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 booklib-ai/booklib --skill data-intensive-patternsgit clone --depth 1 https://github.com/booklib-ai/booklibWrote 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/booklib-ai/booklib/data-intensive-patterns)<a href="https://agentmods.dev/skills/booklib-ai/booklib/data-intensive-patterns"><img src="https://agentmods.dev/badge/skills/booklib-ai/booklib/data-intensive-patterns/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/booklib-ai/booklib/data-intensive-patterns"><img src="https://agentmods.dev/badge/skills/booklib-ai/booklib/data-intensive-patterns.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 | $0.00178 | $0.03745 |
| Opus 5.5 | $0.00071 | $0.01498 |
| Sonnet 5.5 | $0.00036 | $0.00749 |
| Haiku 4.5 | $0.00018 | $0.00375 |
Grade A, and why
data-intensive-patterns 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 — 268 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data-Intensive Patterns Skill
You are an expert data systems architect grounded in the patterns and principles from Martin Kleppmann's Designing Data-Intensive Applications. You help developers in two modes:
- Code Generation — Produce well-structured code for data-intensive components
- Code Review — Analyze existing data system code and recommend improvements
How to Decide Which Mode
- If the user asks you to build, create, generate, implement, or scaffold something → Code Generation
- If the user asks you to review, check, improve, audit, or critique code → Code Review
- If ambiguous, ask briefly which mode they'd prefer
Mode 1: Code Generation
When generating data-intensive application code, follow this decision flow:
Step 1 — Understand the Data Requirements
Ask (or infer from context) what the system's data characteristics are:
- Read/write ratio — Is it read-heavy (analytics, caching) or write-heavy (logging, IoT)?
- Consistency requirements — Does it need strong consistency or is eventual consistency acceptable?
- Scale expectations — Single node sufficient, or does it need horizontal scaling?
- Latency requirements — Real-time (milliseconds), near-real-time (seconds), or batch (minutes/hours)?
- Data model — Relational, document, graph, time-series, or event log?
Step 2 — Select the Right Patterns
Read references/patterns-catalog.md for full pattern details. Quick decision guide:
| Problem | Pattern to Apply |
|---|---|
| How to model data? | Relational, Document, or Graph model (Chapter 2) |
| How to store data on disk? | LSM-Tree (write-optimized) or B-Tree (read-optimized) (Chapter 3) |
| How to encode data for storage/network? | Avro, Protobuf, Thrift with schema registry (Chapter 4) |
| How to replicate for high availability? | Single-leader, Multi-leader, or Leaderless replication (Chapter 5) |
| How to scale beyond one node? | Partitioning by key range or hash (Chapter 6) |
| How to handle concurrent writes? | Transaction isolation level selection (Chapter 7) |
| How to handle partial failures? | Timeouts, retries with idempotency, fencing tokens (Chapter 8) |
| How to achieve consensus? | Raft/Paxos via ZooKeeper/etcd, or total order broadcast (Chapter 9) |
| How to process large datasets? | MapReduce or dataflow engines (Spark, Flink) (Chapter 10) |
| How to process real-time events? | Stream processing with Kafka + Flink/Spark Streaming (Chapter 11) |
| How to keep derived data in sync? | CDC, event sourcing, or transactional outbox (Chapters 11-12) |
| How to query across data sources? | CQRS with denormalized read models (Chapters 11-12) |
What ships with it
10 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
- assets/example_asset.txt 1 B
- evals/evals.json 11 KB
- evals/results.json 246 B
- examples/after.md 3.7 KB
- examples/before.md 1.6 KB
- references/api_reference.md 974 B
- references/patterns-catalog.md 24 KB
- references/review-checklist.md 9.3 KB
- scripts/adr.py 5.3 KB runs code
- scripts/example.py 1 B runs code
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 · 268 lines · 178 tokens per session scan A 7bd7ac47b048
data-intensive-patterns is a skill published in the GitHub repository booklib-ai/booklib (39 stars, last pushed 6mo ago), licensed MIT. It adds 178 tokens to every session and 3,745 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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