production-awareness — independently scanned and version-tracked by SaferSkills.
SaferSkills independently audited production-awareness (Agent Skill) and scored it 100/100 (green). The audit ran 55 deterministic rules across Security, Supply Chain, Maintenance, Transparency, and Community; it found 0 high-severity and 0 lower-severity findings. The full rule-by-rule trace and per-finding evidence are below. Free, methodology-open.
Findings & checks · 0 flagged
Every scanned point with the score it earned and what moved between them.
First recorded scan — no prior version to compare against.
The primary manifest — the file an agent reads to learn what this artifact does.
An agent that considers how code will behave in production - under real load, with real data, facing real failures - while writing it, not after shipping it. Without this skill, agents write code that works in development but fails under production conditions: high concurrency, network failures, malformed data, resource exhaustion, and hostile input.
While writing any code, ask these questions:
| Question | Why It Matters |
|---|---|
| What happens at 100x current load? | Linear algorithms become bottlenecks; database connections exhaust |
| What happens when this external call fails? | Network timeouts, retries, and circuit breakers become essential |
| What happens with unexpected input? | User-generated content includes Unicode, injection attempts, and extreme lengths |
| What happens when disk/memory fills up? | Unbounded logs, caches, and queues cause cascading failures |
| What happens during a deploy? | In-flight requests, database migrations, and cache invalidation need handling |
Every external dependency will eventually fail. Design for it:
Network calls: Always have a timeout, retry policy, and fallback behavior
GOOD: fetch(url, { signal: AbortSignal.timeout(5000) })
.catch(err => fallbackResponse)
BAD: fetch(url) // hangs forever if the server doesn't respondDatabase queries: Handle connection pool exhaustion, slow queries, and deadlocks
File system: Handle disk full, permission denied, and race conditions
ENOENT and EACCES explicitlyTreat production data with care at every level:
Know the resource profile of your code:
| Resource | Production Concern |
|---|---|
| Memory | Unbounded caches, large object allocations in loops, memory leaks from unclosed resources |
| CPU | Synchronous heavy computation blocking the event loop, regex backtracking, JSON parsing of huge payloads |
| Connections | Database connection pool exhaustion, HTTP keep-alive limits, socket leaks |
| Disk | Log rotation, temp file cleanup, upload storage limits |
| External rate limits | Third-party API quotas, email sending limits, OAuth token refresh rates |
While writing code, consider operational needs:
Design systems that bend instead of break:
| Failure | Symptom | Correction |
|---|---|---|
| "Works on my machine" thinking | Code fails under production load, data volume, or network conditions | Test with realistic data sizes and failure scenarios |
| No timeouts on external calls | Requests hang indefinitely; thread/connection pool exhausts | Add timeouts to every network call, database query, and file operation |
| Logging sensitive data | Compliance violation discovered in production logs | Audit every log statement for PII, tokens, and credentials |
| Unbounded resource usage | Memory leaks, disk fills up, connection pools exhaust | Set explicit limits on caches, queues, pools, and temporary storage |
| No fallback behavior | One dependency failure cascades into full system outage | Implement graceful degradation for every external dependency |
~30 seconds. Free. No account. Every finding cites a rule and a line of evidence.