Install
$ agentstack add skill-vaquarkhan-data-engineering-agent-skills-api-and-saas-ingestion-patterns ✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
Verified badge
Passed review? Show it. Paste this badge into your README, it links to the public security report.
Reliability & compatibility
Declared compatibility
Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.
We're building live execution health for every listing: tool-call success rate, median latency, uptime, and last-checked timestamps, measured, not self-reported. It isn't live yet, so we don't show numbers we can't stand behind.
How agent discovery & health will work →About
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
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: vaquarkhan
- Source: vaquarkhan/data-engineering-agent-skills
- License: MIT
- Homepage: https://vaquarkhan.github.io/data-engineering-agent-skills/
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.