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On Call Handoff Patterns

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Dicklesworthstone
on-call-handoff-patterns

Master on-call shift handoffs with context transfer, escalation procedures, and documentation. Use when transitioning on-call responsibilities, documenting shift summaries, or improving on-call processes.

Overview

PublisherDicklesworthstone
Repositorypi_agent_rust
Skill nameon-call-handoff-patterns
Stars
1.9K
Forks
220
Bundled files
Instructions only
Links
  • 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 Dicklesworthstone on GitHub. Read the source before you install it.

Installation

Install the On Call Handoff Patterns AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/Dicklesworthstone/pi_agent_rust/tree/main/tests/ext_conformance/artifacts/agents-wshobson/incident-response/skills/on-call-handoff-patterns
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

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/Dicklesworthstone/pi_agent_rust.git /tmp/pi_agent_rust
mkdir -p .claude/skills
cp -r /tmp/pi_agent_rust/tests/ext_conformance/artifacts/agents-wshobson/incident-response/skills/on-call-handoff-patterns .claude/skills/on-call-handoff-patterns
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable On Call Handoff Patterns 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 On Call Handoff Patterns 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 On Call Handoff Patterns 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.

On-Call Handoff Patterns

Effective patterns for on-call shift transitions, ensuring continuity, context transfer, and reliable incident response across shifts.

When to Use This Skill

  • Transitioning on-call responsibilities
  • Writing shift handoff summaries
  • Documenting ongoing investigations
  • Establishing on-call rotation procedures
  • Improving handoff quality
  • Onboarding new on-call engineers

Core Concepts

1. Handoff Components

ComponentPurpose
Active IncidentsWhat's currently broken
Ongoing InvestigationsIssues being debugged
Recent ChangesDeployments, configs
Known IssuesWorkarounds in place
Upcoming EventsMaintenance, releases

2. Handoff Timing

Recommended: 30 min overlap between shifts

Outgoing:
├── 15 min: Write handoff document
└── 15 min: Sync call with incoming

Incoming:
├── 15 min: Review handoff document
├── 15 min: Sync call with outgoing
└── 5 min: Verify alerting setup

Templates

Template 1: Shift Handoff Document

markdown
# On-Call Handoff: Platform Team

**Outgoing**: @alice (2024-01-15 to 2024-01-22)
**Incoming**: @bob (2024-01-22 to 2024-01-29)
**Handoff Time**: 2024-01-22 09:00 UTC

---

## 🔴 Active Incidents

### None currently active

No active incidents at handoff time.

---

## 🟡 Ongoing Investigations

### 1. Intermittent API Timeouts (ENG-1234)

**Status**: Investigating
**Started**: 2024-01-20
**Impact**: ~0.1% of requests timing out

**Context**:

- Timeouts correlate with database backup window (02:00-03:00 UTC)
- Suspect backup process causing lock contention
- Added extra logging in PR #567 (deployed 01/21)

**Next Steps**:

- [ ] Review new logs after tonight's backup
- [ ] Consider moving backup window if confirmed

**Resources**:

- Dashboard: [API Latency](https://grafana/d/api-latency)
- Thread: #platform-eng (01/20, 14:32)

---

### 2. Memory Growth in Auth Service (ENG-1235)

**Status**: Monitoring
**Started**: 2024-01-18
**Impact**: None yet (proactive)

**Context**:

- Memory usage growing ~5% per day
- No memory leak found in profiling
- Suspect connection pool not releasing properly

**Next Steps**:

- [ ] Review heap dump from 01/21
- [ ] Consider restart if usage > 80%

**Resources**:

- Dashboard: [Auth Service Memory](https://grafana/d/auth-memory)
- Analysis doc: [Memory Investigation](https://docs/eng-1235)

---

## 🟢 Resolved This Shift

### Payment Service Outage (2024-01-19)

- **Duration**: 23 minutes
- **Root Cause**: Database connection exhaustion
- **Resolution**: Rolled back v2.3.4, increased pool size
- **Postmortem**: [POSTMORTEM-89](https://docs/postmortem-89)
- **Follow-up tickets**: ENG-1230, ENG-1231

---

## 📋 Recent Changes

### Deployments

| Service      | Version | Time        | Notes                      |
| ------------ | ------- | ----------- | -------------------------- |
| api-gateway  | v3.2.1  | 01/21 14:00 | Bug fix for header parsing |
| user-service | v2.8.0  | 01/20 10:00 | New profile features       |
| auth-service | v4.1.2  | 01/19 16:00 | Security patch             |

### Configuration Changes

- 01/21: Increased API rate limit from 1000 to 1500 RPS
- 01/20: Updated database connection pool max from 50 to 75

### Infrastructure

- 01/20: Added 2 nodes to Kubernetes cluster
- 01/19: Upgraded Redis from 6.2 to 7.0

---

## ⚠️ Known Issues & Workarounds

### 1. Slow Dashboard Loading

**Issue**: Grafana dashboards slow on Monday mornings
**Workaround**: Wait 5 min after 08:00 UTC for cache warm-up
**Ticket**: OPS-456 (P3)

### 2. Flaky Integration Test

**Issue**: `test_payment_flow` fails intermittently in CI
**Workaround**: Re-run failed job (usually passes on retry)
**Ticket**: ENG-1200 (P2)

---

## 📅 Upcoming Events

| Date        | Event                | Impact              | Contact       |
| ----------- | -------------------- | ------------------- | ------------- |
| 01/23 02:00 | Database maintenance | 5 min read-only     | @dba-team     |
| 01/24 14:00 | Major release v5.0   | Monitor closely     | @release-team |
| 01/25       | Marketing campaign   | 2x traffic expected | @platform     |

---

## 📞 Escalation Reminders

| Issue Type      | First Escalation     | Second Escalation |
| --------------- | -------------------- | ----------------- |
| Payment issues  | @payments-oncall     | @payments-manager |
| Auth issues     | @auth-oncall         | @security-team    |
| Database issues | @dba-team            | @infra-manager    |
| Unknown/severe  | @engineering-manager | @vp-engineering   |

---

## 🔧 Quick Reference

### Common Commands

```bash
# Check service health
kubectl get pods -A | grep -v Running

# Recent deployments
kubectl get events --sort-by='.lastTimestamp' | tail -20

# Database connections
psql -c "SELECT count(*) FROM pg_stat_activity;"

# Clear cache (emergency only)
redis-cli FLUSHDB
```

Important Links


Handoff Checklist

Outgoing Engineer

  • Document active incidents
  • Document ongoing investigations
  • List recent changes
  • Note known issues
  • Add upcoming events
  • Sync with incoming engineer

Incoming Engineer

  • Read this document
  • Join sync call
  • Verify PagerDuty is routing to you
  • Verify Slack notifications working
  • Check VPN/access working
  • Review critical dashboards

### Template 2: Quick Handoff (Async)

```markdown
# Quick Handoff: @alice → @bob

## TL;DR
- No active incidents
- 1 investigation ongoing (API timeouts, see ENG-1234)
- Major release tomorrow (01/24) - be ready for issues

## Watch List
1. API latency around 02:00-03:00 UTC (backup window)
2. Auth service memory (restart if > 80%)

## Recent
- Deployed api-gateway v3.2.1 yesterday (stable)
- Increased rate limits to 1500 RPS

## Coming Up
- 01/23 02:00 - DB maintenance (5 min read-only)
- 01/24 14:00 - v5.0 release

## Questions?
I'll be available on Slack until 17:00 today.

Template 3: Incident Handoff (Mid-Incident)

markdown
# INCIDENT HANDOFF: Payment Service Degradation

**Incident Start**: 2024-01-22 08:15 UTC
**Current Status**: Mitigating
**Severity**: SEV2

---

## Current State

- Error rate: 15% (down from 40%)
- Mitigation in progress: scaling up pods
- ETA to resolution: ~30 min

## What We Know

1. Root cause: Memory pressure on payment-service pods
2. Triggered by: Unusual traffic spike (3x normal)
3. Contributing: Inefficient query in checkout flow

## What We've Done

- Scaled payment-service from 5 → 15 pods
- Enabled rate limiting on checkout endpoint
- Disabled non-critical features

## What Needs to Happen

1. Monitor error rate - should reach <1% in ~15 min
2. If not improving, escalate to @payments-manager
3. Once stable, begin root cause investigation

## Key People

- Incident Commander: @alice (handing off)
- Comms Lead: @charlie
- Technical Lead: @bob (incoming)

## Communication

- Status page: Updated at 08:45
- Customer support: Notified
- Exec team: Aware

## Resources

- Incident channel: #inc-20240122-payment
- Dashboard: [Payment Service](https://grafana/d/payments)
- Runbook: [Payment Degradation](https://wiki/runbooks/payments)

---

**Incoming on-call (@bob) - Please confirm you have:**

- [ ] Joined #inc-20240122-payment
- [ ] Access to dashboards
- [ ] Understand current state
- [ ] Know escalation path

Handoff Sync Meeting

Agenda (15 minutes)

markdown
## Handoff Sync: @alice → @bob

1. **Active Issues** (5 min)
   - Walk through any ongoing incidents
   - Discuss investigation status
   - Transfer context and theories

2. **Recent Changes** (3 min)
   - Deployments to watch
   - Config changes
   - Known regressions

3. **Upcoming Events** (3 min)
   - Maintenance windows
   - Expected traffic changes
   - Releases planned

4. **Questions** (4 min)
   - Clarify anything unclear
   - Confirm access and alerting
   - Exchange contact info

On-Call Best Practices

Before Your Shift

markdown
## Pre-Shift Checklist

### Access Verification

- [ ] VPN working
- [ ] kubectl access to all clusters
- [ ] Database read access
- [ ] Log aggregator access (Splunk/Datadog)
- [ ] PagerDuty app installed and logged in

### Alerting Setup

- [ ] PagerDuty schedule shows you as primary
- [ ] Phone notifications enabled
- [ ] Slack notifications for incident channels
- [ ] Test alert received and acknowledged

### Knowledge Refresh

- [ ] Review recent incidents (past 2 weeks)
- [ ] Check service changelog
- [ ] Skim critical runbooks
- [ ] Know escalation contacts

### Environment Ready

- [ ] Laptop charged and accessible
- [ ] Phone charged
- [ ] Quiet space available for calls
- [ ] Secondary contact identified (if traveling)

During Your Shift

markdown
## Daily On-Call Routine

### Morning (start of day)

- [ ] Check overnight alerts
- [ ] Review dashboards for anomalies
- [ ] Check for any P0/P1 tickets created
- [ ] Skim incident channels for context

### Throughout Day

- [ ] Respond to alerts within SLA
- [ ] Document investigation progress
- [ ] Update team on significant issues
- [ ] Triage incoming pages

### End of Day

- [ ] Hand off any active issues
- [ ] Update investigation docs
- [ ] Note anything for next shift

After Your Shift

markdown
## Post-Shift Checklist

- [ ] Complete handoff document
- [ ] Sync with incoming on-call
- [ ] Verify PagerDuty routing changed
- [ ] Close/update investigation tickets
- [ ] File postmortems for any incidents
- [ ] Take time off if shift was stressful

Escalation Guidelines

When to Escalate

markdown
## Escalation Triggers

### Immediate Escalation

- SEV1 incident declared
- Data breach suspected
- Unable to diagnose within 30 min
- Customer or legal escalation received

### Consider Escalation

- Issue spans multiple teams
- Requires expertise you don't have
- Business impact exceeds threshold
- You're uncertain about next steps

### How to Escalate

1. Page the appropriate escalation path
2. Provide brief context in Slack
3. Stay engaged until escalation acknowledges
4. Hand off cleanly, don't just disappear

Best Practices

Do's

  • Document everything - Future you will thank you
  • Escalate early - Better safe than sorry
  • Take breaks - Alert fatigue is real
  • Keep handoffs synchronous - Async loses context
  • Test your setup - Before incidents, not during

Don'ts

  • Don't skip handoffs - Context loss causes incidents
  • Don't hero - Escalate when needed
  • Don't ignore alerts - Even if they seem minor
  • Don't work sick - Swap shifts instead
  • Don't disappear - Stay reachable during shift

Resources

Frequently asked questions

What does the On Call Handoff Patterns AI skill do?

Master on-call shift handoffs with context transfer, escalation procedures, and documentation. Use when transitioning on-call responsibilities, documenting shift summaries, or improving on-call processes.

Why use On Call Handoff Patterns on TypingMind?

Because you install it once and use it with any model. On Call Handoff Patterns 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 On Call Handoff Patterns in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Dicklesworthstone/pi_agent_rust/tree/main/tests/ext_conformance/artifacts/agents-wshobson/incident-response/skills/on-call-handoff-patterns. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use On Call Handoff Patterns?

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 On Call Handoff Patterns?

As many as you like. As long as a model supports skills, you can use On Call Handoff Patterns with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the On Call Handoff Patterns AI skill free?

It is published on GitHub by Dicklesworthstone. Check the repository for licensing terms. 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.

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