production-readiness
Skill224 starsChanged 20 days agoMITView on GitHub
Use at review and ship, automatically for feature or architecture changes and on request for error handling, logging, crash reporting, or observability; verify errors reach production monitoring.
We split this skill into the rules it gives an agent
3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.
0
Common
Most of the 19 edited versions we read keep these.
3
Distinctive
This author's own choices. Worth a look.
0
Very specific
Tied to their project's names, files or tools. Probably skip.
- Common
- Distinctive
- Very specific
- Distinctive: line 32
Document in Work Log where production errors go:
- Distinctive: line 41
If the project has no production logging infrastructure, document as Known Risk:
- Distinctive: line 61
Alert thresholds: When to page on-call — SRE/ops owns this.
“Common” is measured: kept by at least 6 in 10 of the 19 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.
Files
Production Readiness
Overview
Code that passes all tests but logs errors only via debug-only APIs (e.g., debugPrint, console.log in debug mode) is functionally silent in production. This skill enforces observability readiness before ship — ensuring the team is not blind during beta testing and production rollout.
When to Use
- Auto-recommended for
featureandarchitecture-changeclassifications. - Activate manually when working on error handling, crash reporting, or logging infrastructure.
- Apply at
/review(semantic error check) and/ship(readiness checklist).
Observability Checklist
1. Error Surface Audit (at /review)
For every catch / error-handling block in changed files:
| Check | Pass | Fail |
| Logging call exists | Logger.error(), log.error(), crashReporter.capture() | Empty catch {} |
| Logger is production-observable | Framework logger, crash reporter, structured stdout | debugPrint(), print(), debug-only console.log |
| Error context is actionable | Includes error type, operation, identifiers | "error occurred", raw exception only |
2. Log Sink Documentation (at /ship)
Document in Work Log where production errors go:
## Observability
- Error sink: [e.g., Sentry via Logger.error(), Crashlytics, stdout → CloudWatch]
- Health check: [e.g., /health endpoint, Firebase Vitals, uptime monitor]
- Rollback signal: [e.g., error rate > 2x baseline → revert]If the project has no production logging infrastructure, document as Known Risk:
"No production error reporting configured. Errors in catch blocks will be logged to stdout only. Risk: silent failures in release builds if stdout is not monitored."
3. Rollback Telemetry (at /ship)
The rollback plan (per engineering_guardrails.md §12.5) must answer:
- How will operators know the rollback is needed? (alert, dashboard, manual check)
- How will operators know the rollback succeeded? (error rate drops, health check passes)
Heading-Scoped Read Note
For phase-entry loading, read only:
When to UseObservability Checklist
Load What This Skill Does NOT Cover, Anti-Patterns, and Interaction with Other Skills on full read or cache miss only.
What This Skill Does NOT Cover
- Tool selection: Sentry vs Crashlytics vs Datadog — team/project decides.
- Alert thresholds: When to page on-call — SRE/ops owns this.
- Dashboards: What to visualize — team preference.
- Post-deploy monitoring: This is a pre-ship readiness check, not an ops workflow.
Anti-Patterns
| Anti-Pattern | Why It Fails | Fix |
catch (e) { debugPrint(e); } | Tree-shaken in release build | Use production logger |
catch (e) { /* TODO: add logging */ } | Silent catch escapes review | Add logging now, not later |
Logger.error("error") without context | Useless in production triage | Include operation, identifiers, error type |
| Logging to local file only | Not observable in cloud/mobile | Use centralized error reporting |
Interaction with Other Skills
- systematic-debugging: Complements — debugging finds bugs, production-readiness ensures they're visible when they happen again.
- verification-before-completion: Works alongside — verification checks correctness, production-readiness checks observability.
- auth-security: Security errors are especially critical to observe in production. §5.2a applies with elevated priority to auth error paths.
Other versions19 projects use a version of this.
Word for word · 0
None found.
Edited · 1
- KbWen/agent-virtual-office★ 6596% same
Wrote their own version · 18
- trycompai/comp★ 2.1ksame name
- magnus919/agent-skills★ 162same name
- suhailkakar/the-skills★ 26same name
- caiaffa/claude-code-ultimate-engineering-system★ 17same name
- fmind/dot★ 12same name
- fmind/dotfiles★ 12same name
- wpank/ai★ 11same name
- dekoza/oh-my-slop★ 4same name
- Gladiarn/Glad-Personal★ 4same name
- gilsmt/skills★ 2same name
8 more not listed
How matches are found. Word for word: the exact same text. Edited: shares passages with this file (“99% same” means 99% of its five-word phrases also appear here; 10% or more counts). Their own version: similar meaning, or, weakest, the same name. We can’t tell which project had it first. Projects are counted among the 200 most-starred candidates, and mentions are links to the repository we found, up to 50 per site, so both counts are a floor.
Used alongsideProjects with it also use aws, observability and 3 more.
Other files people use alongside this one.
Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
VercelSkillaws
Emulated AWS cloud services (S3, SQS, Cognito, IAM, STS) for local development and testing. Use when the user needs to interact with AWS API endpoints locally, test S3 bucket and object operations, emulate SQS queues and messages, test Cognito sign-up, sign-in, JWTs, or Identity Pools, manage IAM users/roles/access keys, test STS assume role, or work without hitting real AWS APIs. Triggers include "AWS emulator", "emulate AWS", "mock S3", "local SQS", "test IAM", "emulate S3", "AWS locally", "STS assume role", or any task requiring local AWS service emulation.
Also in 3 projects that use this
OpenAISkillobservability
Vercel Observability expert guidance — Drains (logs, traces, speed insights, web analytics), Web Analytics, Speed Insights, runtime logs, custom events, OpenTelemetry integration, and monitoring dashboards. Use when instrumenting, debugging, or optimizing application performance and user experience on Vercel.
Also in 5 projects that use this
AnthropicSkillincident-response
Run an incident response workflow — triage, communicate, and write postmortem. Trigger with "we have an incident", "production is down", an alert that needs severity assessment, a status update mid-incident, or when writing a blameless postmortem after resolution.
Also in 3 projects that use this
NVIDIASkilltesting
Run Python and Rust tests for physicsnemo-curator using uv + pytest with coverage reporting via pytest-cov, and cargo-nextest for Rust tests. Includes benchmark workflows with pytest-benchmark and criterion.
Also in 4 projects that use this
MicrosoftSkillhandoff
Compact the current conversation into a handoff document for another agent to pick up.
Also in 3 projects that use this
In this repository17 other files it gives its agents.
- AGENTS.mdAGENTS.md
- CLAUDE.mdCLAUDE.md
- .github/copilot-instructions.mdCopilot instructions
- llms.txtllms.txt
- api-designSkill
- auth-securitySkill
- database-designSkill
- dispatching-parallel-agentsSkill
- doc-lookupSkill
- frontend-patternsSkill
- karpathy-principlesSkill
- red-team-adversarialSkill
- subagent-driven-developmentSkill
- systematic-debuggingSkill
- test-driven-developmentSkill
- using-git-worktreesSkill
- verification-before-completionSkill
Does it work?
Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.
Reviews can't be read right now.
Your trail
Files you open will collect here, so you can find them again.
Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.
Now do this for your own.
Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

