eve-deploy-debugging

incept5/eve-skillpacks · updated Apr 8, 2026

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$npx skills add https://github.com/incept5/eve-skillpacks --skill eve-deploy-debugging
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summary

Use these steps to deploy and diagnose app issues quickly.

skill.md

Eve Deploy and Debug

Use these steps to deploy and diagnose app issues quickly.

Environment Setup

  • Get the staging API URL from your admin.
  • Create and use a profile:
eve profile create staging --api-url https://api.eh1.incept5.dev
eve profile use staging

Infrastructure Change Policy

Never run kubectl apply, helm install, or any direct Kubernetes resource creation against shared infrastructure. All infrastructure changes go through Terraform. Use the Eve CLI (eve env, eve env deploy) to manage application deployments — the platform handles the underlying k8s resources.

Deploy Flow (Staging)

# Create env if needed
eve env create staging --project proj_xxx --type persistent

# Deploy (requires --ref with 40-char SHA or a ref resolved against --repo-dir)
eve env deploy staging --ref main --repo-dir .

# When environment has a pipeline configured, the above triggers the pipeline.
# Use --direct to bypass pipeline and deploy directly:
eve env deploy staging --ref main --repo-dir . --direct

# Pass inputs to pipeline:
eve env deploy staging --ref main --repo-dir . --inputs '{"key":"value"}'

Deploy Polling Flow

When eve env deploy is called:

  1. Direct deploy (no pipeline): Returns deployment_status directly. Poll health endpoint until ready === true.
  2. Pipeline deploy: Returns pipeline_run_id. Poll GET /pipelines/{name}/runs/{id} until all steps complete, then check health.

Deploy is complete when: ready === true AND active_pipeline_run === null.

Observe the Deploy

eve job list --phase active
eve job follow <job-id>              # Real-time SSE streaming
eve job watch <job-id>               # Poll-based status updates
eve job diagnose <job-id>            # Full diagnostic
eve job result <job-id>              # Final result
eve job runner-logs <job-id>         # Raw worker logs

Real-Time Debugging (3-Terminal Approach)

# Terminal 1: Pipeline/job progress
eve job follow <job-id>

# Terminal 2: Environment health
eve env diagnose <project> <env>

# Terminal 3: System-level logs
eve system logs

Debugging Workflows

Job Won't Start

  1. Check dependencies: eve job dep list <job-id>
  2. Check if blocked: eve job show <job-id> → look at blocked_by
  3. Verify environment readiness: eve env show <project> <env>
  4. Check orchestrator: eve system orchestrator status

Job Failed

  1. Get the error: eve job diagnose <job-id>
  2. Check logs: eve job follow <job-id> or eve job runner-logs <job-id>
  3. If build failure: eve build diagnose <build-id>
  4. If secret failure: eve secrets list --project <project_id>

Job Stuck Active

  1. Check if waiting for input: eve job show <job-id>effective_phase
  2. Check thread messages: eve thread messages <thread-id>
  3. Check runner pod: eve system pods

System Issues

  1. API health: eve system health
  2. Orchestrator: eve system orchestrator status
  3. Recent events: eve system events

Common Error Messages

Error Cause Fix
401 Unauthorized Token expired eve auth login
git clone failed Missing credentials Set github_token or ssh_key secret
service not provisioned Environment not created eve env create <env>
image pull backoff Registry auth failed If using BYO/custom registry, verify REGISTRY_USERNAME + REGISTRY_PASSWORD; for managed apps use registry: "eve"
healthcheck timeout App not starting Check app logs, verify ports in manifest

Build Failures

If a deploy pipeline fails at the build step:

eve build list --project <project_id>
eve build diagnose <build_id>
eve build logs <build_id>
eve secrets list --project <project_id>     # Required for BYO/custom registry: REGISTRY_USERNAME, REGISTRY_PASSWORD

Common build failures:

  • Registry auth: For BYO/custom registry, verify REGISTRY_USERNAME and REGISTRY_PASSWORD secrets
  • Dockerfile not found: Check build.context path in manifest
  • Multi-stage build failure: BuildKit handles these correctly; Kaniko may have issues
  • Workspace errors: Build context not available — check eve build diagnose

Worker Image Registry

Eve publishes worker images to the configured private registry with these variants:

Variant Contents
base Node.js, git, standard CLI tools
python Base + Python runtime
rust Base + Rust toolchain
java Base + JDK
kotlin Base + Kotlin compiler
full All runtimes combined

Version pinning: Use semver tags (e.g., v1.2.3) in production. Use SHA tags or :latest in development.

Platform Environment Variables

Eve automatically injects these into every deployed service container:

Variable Purpose
EVE_API_URL Internal cluster URL for server-to-server calls
EVE_PUBLIC_API_URL Public ingress URL for browser-facing apps (when configured)
EVE_SSO_URL SSO broker URL for user authentication (when configured)
EVE_PROJECT_ID Current project ID
EVE_ORG_ID Current organization ID
EVE_ENV_NAME Current environment name

Use EVE_API_URL for backend calls. Use EVE_PUBLIC_API_URL for browser/client-side code. Services can override any of these by defining them explicitly in their manifest environment section.

Access URLs

  • URL pattern: {service}.{orgSlug}-{projectSlug}-{env}.{domain}
  • Local dev default domain: lvh.me
  • Ask the admin for the correct domain (staging vs production).

Environment-Specific Debugging

Environment How to Debug
Local (k3d) Direct service access via ingress, eve system logs
Docker Compose docker compose logs <service>, dev-only (no production use)
Kubernetes Ingress-based access, kubectl -n eve logs as last resort

Private Endpoints (Tailscale)

Connect services on private networks (home lab GPUs, internal APIs, dev machines) to the Eve cluster. The platform creates K8s ExternalName services backed by Tailscale egress proxies.

# Register a private endpoint
eve endpoint add \
  --name lmstudio \
  --provider tailscale \
  --tailscale-hostname mac-mini.tail12345.ts.net \
  --port 1234 \
  --org org_xxx

# List and inspect
eve endpoint list --org org_xxx
eve endpoint show lmstudio --org org_xxx

# Diagnose connectivity
eve endpoint diagnose lmstudio

# Remove
eve endpoint remove lmstudio --org org_xxx

Each endpoint gets a stable in-cluster DNS name: http://{orgSlug}-{name}.eve-tunnels.svc.cluster.local:{port}. Wire it into apps/agents via secrets:

eve secrets set LLM_BASE_URL \
  "http://myorg-lmstudio.eve-tunnels.svc.cluster.local:1234/v1" \
  --scope project

Diagnostics check: operator status, K8s service existence, DNS resolution, TCP connectivity, and HTTP health.

Worker Toolchain-on-Demand

The default worker image is base (~800MB with Node.js, git, and all harnesses). Toolchains (Python, Rust, Java, Kotlin, media) are injected on-demand via init containers rather than bundled in a fat image.

Deployment impact: If an agent job needs toolchains, the runner pod starts init containers that copy toolchain binaries from small pre-built images. First pull adds ~5-10s; subsequent jobs on the same node use cached images.

Debugging toolchain issues:

# Check if toolchains are declared in agent config
# agents.yaml: toolchains: [python]

# If a toolchain binary is missing at runtime:
# 1. Verify agent config has the toolchain declared
# 2. Check init container logs on the runner pod
# 3. Verify toolchain images are available in the registry

To use the full image (all toolchains bundled): set EVE_WORKER_VARIANT=full or use --variant full locally.

App Undeploy/Delete Lifecycle

Remove environments and clean up resources:

# Undeploy services from an environment (stops pods, keeps env record)
eve env undeploy <project> <env>

# Delete the environment entirely (removes env record, managed DB, secrets)
eve env delete <project> <env>

When a managed DB is attached, eve env delete deprovisions it. Secrets scoped to the environment are cleaned up. The environment's pipeline history remains in the audit log.

For app-level cleanup, remove the project:

eve project delete <project-id>

This cascades: environments, secrets, pipeline history, and build artifacts are removed.

Workspace Janitor

Production disk management for agent workspaces:

  • EVE_WORKSPACE_MAX_GB — total workspace budget
  • EVE_WORKSPACE_MIN_FREE_GB — trigger cleanup threshold
  • EVE_SESSION_TTL_HOURS — auto-evict stale sessions
  • LRU eviction when approaching budget; TTL cleanup for idle sessions
  • K8s: per-attempt PVCs deleted on completion

Related Skills

  • Local dev loop: eve-local-dev-loop
  • Secrets: eve-auth-and-secrets
  • Manifest changes: eve-manifest-authoring
how to use eve-deploy-debugging

How to use eve-deploy-debugging on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add eve-deploy-debugging
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/incept5/eve-skillpacks --skill eve-deploy-debugging

The skills CLI fetches eve-deploy-debugging from GitHub repository incept5/eve-skillpacks and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/eve-deploy-debugging

Reload or restart Cursor to activate eve-deploy-debugging. Access the skill through slash commands (e.g., /eve-deploy-debugging) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

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general reviews

Ratings

4.647 reviews
  • Xiao Farah· Dec 28, 2024

    I recommend eve-deploy-debugging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Shikha Mishra· Dec 4, 2024

    eve-deploy-debugging has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Amelia Gupta· Dec 4, 2024

    Keeps context tight: eve-deploy-debugging is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Aanya Tandon· Nov 19, 2024

    Useful defaults in eve-deploy-debugging — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Nikhil Haddad· Sep 25, 2024

    eve-deploy-debugging fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Oshnikdeep· Sep 21, 2024

    eve-deploy-debugging reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hana Zhang· Sep 17, 2024

    I recommend eve-deploy-debugging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Maya Huang· Sep 13, 2024

    eve-deploy-debugging is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Aanya Ramirez· Aug 16, 2024

    Registry listing for eve-deploy-debugging matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Ganesh Mohane· Aug 12, 2024

    I recommend eve-deploy-debugging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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