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 cosmicstack-labs/mercury-agent-skills --skill data-pipelinegit clone --depth 1 https://github.com/cosmicstack-labs/mercury-agent-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/cosmicstack-labs/mercury-agent-skills/data-pipeline)<a href="https://agentmods.dev/skills/cosmicstack-labs/mercury-agent-skills/data-pipeline"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/data-pipeline/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/cosmicstack-labs/mercury-agent-skills/data-pipeline"><img src="https://agentmods.dev/badge/skills/cosmicstack-labs/mercury-agent-skills/data-pipeline.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 Data Exfiltration · line 411 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00036 | $0.05500 |
| Opus 5 | $0.00018 | $0.02750 |
| Sonnet 5 | $0.00007 | $0.01100 |
| Haiku 4.5 | $0.00004 | $0.00550 |
Grade A, and why
data-pipeline scanned grade A with 1 finding 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 10d 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
response = requests.get(f"https://api.example.com/orders?date={date}") How it starts
The opening of the file, as written. The whole thing — 717 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Pipeline Design
Core Principles
Data pipelines are the arteries of modern data platforms. A well-designed pipeline is reliable, observable, idempotent, and cost-efficient. The following principles guide every decision:
- Idempotency First — Running a pipeline twice should produce the same result. This enables safe retries and backfills without data duplication.
- Observability by Default — Every stage must emit metrics, logs, and lineage metadata. If you can't see it, you can't fix it.
- Fail Gracefully — Assume failures will happen. Design dead letter queues, retry logic with exponential backoff, and alerting on anomalies.
- Incremental Processing — Process only what's changed. Full refreshes are for schema migrations and backfills only.
- Data Contracts — Define and enforce schemas at every boundary. Catch drift before it reaches downstream consumers.
- Separation of Concerns — Extract, transform, load are distinct phases. Each should be independently testable and debuggable.
- Cost Awareness — Every byte processed costs money. Partition, compress, and prune aggressively.
Pipeline Maturity Model
| Level | Name | Characteristics |
|---|---|---|
| 0 | Ad-hoc | Manual scripts, no scheduling, no error handling, no documentation |
| 1 | Scheduled | Cron-based scheduling, basic retries, simple logging |
| 2 | Monitored | Centralized logging, metrics dashboards, alerts on failure, basic data quality checks |
| 3 | Observable | Full lineage tracking, freshness SLAs, schema validation, data contract enforcement |
| 4 | Self-healing | Automated retry with backoff, dead letter queues, anomaly detection triggers auto-pause |
| 5 | Autonomous | Adaptive pipelines that optimize themselves (auto-partitioning, dynamic resource allocation, intelligent backfilling) |
Target: At minimum Level 3 for production pipelines. Level 4 for critical business 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.
- 10d ago First seen · 717 lines · 36 tokens per session scan A 8c656009730e
data-pipeline is a skill published in the GitHub repository cosmicstack-labs/mercury-agent-skills (471 stars, last pushed 15d ago), licensed MIT. It adds 36 tokens to every session and 5,500 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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