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 data-resiliency-testing-and-failure-injectiongit 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/data-resiliency-testing-and-failure-injection)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/data-resiliency-testing-and-failure-injection"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-resiliency-testing-and-failure-injection/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/data-resiliency-testing-and-failure-injection"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/data-resiliency-testing-and-failure-injection.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 | $0.00054 | $0.00932 |
| Opus 5 | $0.00027 | $0.00466 |
| Sonnet 5 | $0.00011 | $0.00186 |
| Haiku 4.5 | $0.00005 | $0.00093 |
Grade A, and why
data-resiliency-testing-and-failure-injection 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 — 102 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Data Resiliency Testing And Failure Injection
Overview
Use this skill when the goal is to prove that a data system recovers safely under failure, not only when everything goes right. It helps agents design controlled drills for retries, restarts, dependency outages, state recovery, duplicate prevention, backlog catch-up, and publish protection.
When to Use
- hardening a production pipeline before broad rollout
- testing failover, restart, replay, or checkpoint recovery behavior
- validating that retries do not duplicate or corrupt data
- proving recovery objectives for orchestrators, jobs, streams, or warehouse publishes
- converting a past incident into a repeatable resilience drill
Do not treat resilience testing as random breakage. The point is to validate recovery behavior with explicit safety limits and evidence.
Workflow
-
Define the failure modes that matter. Prioritize:
- source outage or delayed upstream delivery
- worker or task restart
- orchestrator retry and timeout behavior
- duplicate event or duplicate file delivery
- checkpoint or incremental-state recovery
- credential, secret, or network dependency failure
- partial publish or downstream unavailability
-
Define the resilience objectives. Include:
- acceptable data loss behavior
- recovery time objective
- replay or backlog catch-up expectation
- duplicate-prevention requirement
- publish block or quarantine behavior
- alert and escalation expectation
-
Choose the safest drill environment. Prefer:
- staging or isolated non-production
- canary datasets or partitions
- synthetic or masked test data
- bounded windows and rollback-ready test scope
-
Inject one failure mode at a time. Use controlled exercises such as:
- killing a task or worker
- pausing an upstream dependency
- delaying input arrival
- replaying a duplicate input
- forcing an expired secret or denied permission in a safe environment
- simulating partial output and validating publish closure
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 · 102 lines · 54 tokens per session scan A a8d5c798d3a9
data-resiliency-testing-and-failure-injection is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (44 stars, last pushed 2mo ago), licensed MIT. It adds 54 tokens to every session and 932 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.
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