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 vaquarkhan/data-engineering-agent-skills --skill api-and-saas-ingestion-patternsgit clone --depth 1 https://github.com/vaquarkhan/data-engineering-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/vaquarkhan/data-engineering-agent-skills/api-and-saas-ingestion-patterns)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/api-and-saas-ingestion-patterns"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/api-and-saas-ingestion-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/vaquarkhan/data-engineering-agent-skills/api-and-saas-ingestion-patterns"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/api-and-saas-ingestion-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 | $0.00051 | $0.00525 |
| Opus 5 | $0.00026 | $0.00262 |
| Sonnet 5 | $0.00010 | $0.00105 |
| Haiku 4.5 | $0.00005 | $0.00052 |
Grade A, and why
api-and-saas-ingestion-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 11d 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 — 74 lines — stays where its author put it; the contents beside it link to each section on GitHub.
API And SaaS Ingestion Patterns
Overview
Use this skill when the source system is an external API or SaaS platform rather than a database or file drop. It helps agents design reliable extraction, pagination, throttling, auth handling, and backfill-safe ingestion contracts.
When to Use
- extracting from REST or GraphQL APIs
- ingesting SaaS platform data
- handling pagination, cursors, or sync tokens
- dealing with rate limits and source-side throttling
- backfilling historical API data safely
Do not treat APIs like static tables. They change behavior, availability, and limits over time.
Workflow
-
Define the source contract. Include:
- endpoint or object name
- auth method
- extraction window
- pagination style
- rate limits
- data freshness expectations
-
Design for extraction resilience. Handle:
- retries
- backoff
- token refresh
- idempotent re-fetch behavior
- partial page failure
-
Make incremental behavior explicit. Decide whether the source supports:
- updated timestamps
- cursors
- change tokens
- full snapshots only
-
Record raw source evidence where useful. API sources often need raw response retention for incident diagnosis.
-
Validate contracts against source drift.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "We can just loop through pages." | Pagination bugs and retry gaps often cause silent data loss. |
| "The vendor API is stable enough." | SaaS APIs change rate limits, fields, and semantics more often than teams expect. |
| "If a request fails, we can rerun later." | Without windowing and idempotency rules, reruns can miss or duplicate data. |
Red Flags
- no rate-limit strategy exists
- extraction windows depend on undocumented source behavior
- retries ignore duplicate or partial-page risks
- auth rotation and token expiry are not considered
Verification
- The source contract covers pagination, limits, auth, and cadence
- Extraction retries and failures are operationally safe
- Incremental or snapshot behavior is explicit
- Source drift and raw evidence handling are considered
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.
- 11d ago First seen · 74 lines · 51 tokens per session scan A 45329e33e6a4
api-and-saas-ingestion-patterns is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 51 tokens to every session and 525 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 2026-08-30.
Other skills, from other repositories
kafka-shadowtraffic
Generate a ShadowTraffic configuration to populate a Kafka topic with realistic synthetic data. Discovers the target topic, its key and value schemas, and the correct serializers from the live cluster via any attached Kafka MCP server, then writes a ready-to-run shadowtraffic-config.json and Docker command. Use when…
claude-api
Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports anthropic/@anthropic-ai/sdk; user asks for…
migrating-ai-sdk-to-common-ai
Migrates Airflow projects from airflow-ai-sdk to apache-airflow-providers-common-ai 0.4.0+. Use when replacing airflow-ai-sdk with the official Airflow AI provider - migrating LLM decorators (@task.llm, @task.agent, @task.llmbranch, @task.embed), switching from model strings/objects to connection-based LLM…
creating-openlineage-extractors
Create custom OpenLineage extractors for Airflow operators. Use when the user needs lineage from unsupported or third-party operators, wants column-level lineage, or needs complex extraction logic beyond what inlets/outlets provide.
telnyx-ai-inference-curl
Access Telnyx LLM inference APIs, embeddings, and AI analytics for call insights and summaries. This skill provides REST API (curl) examples.
kafka-dlq-review
Review dead letter queue implementations for completeness using the Lenses MCP server. Checks DLQ topic existence, configuration, monitoring, metadata preservation, retry logic, reprocessing paths and connector DLQ alignment. Use when user says "review dead letter queues", "check DLQ setup", "DLQ audit" or asks about…