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 incident-triage-and-pipeline-recoverygit 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/incident-triage-and-pipeline-recovery)<a href="https://agentmods.dev/skills/vaquarkhan/data-engineering-agent-skills/incident-triage-and-pipeline-recovery"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/incident-triage-and-pipeline-recovery/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/incident-triage-and-pipeline-recovery"><img src="https://agentmods.dev/badge/skills/vaquarkhan/data-engineering-agent-skills/incident-triage-and-pipeline-recovery.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 | $0.00049 | $0.00595 |
| Opus 5 | $0.00024 | $0.00298 |
| Sonnet 5 | $0.00010 | $0.00119 |
| Haiku 4.5 | $0.00005 | $0.00060 |
Grade A, and why
incident-triage-and-pipeline-recovery 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 12d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Incident Triage And Pipeline Recovery
Overview
Use this skill when production behavior is already wrong and the team needs controlled recovery. It helps agents contain blast radius, diagnose quickly, restore trust, and avoid making the incident worse.
When to Use
- failed production runs
- bad or partial publishes
- corrupted incremental state
- freshness SLA breaches
- emergency rollback or replay decisions
Do not jump to fixes before stabilizing the system and understanding impact.
Workflow
-
Contain the incident. Decide whether to:
- pause schedules
- block downstream publish
- isolate bad partitions
- notify owners and consumers
-
Pull live signals before mutation when possible. Load
mcp-data-observability-integrationto inspect lag, run state, or Spark stage metrics before reruns or replays. -
Classify impact. Identify:
- affected datasets
- time window
- downstream consumers
- whether data is late, missing, duplicated, or wrong
-
Determine the safest recovery path. Options include:
- rerun
- replay
- rollback
- partial correction
- full backfill
When replay or backfill is chosen, load
safe-backfill-and-replay-orchestrationand drafttemplates/backfill-plan.yamlbefore execution. -
Validate recovery before reopening publish paths.
-
Record the incident and add a guardrail. A fixed incident with no prevention work is unfinished.
-
Turn high-value incidents into repeatable resilience tests. Load
skills/data-resiliency-testing-and-failure-injection/SKILL.mdorreferences/data-resiliency-testing-patterns.mdwhen the same failure mode must be prevented from surprising the team again.
Common Rationalizations
| Rationalization | Reality |
|---|---|
| "Let us rerun everything now." | Blind reruns can duplicate data or destroy evidence. |
| "We can clean up downstream later." | Downstream trust loss is often harder to recover than the pipeline itself. |
| "The root cause can wait." | Without a guardrail, the same incident often returns quickly. |
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.
- 12d ago First seen · 80 lines · 49 tokens per session scan A beac27f2c5d7
incident-triage-and-pipeline-recovery is a skill published in the GitHub repository vaquarkhan/data-engineering-agent-skills (45 stars, last pushed 3mo ago), licensed MIT. It adds 49 tokens to every session and 595 once invoked, about $0.0002 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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