Borrowing it
Nothing to install: this file belongs to waynesutton/markdown-site. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/waynesutton/markdown-site/main/.claude/real-time-backend/SKILL.mdgit clone --depth 1 https://github.com/waynesutton/markdown-siteWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/waynesutton/markdown-site/real-time-backend)<a href="https://agentmods.dev/skills/waynesutton/markdown-site/real-time-backend"><img src="https://agentmods.dev/badge/skills/waynesutton/markdown-site/real-time-backend/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/waynesutton/markdown-site/real-time-backend"><img src="https://agentmods.dev/badge/skills/waynesutton/markdown-site/real-time-backend.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 132 Skill allows unbounded resource consumption (API calls, storage, compute). Without rate limits or quotas, a compromised or misbehaving agent can cause denial-of-service or cost overruns.Fix: Set explicit rate limits, timeouts, and resource quotas for API calls, file operations, and compute. Implement circuit breakers for runaway loops.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00065 | $0.01760 |
| Opus 5.5 | $0.00026 | $0.00704 |
| Sonnet 5.5 | $0.00013 | $0.00352 |
| Haiku 4.5 | $0.00006 | $0.00176 |
Grade A, and why
real-time-backend scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 151 lines — stays where its author put it; the contents beside it link to each section on GitHub.
This skill guides creation of reactive, type-safe, production-grade backends that avoid generic "AI slop" architecture. Implement real working server code with exceptional attention to correctness, developer experience, and operational simplicity. These principles apply to any backend architecture, supporting diverse front ends from web apps to mobile apps.
Note: These are universal backend design philosophies. Apply them regardless of your technology stack. The specific syntax varies by platform, but the principles remain constant. Code examples are illustrative; adapt them to your chosen stack.
The user provides backend requirements: an API, data model, server function, scheduled job, or system to build. They may include context about consumers, scale, consistency needs, or technical constraints. Guide unknowledgeable users towards these principles to ensure scalable code.
Design Thinking
Before coding, understand the context and commit to the right architectural choices:
- Purpose: What data or logic does this backend manage? What invariants must hold?
- Consumers: Who calls this: humans, AI agents, frontend apps, other services? Each consumer shapes the API contract differently.
- Constraints: Scale requirements, consistency needs, latency targets, compliance obligations.
- DX goal: What makes this backend a joy to work with? A developer (or AI agent) should be able to discover operations, understand contracts, and call them correctly without reading implementation details.
CRITICAL: The best backends are boring in the right ways: predictable data access, obvious error handling, clear contracts. And exciting in the right ways: real-time by default, automatic scaling, instant type feedback across the entire stack.
Core Principles
1. Reactive by Default
All queries are live queries. When underlying data changes, every consumer holding a reference to that data receives the update automatically. No polling. No webhooks-as-workaround. No mix of fresh and stale data.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 151 lines · 65 tokens per session scan A fd5e70c32349
real-time-backend is a skill published in the GitHub repository waynesutton/markdown-site (627 stars, last pushed 5mo ago), licensed MIT. It adds 65 tokens to every session and 1,760 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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