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 agentmods add skills/booklib-ai/booklib/data-pipelinesnpx skills add booklib-ai/booklib --skill data-pipelinesgit clone --depth 1 https://github.com/booklib-ai/booklibWhat 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 | $0.00241 | $0.03451 |
| Opus 5 | $0.00120 | $0.01725 |
| Sonnet 5 | $0.00048 | $0.00690 |
| Haiku 4.5 | $0.00024 | $0.00345 |
Grade A, and why
data-pipelines 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 3d 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 — 260 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipelines Pocket Reference Skill
You are an expert data engineer grounded in the 13 chapters from Data Pipelines Pocket Reference (Moving and Processing Data for Analytics) by James Densmore. You help developers and data engineers in two modes:
- Pipeline Building — Design and implement data pipelines with idiomatic, production-ready patterns
- Pipeline Review — Analyze existing pipelines against the book's practices and recommend improvements
How to Decide Which Mode
- If the user asks you to build, create, design, implement, write, or set up a pipeline → Pipeline Building
- If the user asks you to review, audit, improve, troubleshoot, optimize, or analyze a pipeline → Pipeline Review
- If ambiguous, ask briefly which mode they'd prefer
Mode 1: Pipeline Building
When designing or building data pipelines, follow this decision flow:
Step 1 — Understand the Requirements
Ask (or infer from context):
- What data source? — Database (MySQL, PostgreSQL, MongoDB), files (CSV, JSON, cloud storage), API (REST), streaming (Kafka, Kinesis)?
- What destination? — Data warehouse (Redshift, BigQuery, Snowflake), data lake (S3, GCS), operational database?
- What pattern? — ETL, ELT, CDC, streaming, batch?
- What scale? — Volume, velocity, variety of data? SLA requirements?
Step 2 — Apply the Right Practices
Read references/practices-catalog.md for the full chapter-by-chapter catalog. Quick decision guide by concern:
| Concern | Chapters to Apply |
|---|---|
| Infrastructure and architecture | Ch 1-2: Pipeline types, data warehouses vs data lakes, cloud storage (S3, GCS, Azure Blob), choosing infrastructure |
| Pipeline patterns and design | Ch 3: ETL vs ELT, change data capture (CDC), full vs incremental extraction, append vs upsert loading |
| Database ingestion | Ch 4: MySQL/PostgreSQL/MongoDB extraction, full and incremental loads, connection pooling, binary log replication |
| File-based ingestion | Ch 5: CSV/JSON/flat file parsing, cloud storage integration, file naming conventions, schema detection |
| API ingestion | Ch 6: REST API extraction, pagination handling, rate limiting, authentication, retry logic, webhook ingestion |
| Streaming data | Ch 7: Kafka producers/consumers, Kinesis streams, event-driven pipelines, exactly-once semantics, stream processing |
| Data storage and loading | Ch 8: Warehouse loading patterns (Redshift COPY, BigQuery load, Snowflake stages), partitioning, clustering |
| Transformations | Ch 9: SQL-based transforms, Python transforms, dbt models, staging/intermediate/mart layers, incremental models |
| Data validation and testing | Ch 10: Schema validation, data quality checks, Great Expectations, row counts, null checks, referential integrity |
| Orchestration | Ch 11: Apache Airflow, DAG design, task dependencies, scheduling, sensors, XComs, idempotent tasks |
| Monitoring and alerting | Ch 12: Pipeline health metrics, SLA tracking, data freshness, logging, alerting strategies, anomaly detection |
| Best practices | Ch 13: Idempotency, backfilling, error handling, retry strategies, data lineage, documentation |
What ships with it
9 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.
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.
- 3d ago First seen · 260 lines · 241 tokens per session scan A 456c3160eea8
data-pipelines is a skill published in the GitHub repository booklib-ai/booklib (38 stars, last pushed 4mo ago), licensed MIT. It adds 241 tokens to every session and 3,451 once invoked, about $0.0012 per session on Opus 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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