alerts
3. Delegate to argocd-operator: list out-of-sync apps
4. Summarize in a concise table
## Output Format
- Node health: table
- Active alerts: bullet list
- ArgoCD status: table
- Recommendations: if any issues found
```
---
## Interaction Methods
### Terminal/CLI
- Primary interaction via Claude Code
- Fallback when cluster is unavailable
- Use skills to invoke workflows
### Dashboard (Web UI)
- Deployed on cluster (Pi 3 node)
- Views: Status, Pending Confirmations, History, Workflows
- Approve/reject risky actions
### Push Notifications (Future)
- Discord, Slack, or Telegram integration
- Alert on issues requiring attention
---
## Dashboard Specification
### Tech Stack
- **Backend**: Go binary (single static binary, embedded assets)
- **Storage**: SQLite or flat JSON files
- **Resources**: Minimal footprint for Pi 3
### Deployment
```yaml
apiVersion: apps/v1
kind: Deployment
metadata:
name: k8s-agent-dashboard
spec:
replicas: 1
template:
spec:
containers:
- name: dashboard
image: k8s-agent-dashboard:latest
resources:
requests:
memory: "32Mi"
cpu: "10m"
limits:
memory: "64Mi"
cpu: "100m"
tolerations:
- key: "node-type"
operator: "Equal"
value: "pi3"
effect: "NoSchedule"
nodeSelector:
kubernetes.io/arch: arm64
```
### Views
| View | Description |
|------|-------------|
| Status | Current cluster health, active alerts, ArgoCD sync state |
| Pending | Actions awaiting confirmation with approve/reject buttons |
| History | Recent actions taken, filterable by agent/workflow |
| Workflows | List of defined workflows, manual trigger capability |
---
## Implementation Phases
### Phase 1: Core Agent System
**Deliverables:**
- `~/.claude/` directory structure
- Orchestrator and 4 subagent prompt files
- `settings.json` with agent configurations
- 3-4 essential workflows (cluster-health, deploy, diagnose)
- Core skills (/cluster-status, /deploy, /diagnose)
**Validation:**
- Manual CLI invocation
- Test each subagent independently
- Run health check workflow end-to-end
### Phase 2: Dashboard
**Deliverables:**
- Go-based dashboard application
- Kubernetes manifests for Pi 3 deployment
- Pending confirmations queue
- Action history view
- Approval flow integration
### Phase 3: Automation
**Deliverables:**
- Scheduled workflow execution
- Alertmanager webhook integration
- Expanded incident response workflows
### Phase 4: Expansion (Future)
**Potential additions:**
- Push notifications (Discord/Telegram)
- Additional domains (development, research, productivity)
- SDK-based background daemon for true autonomy
---
## Future Domain Expansion
The system is designed to expand beyond DevOps:
| Domain | Use Cases |
|--------|-----------|
| Software Development | Code generation, refactoring, testing across repos |
| Research & Analysis | Information gathering, summarizing, recommendations |
| Personal Productivity | File management, notes, task tracking |
New domains would add:
- Additional subagents with specialized prompts
- Domain-specific workflows
- New skills for user invocation

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# Plan: Fix Zed Slow Launch and Performance on Hyprland
## Problem Analysis
**Original Request:** Compile Zed with OpenGL support instead of Vulkan for Intel HD Graphics 4000
**Actual Issue Discovered:** Zed is using X11/XWayland instead of native Wayland on Hyprland, causing:
- 15+ second launch times
- Sluggish runtime performance
- The hardware (Intel HD Graphics 4000) is properly supported and works fine on Gnome
**System Information:**
- Binary: `/usr/lib/zed/zed-editor` (Arch package v0.213.4-1)
- Command: `zeditor`
- Hyprland version: 0.52.1
- Mesa version: 25.2.7-arch1.1
- Environment: Both `WAYLAND_DISPLAY=wayland-1` and `DISPLAY=:1` are set
**Evidence from logs** (`~/.local/share/zed/logs/Zed.log`):
```
2025-12-12T22:12:02-08:00 INFO [gpui::platform::linux::x11::window] Using Visual { id: 112, colormap: 0, depth: 32 }
2025-12-12T22:12:02-08:00 INFO [gpui::platform::linux::x11::window] x11: no compositor present, falling back to server-side window decorations
2025-12-12T22:12:02-08:00 ERROR [gpui::platform::linux::x11::client] XIMClientError: Can't read xim message: Invalid Data ErrorCode: 0
```
## Root Cause
**Mystery Identified:** Despite both environment variables being correctly set (`WAYLAND_DISPLAY=wayland-1` and `DISPLAY=:1`), Zed is still choosing X11.
Based on the code in `crates/gpui/src/platform.rs` (lines 139-164), Zed should prioritize Wayland when `WAYLAND_DISPLAY` is set. The fact that it's using X11 despite this suggests:
**Most Likely:** The Arch package (v0.213.4-1) was compiled **without the `wayland` Cargo feature enabled**, causing the binary to only support X11 even when Wayland is available.
This is supported by:
- The platform selection code has `#[cfg(feature = "wayland")]` guards
- If Wayland feature is disabled at compile time, `guess_compositor()` will always return "X11"
- A similar issue was documented in Arch forums ([thread](https://bbs.archlinux.org/viewtopic.php?id=299290))
## Recommended Solution
**Compile Zed from source with Wayland support enabled.** The Arch package appears to lack Wayland support at compile time.
### Step 1: Build Zed with Wayland Support
From the repository root (`/home/will/repo/zed`):
```bash
# Clean any previous builds (optional)
cargo clean
# Build with Wayland feature explicitly enabled
cargo build --release --features gpui/wayland
# Or build the full zed binary with default features (which includes wayland)
cargo build --release -p zed
```
**Build time estimate:** 20-40 minutes on first build (depending on CPU)
### Step 2: Install the Binary
```bash
# Create installation directory if needed
mkdir -p ~/.local/bin
# Copy the binary
cp target/release/zed ~/.local/bin/zed-wayland
# Or replace the system zeditor
sudo cp target/release/zed /usr/local/bin/zeditor
```
### Step 3: Verify Wayland Support
```bash
# Launch and check logs
~/.local/bin/zed-wayland
grep -E "wayland|x11" ~/.local/share/zed/logs/Zed.log | tail -5
```
Expected output should show Wayland initialization, NOT X11 messages.
## Alternative: Install from AUR or Use Different Package
If you don't want to compile from source, consider:
1. **Install `zed-git` from AUR** (may have Wayland support):
```bash
paru -S zed-git # or yay -S zed-git
```
2. **File a bug report with Arch** to request Wayland support in the official package
3. **Wait for official Arch package update** to include Wayland support
## Why NOT OpenGL/GLES?
The original request was to compile with OpenGL instead of Vulkan. **This is unnecessary** because:
- Your Intel HD Graphics 4000 supports Vulkan (working fine on Gnome)
- The slowness is from X11/XWayland overhead, NOT the graphics backend
- Mesa drivers are properly installed (Vulkan, OpenGL 4.2, OpenGL ES 3.0 all working)
- Switching to GLES won't fix the X11 vs Wayland issue
**GLES compilation would only be needed if:**
- Vulkan completely fails (`vkcube` doesn't work)
- Zed shows "NoSupportedDeviceFound" error
- You get GPU-related crashes
## Expected Outcome
After compiling with Wayland support and running the new binary:
- **Launch time:** Should match Gnome performance (~2 seconds)
- **Runtime performance:** Smooth, no sluggishness
- **Logs:** Will show Wayland client initialization instead of X11
## Build Requirements
The system already has all necessary dependencies:
- Rust toolchain (rustup)
- Wayland development libraries (installed)
- Build tools (gcc, clang, cmake, etc.)
- Vulkan and Mesa drivers (working)
From `crates/gpui/Cargo.toml` (lines 40-59), the `wayland` feature depends on:
- `wayland-client`, `wayland-cursor`, `wayland-protocols` (all available on Arch)
- `blade-graphics` (already in use for Vulkan)
- Font libraries (cosmic-text, font-kit) - already installed
## Critical Files for Reference
- `crates/gpui/Cargo.toml:20` - Default features include "wayland" and "x11"
- `crates/gpui/Cargo.toml:40-59` - Wayland feature dependencies
- `crates/gpui/src/platform.rs:101-164` - Platform selection logic
- `crates/gpui/src/platform/linux/wayland/client.rs` - Wayland client implementation
- `crates/gpui/src/platform/linux/x11/client.rs` - X11 client implementation
## Implementation Steps
1. **Build from source** with Wayland support (20-40 min)
2. **Install the binary** to ~/.local/bin or /usr/local/bin
3. **Test launch** and verify Wayland is being used via logs
4. **Confirm performance** matches Gnome experience

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# Vulkan Installation Verification Plan
## Goal
Verify that Vulkan is correctly installed and functional on the system, and determine whether the Mesa warning about incomplete Ivy Bridge support is a concern.
## Background Context
**Hardware**: Intel HD Graphics 4000 (Ivy Bridge) - circa 2012
**Current Status**:
- Vulkan 1.4.328 is installed
- `vulkaninfo` runs successfully
- Mesa driver shows warning: "MESA-INTEL: warning: Ivy Bridge Vulkan support is incomplete"
- Zed editor launches successfully despite the warning
## Understanding the Warning
The "Ivy Bridge Vulkan support is incomplete" warning is **expected behavior** for your hardware:
1. **Why it appears**: Intel HD Graphics 4000 is a 3rd-generation (Ivy Bridge) GPU from 2012, before Vulkan was standardized
2. **What it means**: Mesa provides best-effort Vulkan support through a compatibility layer, but not all Vulkan features are hardware-accelerated
3. **Is it a problem?**: Generally no - applications will either:
- Use the incomplete Vulkan support (works for most tasks)
- Fall back to OpenGL automatically
- Use the software renderer (llvmpipe) as a last resort
## Verification Approach
### Step 1: Verify Vulkan Device Detection ✅
Diagnostic results confirm:
- ✅ Vulkan loader is installed (`vulkaninfo` successful)
- ✅ GPU is detected (Intel HD Graphics 4000 IVB GT2)
- ✅ Both hardware and software renderers available:
- Hardware: Intel HD Graphics 4000 (using intel_icd.x86_64.json)
- Software fallback: llvmpipe (LLVM 21.1.5, 256 bits)
- ✅ Vulkan ICD files present:
- intel_icd.x86_64.json (for Ivy Bridge/Broadwell)
- intel_hasvk_icd.x86_64.json (for Haswell+)
- lvp_icd.x86_64.json (lavapipe software renderer)
- nouveau_icd.x86_64.json (Nouveau open-source driver)
- nvidia_icd.json (NVIDIA proprietary driver)
### Step 2: Test Vulkan Functionality
Run simple Vulkan test applications to verify:
- Basic rendering works
- Applications can create Vulkan instances and devices
- The warning doesn't prevent normal operation
Commands to run:
```bash
# Verify Vulkan ICD (Installable Client Driver) files
ls -la /usr/share/vulkan/icd.d/
# Check which Vulkan layers are available
vulkaninfo --summary | grep -A 5 "Layer"
# Test with a simple Vulkan application (if vkcube is installed)
vkcube || echo "vkcube not installed, will try alternative"
# Check if Zed can actually use Vulkan
VK_ICD_FILENAMES=/usr/share/vulkan/icd.d/intel_icd.x86_64.json /usr/lib/zed/zed-editor --version
```
### Step 3: Verify Mesa Driver Status
Confirm Mesa drivers are up to date:
```bash
# Check Mesa version
glxinfo | grep "OpenGL version"
# Verify Intel driver is loaded
lsmod | grep i915
# Check Vulkan driver info
vulkaninfo | grep -A 10 "GPU id.*Intel"
```
### Step 4: Document Findings
Create a summary report showing:
1. Vulkan is installed: ✅ (version 1.4.328)
2. GPU is detected: ✅ (Intel HD Graphics 4000 + llvmpipe fallback)
3. Warning is expected: ✅ (Ivy Bridge has incomplete Vulkan support by design)
4. Applications work: ✅/❌ (to be verified)
## Expected Outcome
**Vulkan is correctly installed** if:
- `vulkaninfo` runs without errors (✅ confirmed)
- At least one Vulkan device is available (✅ Intel HD 4000 + llvmpipe)
- Applications launch and run (✅ Zed editor works)
**The warning is harmless** because:
- It's informational, not an error
- Applications handle this gracefully by:
- Using available Vulkan features
- Falling back to OpenGL/software rendering
- Automatically selecting the best available renderer
## Recommendations
1. **No action needed** - Vulkan is working as well as it can on Ivy Bridge hardware
2. **Optional**: Set environment variable to suppress the warning if it's annoying:
```bash
export INTEL_DEBUG=nowarn
```
3. **Optional**: For better graphics performance, consider using OpenGL mode in applications when available (Ivy Bridge's OpenGL support is more mature than Vulkan)
## Files to Review
None - this is a system-level verification task, not a code modification.

View File

@@ -0,0 +1,186 @@
{
"version": 1,
"fetchedAt": "2025-12-26T18:14:58.883Z",
"counts": [
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},
{
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{
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{
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{
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{
"plugin": "document-skills@claude-plugins-official",
"unique_installs": 1
}
]
}

View File

@@ -0,0 +1,48 @@
{
"version": 2,
"plugins": {
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{
"scope": "user",
"installPath": "/home/will/.claude/plugins/cache/claude-plugins-official/frontend-design/6d3752c000e2",
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"lastUpdated": "2025-12-24T19:08:12.422Z",
"gitCommitSha": "6d3752c000e2b3d0e6137bd7adb04895d6f40f14",
"isLocal": true
}
],
"typescript-lsp@claude-plugins-official": [
{
"scope": "user",
"installPath": "/home/will/.claude/plugins/cache/claude-plugins-official/typescript-lsp/1.0.0",
"version": "1.0.0",
"installedAt": "2025-12-24T19:08:12.422Z",
"lastUpdated": "2025-12-24T19:08:12.422Z",
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}
],
"commit-commands@claude-plugins-official": [
{
"scope": "user",
"installPath": "/home/will/.claude/plugins/cache/claude-plugins-official/commit-commands/6d3752c000e2",
"version": "6d3752c000e2",
"installedAt": "2025-12-24T19:10:05.451Z",
"lastUpdated": "2025-12-24T19:10:36.843Z",
"isLocal": true
}
],
"superpowers@superpowers-marketplace": [
{
"scope": "user",
"installPath": "/home/will/.claude/plugins/cache/superpowers-marketplace/superpowers/4.0.2",
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"installedAt": "2025-12-26T18:15:13.657Z",
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"gitCommitSha": "131c1f189f75a14faad4227089841d1deee73e2a",
"isLocal": false
}
]
}
}

View File

@@ -0,0 +1,18 @@
{
"claude-plugins-official": {
"source": {
"source": "github",
"repo": "anthropics/claude-plugins-official"
},
"installLocation": "/home/will/.claude/plugins/marketplaces/claude-plugins-official",
"lastUpdated": "2025-12-26T18:15:48.383Z"
},
"superpowers-marketplace": {
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},
"installLocation": "/home/will/.claude/plugins/marketplaces/superpowers-marketplace",
"lastUpdated": "2025-12-26T18:14:47.747Z"
}
}

10
settings.json Normal file
View File

@@ -0,0 +1,10 @@
{
"enabledPlugins": {
"frontend-design@claude-plugins-official": true,
"typescript-lsp@claude-plugins-official": true,
"commit-commands@claude-plugins-official": true,
"superpowers@superpowers-marketplace": true
},
"alwaysThinkingEnabled": true,
"model": "opus"
}