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 cnfeat/top-pm-skills --skill data-pipeline-specgit clone --depth 1 https://github.com/cnfeat/top-pm-skillsWrote 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/cnfeat/top-pm-skills/data-pipeline-spec)<a href="https://agentmods.dev/skills/cnfeat/top-pm-skills/data-pipeline-spec"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/data-pipeline-spec/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/cnfeat/top-pm-skills/data-pipeline-spec"><img src="https://agentmods.dev/badge/skills/cnfeat/top-pm-skills/data-pipeline-spec.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 | $0.00070 | $0.02478 |
| Opus 5 | $0.00035 | $0.01239 |
| Sonnet 5 | $0.00014 | $0.00496 |
| Haiku 4.5 | $0.00007 | $0.00248 |
Grade A, and why
data-pipeline-spec 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 8d 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Spec Skill
This skill produces a complete data pipeline specification covering sources, transformations, destinations, scheduling, SLAs, error handling, data quality checks, and monitoring requirements. Output is ready for engineering handoff or architecture review.
Required Inputs
Ask the user for these if not provided:
- Pipeline purpose — what business question or workflow does this pipeline serve?
- Source systems — where does data come from? (databases, APIs, files, event streams)
- Destination — where does data land? (data warehouse, data lake, downstream DB, reporting tool)
- Transformation type — ETL (transform before loading) or ELT (load raw, transform in warehouse)?
- Frequency / SLA — how often must data be fresh? (real-time / hourly / daily / weekly)
- Volume estimate — approximate rows/events per run
- Data quality requirements — completeness, deduplication, freshness, schema enforcement
- Team or stack — any specific tools in use? (Airflow, dbt, Fivetran, Spark, Kafka, etc.)
Output Structure
Data Pipeline Spec: [Pipeline Name]
Purpose: [One sentence — what decision or workflow does this pipeline enable?] Type: [ETL / ELT / Streaming / Batch] Owner: [Team or individual] Version: [1.0] Date: [Date] Status: [Draft / Under Review / Approved]
1. Overview
[2–3 sentences describing the pipeline end-to-end: what data moves, from where to where, at what cadence, and why.]
Architecture diagram (text):
[Source A] ──┐
[Source B] ──┤──► [Ingestion Layer] ──► [Transform Layer] ──► [Destination] ──► [Consumers]
[Source C] ──┘
2. Sources
| Source | System | Connection type | Data format | Update pattern | Volume |
|---|---|---|---|---|---|
| [Source 1] | [PostgreSQL / Salesforce / S3 / Kafka] | [JDBC / REST API / SDK / Webhook] | [JSON / CSV / Parquet / CDC] | [Append / Full refresh / Incremental] | [X rows/day] |
| [Source 2] | [...] | [...] | [...] | [...] | [...] |
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.
- 8d ago First seen · 230 lines · 70 tokens per session scan A ece0fcd73575
data-pipeline-spec is a skill published in the GitHub repository cnfeat/top-pm-skills (48 stars, last pushed 3mo ago), licensed MIT. It adds 70 tokens to every session and 2,478 once invoked, about $0.0003 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 2026-09-03.
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