Incident Triage And Pipeline Recovery

Community

vaquarkhan

incident-triage-and-pipeline-recovery

Guides agents through production data incidents. Use when a pipeline fails, publishes bad data, misses an SLA, partially loads, corrupts state, or requires rollback, replay, or stakeholder communication.

Overview

Publisher	vaquarkhan
Repository	`data-engineering-agent-skills`
Skill name	`incident-triage-and-pipeline-recovery`
Stars	60
Forks	8
Bundled files	Instructions only
License	MIT
Links	Skill source Repository Homepage

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
Self-contained
Everything the model needs lives in the instructions — no extra files to sync.
Open source
Published by vaquarkhan on GitHub. Read the source before you install it.

Installation

Install the Incident Triage And Pipeline Recovery AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/incident-triage-and-pipeline-recovery
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/incident-triage-and-pipeline-recovery

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/vaquarkhan/data-engineering-agent-skills.git /tmp/data-engineering-agent-skills
mkdir -p .claude/skills
cp -r /tmp/data-engineering-agent-skills/skills/incident-triage-and-pipeline-recovery .claude/skills/incident-triage-and-pipeline-recovery

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Incident Triage And Pipeline Recovery in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Incident Triage And Pipeline Recovery on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Incident Triage And Pipeline Recovery is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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-integration to 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-orchestration and draft templates/backfill-plan.yaml before 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.md or references/data-resiliency-testing-patterns.md when 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.

Red Flags

no attempt to contain blast radius
reruns happen before impact is understood
downstream consumers are not informed
recovery succeeds but no new guardrail is added

Verification

Impact scope and affected consumers are identified
The chosen recovery path is safer than the alternatives
Publish is reopened only after validation
Post-incident guardrails or checks are added

Frequently asked questions

What does the Incident Triage And Pipeline Recovery AI skill do?

Why use Incident Triage And Pipeline Recovery on TypingMind?

Because you install it once and use it with any model. Incident Triage And Pipeline Recovery is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Incident Triage And Pipeline Recovery in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vaquarkhan/data-engineering-agent-skills/tree/main/skills/incident-triage-and-pipeline-recovery. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Incident Triage And Pipeline Recovery?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Incident Triage And Pipeline Recovery?

As many as you like. As long as a model supports skills, you can use Incident Triage And Pipeline Recovery with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Incident Triage And Pipeline Recovery AI skill free?

Yes. It is published on GitHub by vaquarkhan under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Grok

NousResearch

OrganizationPopular

Delegate coding to xAI Grok Build CLI (features, PRs).

Setup Wizard Generator

NousResearch

OrganizationPopular

Generate a bash wizard guiding a human through manual setup.

Mcp Oauth Remote Gateway

NousResearch

OrganizationPopular

Manual OAuth for remote MCP servers on headless gateways.

Serving Llms Vllm

vLLM: high-throughput LLM serving, OpenAI API, quantization.

Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Incident Triage And Pipeline Recovery

Overview

When to Use

Workflow

Common Rationalizations

Red Flags

Verification

Frequently asked questions

Related AI Skills

Set up your own AI workspace now