eve-deploy-debugging▌
incept5/eve-skillpacks · updated Apr 8, 2026
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Use these steps to deploy and diagnose app issues quickly.
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:
- Direct deploy (no pipeline): Returns
deployment_statusdirectly. Poll health endpoint untilready === true. - Pipeline deploy: Returns
pipeline_run_id. PollGET /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
- Check dependencies:
eve job dep list <job-id> - Check if blocked:
eve job show <job-id>→ look atblocked_by - Verify environment readiness:
eve env show <project> <env> - Check orchestrator:
eve system orchestrator status
Job Failed
- Get the error:
eve job diagnose <job-id> - Check logs:
eve job follow <job-id>oreve job runner-logs <job-id> - If build failure:
eve build diagnose <build-id> - If secret failure:
eve secrets list --project <project_id>
Job Stuck Active
- Check if waiting for input:
eve job show <job-id>→effective_phase - Check thread messages:
eve thread messages <thread-id> - Check runner pod:
eve system pods
System Issues
- API health:
eve system health - Orchestrator:
eve system orchestrator status - 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_USERNAMEandREGISTRY_PASSWORDsecrets - Dockerfile not found: Check
build.contextpath 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 budgetEVE_WORKSPACE_MIN_FREE_GB— trigger cleanup thresholdEVE_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 on Cursor
AI-first code editor with Composer
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
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches eve-deploy-debugging from GitHub repository incept5/eve-skillpacks and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
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
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.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 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▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★47 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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