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Runbook Creation

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BagelHole
runbook-creation

Create operational runbooks and standard operating procedures. Document troubleshooting guides and recovery procedures. Use when documenting operational knowledge.

Overview

PublisherBagelHole
RepositoryDevOps-Security-Agent-Skills
Skill namerunbook-creation
Stars
1.1K
Forks
161
Bundled files
Instructions only
LicenseMIT
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 BagelHole on GitHub. Read the source before you install it.

Installation

Install the Runbook Creation 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/BagelHole/DevOps-Security-Agent-Skills/tree/main/compliance/continuity/runbook-creation
  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/BagelHole/DevOps-Security-Agent-Skills.git /tmp/DevOps-Security-Agent-Skills
mkdir -p .claude/skills
cp -r /tmp/DevOps-Security-Agent-Skills/compliance/continuity/runbook-creation .claude/skills/runbook-creation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Runbook Creation 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 Runbook Creation 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 Runbook Creation 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.

Runbook Creation

Create effective operational runbooks, standard operating procedures, and troubleshooting guides that any on-call engineer can follow under pressure.

Runbook Template — Full Structure

markdown
# Runbook: [Service / Process Name]

**Owner:** [Team or individual]
**Last Reviewed:** YYYY-MM-DD
**Version:** X.Y
**Severity if unavailable:** SEV[1-4]

---

## Overview

Brief description of the service, why this runbook exists, and when to
use it.

## Prerequisites

- [ ] Required access / IAM role: [details]
- [ ] Tools installed: [kubectl, aws-cli, psql, etc.]
- [ ] VPN connected to [environment]
- [ ] Communication channel open: [Slack #channel]

## Procedure

### Step 1 — [Action Name]

[Explanation of what this step does and why.]

```bash
# command here
```

**Expected output:** [describe what success looks like]

### Step 2 — [Action Name]

```bash
# command here
```

**Expected output:** [description]

*(Continue with numbered steps...)*

## Verification

How to confirm the procedure succeeded:

- [ ] [Check 1 — e.g., health endpoint returns 200]
- [ ] [Check 2 — e.g., no errors in logs for 5 minutes]
- [ ] [Check 3 — e.g., metrics return to baseline]

## Rollback

If the procedure fails or causes unexpected issues:

### Rollback Step 1
```bash
# rollback command
```

### Rollback Step 2
```bash
# rollback command
```

## Troubleshooting

| Symptom | Likely Cause | Resolution |
|---------|-------------|------------|
| [symptom 1] | [cause] | [fix] |
| [symptom 2] | [cause] | [fix] |

## Escalation

If unresolved after [X] minutes:
- **Primary:** @[team-lead] — [phone/Slack]
- **Secondary:** @[manager] — [phone/Slack]

## Related Runbooks

- [Link to related runbook 1]
- [Link to related runbook 2]

## Change Log

| Date | Author | Change |
|------|--------|--------|
| YYYY-MM-DD | [Name] | Initial version |

Example Runbook — Database Failover

markdown
# Runbook: PostgreSQL Database Failover

**Owner:** Platform / DBA team
**Last Reviewed:** 2025-06-15
**Version:** 2.1
**Severity if unavailable:** SEV1

---

## Overview

Failover the primary PostgreSQL instance to the synchronous replica when
the primary is unreachable or degraded. This runbook covers both planned
(maintenance) and unplanned (emergency) failover.

## Prerequisites

- [ ] DBA or SRE-level access to primary and replica hosts
- [ ] `psql` client installed (v14+)
- [ ] VPN connected to production network
- [ ] Slack channel #db-ops open
- [ ] Confirm replica is in sync: replication lag < 1 MB

## Procedure

### Step 1 — Verify Replica Health

```bash
psql -h replica.db.internal -U dba -d postgres -c \
  "SELECT pg_is_in_recovery(), pg_last_wal_replay_lsn();"
```

**Expected output:** `pg_is_in_recovery = t`, LSN advancing.

### Step 2 — Stop Application Writes

```bash
kubectl scale deployment api-server --replicas=0 -n production
kubectl scale deployment worker --replicas=0 -n production
```

**Expected output:** Deployments scaled to 0 pods.

### Step 3 — Confirm Write Quiesce

```bash
psql -h primary.db.internal -U dba -d postgres -c \
  "SELECT count(*) FROM pg_stat_activity WHERE state = 'active' AND query !~ 'pg_stat';"
```

**Expected output:** Count = 0 (no active queries).

### Step 4 — Promote Replica

```bash
psql -h replica.db.internal -U dba -d postgres -c "SELECT pg_promote();"
```

Wait up to 30 seconds, then confirm:

```bash
psql -h replica.db.internal -U dba -d postgres -c "SELECT pg_is_in_recovery();"
```

**Expected output:** `pg_is_in_recovery = f` (no longer a replica).

### Step 5 — Update DNS

```bash
aws route53 change-resource-record-sets \
  --hosted-zone-id Z1234567890 \
  --change-batch '{
    "Changes": [{
      "Action": "UPSERT",
      "ResourceRecordSet": {
        "Name": "db.internal.example.com",
        "Type": "CNAME",
        "TTL": 60,
        "ResourceRecords": [{"Value": "replica.db.internal"}]
      }
    }]
  }'
```

### Step 6 — Restart Application

```bash
kubectl scale deployment api-server --replicas=6 -n production
kubectl scale deployment worker --replicas=4 -n production
```

## Verification

- [ ] `psql -h db.internal.example.com -c "SELECT 1;"` returns successfully
- [ ] Application logs show successful DB connections (no errors for 5 min)
- [ ] Transaction throughput returns to baseline on Grafana dashboard
- [ ] No replication-lag alerts firing

## Rollback

If the promoted replica has issues, restore from the most recent backup:

```bash
# Restore latest automated snapshot (RDS example)
aws rds restore-db-instance-from-db-snapshot \
  --db-instance-identifier prod-db-restored \
  --db-snapshot-identifier prod-db-latest-snapshot
```

## Escalation

If unresolved after 15 minutes:
- **Primary:** @dba-lead — +1-555-0101
- **Secondary:** @platform-oncall — +1-555-0102

Automation Scripts for Common Operations

Service Health Check

bash
#!/usr/bin/env bash
# health-check.sh — Check health of critical services
set -euo pipefail

SERVICES=(
  "https://api.example.com/healthz"
  "https://app.example.com/healthz"
  "https://admin.example.com/healthz"
)

EXIT_CODE=0

for url in "${SERVICES[@]}"; do
  HTTP_CODE=$(curl -so /dev/null -w '%{http_code}' --max-time 5 "$url" 2>/dev/null || echo "000")
  if [ "$HTTP_CODE" -eq 200 ]; then
    printf "  OK    %s\n" "$url"
  else
    printf "  FAIL  %s (HTTP %s)\n" "$url" "$HTTP_CODE"
    EXIT_CODE=1
  fi
done

exit $EXIT_CODE

Log Collection for Incident Investigation

bash
#!/usr/bin/env bash
# collect-logs.sh — Gather logs from multiple sources for incident review
set -euo pipefail

INCIDENT_ID="${1:?Usage: collect-logs.sh <incident-id>}"
OUTDIR="/tmp/incident-${INCIDENT_ID}"
mkdir -p "$OUTDIR"

echo "Collecting logs for incident $INCIDENT_ID..."

# Kubernetes pod logs (last 30 min)
kubectl logs -l app=api-server -n production --since=30m \
  > "${OUTDIR}/api-server-pods.log" 2>&1

# CloudWatch Logs (last 30 min)
aws logs filter-log-events \
  --log-group-name /ecs/production/api \
  --start-time "$(date -d '30 minutes ago' +%s)000" \
  --output text > "${OUTDIR}/cloudwatch-api.log" 2>&1

# Database slow query log
psql -h db.internal -U dba -d postgres -c \
  "SELECT * FROM pg_stat_activity WHERE state != 'idle' ORDER BY query_start;" \
  > "${OUTDIR}/db-active-queries.log" 2>&1

# System resource snapshot
kubectl top pods -n production > "${OUTDIR}/pod-resources.log" 2>&1

echo "Logs saved to $OUTDIR"
tar czf "${OUTDIR}.tar.gz" -C /tmp "incident-${INCIDENT_ID}"
echo "Archive: ${OUTDIR}.tar.gz"

Certificate Expiry Check

bash
#!/usr/bin/env bash
# cert-check.sh — Warn if TLS certificates expire within 30 days
set -euo pipefail

DOMAINS=(
  "api.example.com"
  "app.example.com"
  "admin.example.com"
)

WARN_DAYS=30
TODAY=$(date +%s)
EXIT_CODE=0

for domain in "${DOMAINS[@]}"; do
  EXPIRY=$(echo | openssl s_client -servername "$domain" -connect "${domain}:443" 2>/dev/null \
    | openssl x509 -noout -enddate 2>/dev/null | cut -d= -f2)
  EXPIRY_EPOCH=$(date -d "$EXPIRY" +%s 2>/dev/null || echo 0)
  DAYS_LEFT=$(( (EXPIRY_EPOCH - TODAY) / 86400 ))

  if [ "$DAYS_LEFT" -lt "$WARN_DAYS" ]; then
    printf "  WARN  %s expires in %d days (%s)\n" "$domain" "$DAYS_LEFT" "$EXPIRY"
    EXIT_CODE=1
  else
    printf "  OK    %s — %d days remaining\n" "$domain" "$DAYS_LEFT"
  fi
done

exit $EXIT_CODE

Disk Space Cleanup

bash
#!/usr/bin/env bash
# disk-cleanup.sh — Free disk space on a host
set -euo pipefail

echo "=== Disk Usage Before ==="
df -h /

# Remove old journal logs (> 7 days)
journalctl --vacuum-time=7d 2>/dev/null || true

# Clean Docker artifacts
docker system prune -f --volumes 2>/dev/null || true

# Remove old log files
find /var/log -name "*.gz" -mtime +7 -delete 2>/dev/null || true
find /tmp -type f -mtime +3 -delete 2>/dev/null || true

echo "=== Disk Usage After ==="
df -h /

Runbook Review Checklist

Use this checklist every time a runbook is created or updated.

yaml
content_review:
  - [ ] Title clearly identifies the service and operation
  - [ ] Overview explains WHEN and WHY to use this runbook
  - [ ] Prerequisites list all required access, tools, and setup
  - [ ] Every step has a concrete command (no vague instructions)
  - [ ] Expected output is documented for each step
  - [ ] Verification section confirms success with specific checks
  - [ ] Rollback section exists and has been tested
  - [ ] Escalation contacts are current (names, phones, Slack handles)
  - [ ] Troubleshooting table covers the top 3-5 known failure modes

usability_review:
  - [ ] A new team member can follow the runbook without tribal knowledge
  - [ ] Steps are numbered and sequential (no branching without clear labels)
  - [ ] Commands can be copy-pasted (no placeholder values without explanation)
  - [ ] Time estimates included for long-running steps
  - [ ] No jargon or acronyms used without definition

maintenance_review:
  - [ ] Owner and last-reviewed date are set
  - [ ] Version number incremented
  - [ ] Change log entry added
  - [ ] Related runbooks section is up to date
  - [ ] Links to dashboards and docs are valid (not broken)

Runbook Testing Procedures

yaml
testing_strategy:
  dry_run:
    frequency: "Every time a runbook is created or substantially edited"
    method: "Walk through each step in a staging environment"
    goal: "Verify commands work and output matches documentation"

  peer_review:
    frequency: "Every edit"
    method: "Another engineer follows the runbook in staging without help"
    goal: "Confirm the runbook is self-contained and unambiguous"

  scheduled_validation:
    frequency: "Quarterly"
    method: "SRE team picks 5 runbooks at random, executes in staging"
    goal: "Catch runbooks that have drifted from production reality"

  incident_triggered:
    trigger: "Any time a runbook is used in a real incident"
    method: "Post-mortem includes runbook accuracy assessment"
    goal: "Capture improvements while the experience is fresh"

  automation_testing:
    method: "CI pipeline validates bash scripts with shellcheck and dry-run"
    example: |
      # .github/workflows/runbook-lint.yml
      name: Lint Runbook Scripts
      on: [pull_request]
      jobs:
        shellcheck:
          runs-on: ubuntu-latest
          steps:
            - uses: actions/checkout@v4
            - name: ShellCheck
              run: |
                find runbooks/ -name "*.sh" -exec shellcheck {} +

Versioning Strategy

yaml
versioning:
  storage: "Git repository — one directory per service, one file per runbook"
  naming: "runbooks/<service>/<operation>.md"
  branching: "PRs required for all changes; reviewed by service owner"

  version_scheme:
    format: "MAJOR.MINOR"
    major_bump: "Procedure changes that alter the steps or their order"
    minor_bump: "Clarifications, typo fixes, updated contact info"

  directory_layout: |
    runbooks/
      api-server/
        deploy.md
        rollback.md
        scale-up.md
      database/
        failover.md
        backup-restore.md
        vacuum-maintenance.md
      infrastructure/
        dns-update.md
        certificate-renewal.md
        disk-cleanup.md

  review_requirements:
    - PR must be approved by the service owner
    - CI must pass (shellcheck for scripts, markdown lint)
    - Reviewer confirms they can follow the steps independently

  retention: "Git history serves as full audit trail — never delete old versions"

Runbook Index Template

Keep a top-level index so engineers can find the right runbook quickly.

markdown
# Runbook Index

| Service | Runbook | Severity | Owner | Last Tested |
|---------|---------|----------|-------|-------------|
| API Server | [Deploy](api-server/deploy.md) | — | @platform | 2025-05-01 |
| API Server | [Rollback](api-server/rollback.md) | SEV1 | @platform | 2025-05-01 |
| Database | [Failover](database/failover.md) | SEV1 | @dba | 2025-04-15 |
| Database | [Backup Restore](database/backup-restore.md) | SEV2 | @dba | 2025-04-15 |
| Infra | [DNS Update](infrastructure/dns-update.md) | SEV2 | @sre | 2025-06-01 |
| Infra | [Cert Renewal](infrastructure/certificate-renewal.md) | SEV3 | @sre | 2025-06-01 |

Best Practices

  • Write runbooks for the engineer at 3 AM — clear, sequential, copy-pasteable
  • Include expected output so the operator knows if a step succeeded
  • Always provide a rollback path; every action should be reversible
  • Test runbooks in staging before they are needed in production
  • Keep runbooks in version control alongside the code they support
  • Assign an owner to every runbook; ownerless runbooks rot fast
  • After every incident, update the relevant runbook with lessons learned
  • Automate repetitive runbook steps into scripts, but keep the runbook as the orchestration guide so operators understand the "why"

Frequently asked questions

What does the Runbook Creation AI skill do?

Create operational runbooks and standard operating procedures. Document troubleshooting guides and recovery procedures. Use when documenting operational knowledge.

Why use Runbook Creation on TypingMind?

Because you install it once and use it with any model. Runbook Creation 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 Runbook Creation in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/BagelHole/DevOps-Security-Agent-Skills/tree/main/compliance/continuity/runbook-creation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Runbook Creation?

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 Runbook Creation?

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

Is the Runbook Creation AI skill free?

Yes. It is published on GitHub by BagelHole 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.

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