Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add luoling8192/ai-coding-principles --skill ddia-principlesgit clone --depth 1 https://github.com/luoling8192/ai-coding-principlesWrote 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/luoling8192/ai-coding-principles/ddia-principles)<a href="https://agentmods.dev/skills/luoling8192/ai-coding-principles/ddia-principles"><img src="https://agentmods.dev/badge/skills/luoling8192/ai-coding-principles/ddia-principles/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/luoling8192/ai-coding-principles/ddia-principles"><img src="https://agentmods.dev/badge/skills/luoling8192/ai-coding-principles/ddia-principles.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00185 | $0.04138 |
| Opus 5.5 | $0.00074 | $0.01655 |
| Sonnet 5.5 | $0.00037 | $0.00828 |
| Haiku 4.5 | $0.00018 | $0.00414 |
Grade A, and why
ddia-principles 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ddia-principles — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 422 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Designing Data-Intensive Applications — Distilled Guide
Source: Martin Kleppmann, Designing Data-Intensive Applications Central thesis: Data is the core challenge of modern applications — not compute.
Part I: Foundations of Data Systems
Chapter 1: Reliability, Scalability, Maintainability
Three Pillars
| Pillar | Definition | Key Metric |
|---|---|---|
| Reliability | System works correctly even when faults occur | Fault ≠ Failure; tolerate faults, prevent failures |
| Scalability | System handles load growth gracefully | Measure with percentiles: p50, p95, p99, p999 |
| Maintainability | System is easy to operate, understand, evolve | Operability + Simplicity + Evolvability |
Fault Categories
- Hardware: Random, independent (disk, RAM, power). Mitigate with redundancy (RAID, dual power).
- Software: Systematic bugs affecting all nodes simultaneously (leap-second bug). Mitigate with process isolation, monitoring, chaos engineering.
- Human: #1 cause of outages (config errors). Mitigate with good abstractions, sandboxes, canary deployments, fast rollback.
Scalability Patterns
- Vertical (scale-up): Bigger machine. Simple but has ceiling.
- Horizontal (scale-out): More machines (shared-nothing). Complex but unlimited.
- Elastic: Auto-scale on load detection. Good for unpredictable workloads.
Twitter fan-out case study: 4.6k writes/s but 300k reads/s. Solution: pre-compute timelines (write fan-out) for most users; read-time merge for celebrities.
Performance: Use Percentiles, Not Averages
- p50 = median. p99 = tail latency matters for user experience.
- Amazon: 100ms delay = 1% revenue loss.
- Tail latency amplification: One slow backend call slows entire parallel request.
Chapter 2: Data Models & Query Languages
Model Selection Guide
| Model | Best For | Weakness |
|---|---|---|
| Relational | Structured data, complex joins, ACID transactions | Rigid schema, impedance mismatch with OOP |
| Document | Hierarchical data, flexible schema, data locality | Poor joins, many-to-many relationships |
| Graph | Highly connected data, variable-depth traversals | Less mature tooling, harder to partition |
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 · 422 lines · 185 tokens per session scan A 3360b207b193
ddia-principles is a skill published in the GitHub repository luoling8192/ai-coding-principles (171 stars, last pushed 7mo ago), licensed MIT. It adds 185 tokens to every session and 4,138 once invoked, about $0.0007 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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