adobe-incident-runbook
Execute Adobe incident response procedures with triage, mitigation, and postmortem for Firefly Services, PDF Services, and I/O Events outages. Use when responding to Adobe-related incidents, investigating API failures, or running post-incident reviews. Trigger with phrases like "adobe incident", "adobe outage", "adobe down", "adobe on-call", "adobe emergency".
What this skill does
# Adobe Incident Runbook
## Overview
Rapid incident response procedures for Adobe API-related outages, covering IMS authentication failures, Firefly/Photoshop API downtime, PDF Services quota exhaustion, and I/O Events delivery failures.
## Prerequisites
- Access to Adobe Developer Console and Admin Console
- Access to application monitoring (Grafana, Datadog, etc.)
- kubectl access to production cluster (if applicable)
- Communication channels (Slack, PagerDuty)
## Severity Matrix
| Level | Definition | Response Time | Example |
|-------|------------|---------------|---------|
| P1 | Complete Adobe integration failure | < 15 min | IMS auth broken, all APIs down |
| P2 | Single API degraded | < 1 hour | Firefly 429s, Photoshop timeouts |
| P3 | Minor impact | < 4 hours | Webhook delays, slow PDF extraction |
| P4 | No user impact | Next business day | Monitoring gap, metric anomaly |
## Quick Triage (Run These First)
```bash
# 1. Is Adobe itself down?
curl -s -o /dev/null -w "Adobe Status: %{http_code}\n" https://status.adobe.com
# 2. Can we generate an access token?
curl -s -o /dev/null -w "IMS Auth: %{http_code}\n" -X POST \
'https://ims-na1.adobelogin.com/ims/token/v3' \
-d "client_id=${ADOBE_CLIENT_ID}&client_secret=${ADOBE_CLIENT_SECRET}&grant_type=client_credentials&scope=${ADOBE_SCOPES}"
# 3. Can we reach each API endpoint?
for endpoint in firefly-api.adobe.io image.adobe.io pdf-services.adobe.io; do
CODE=$(curl -s -o /dev/null -w "%{http_code}" --connect-timeout 5 "https://$endpoint" 2>/dev/null || echo "UNREACHABLE")
echo "$endpoint: $CODE"
done
# 4. Check our app health
curl -sf https://your-app.com/health | python3 -m json.tool
# 5. Recent errors in our logs (last 5 min)
kubectl logs -l app=adobe-service --since=5m 2>/dev/null | grep -i "error\|failed\|429\|401\|500" | tail -20
```
## Decision Tree
```
Adobe APIs returning errors?
├── YES: Is status.adobe.com reporting an incident?
│ ├── YES → Adobe-side outage. Enable fallback mode. Monitor status page.
│ └── NO → Check our credentials and config.
│ ├── 401 errors → Credentials expired/rotated. See "Auth Recovery" below.
│ ├── 429 errors → Rate limited. See "Rate Limit Recovery" below.
│ └── 500/503 errors → Adobe server issue (unreported). Open support ticket.
└── NO: Is our application healthy?
├── YES → Likely resolved or intermittent. Continue monitoring.
└── NO → Our infrastructure issue. Check pods, memory, network.
```
## Recovery Procedures
### Auth Recovery (401/403)
```bash
# 1. Verify credentials are still valid in Developer Console
# https://developer.adobe.com/console → Your Project → Credentials
# 2. Test credential directly
curl -v -X POST 'https://ims-na1.adobelogin.com/ims/token/v3' \
-d "client_id=${ADOBE_CLIENT_ID}&client_secret=${ADOBE_CLIENT_SECRET}&grant_type=client_credentials&scope=${ADOBE_SCOPES}" 2>&1 | grep -E "HTTP|error"
# 3. If credentials were rotated, update in secret manager
gcloud secrets versions add adobe-client-secret --data-file=- <<< "new_p8_secret"
# OR
aws secretsmanager update-secret --secret-id adobe/production/credentials \
--secret-string '{"client_id":"...","client_secret":"new_secret"}'
# 4. Restart application to clear cached token
kubectl rollout restart deployment/adobe-service
# 5. Verify recovery
curl -sf https://your-app.com/health | jq '.services.adobe'
```
### Rate Limit Recovery (429)
```bash
# 1. Check if rate limiting is transient or sustained
# Look at 429 error rate over last 30 min
# 2. Reduce throughput immediately
# Option A: Scale down workers
kubectl scale deployment/adobe-batch-worker --replicas=1
# Option B: Enable rate limit queue mode
kubectl set env deployment/adobe-service ADOBE_RATE_LIMIT_MODE=queue
# 3. For sustained rate limiting, contact Adobe for limit increase
# Include: client_id, typical request volume, business justification
```
### Fallback Mode
```bash
# Enable fallback mode (app continues working without Adobe)
kubectl set env deployment/adobe-service ADOBE_FALLBACK_MODE=true
# Verify fallback is working
curl -sf https://your-app.com/health | jq '.services.adobe'
# Should return { "status": "degraded", "mode": "fallback" }
```
## Communication Templates
### Internal (Slack)
```
P[1-4] INCIDENT: Adobe [API Name] Integration
Status: INVESTIGATING / IDENTIFIED / MONITORING / RESOLVED
Impact: [User-facing description]
Root cause: [Adobe outage / credential issue / rate limit / our bug]
Current action: [What you're doing right now]
Next update: [Time]
Commander: @[name]
```
### Postmortem Template
```markdown
## Incident: Adobe [API] [Error Type]
**Date:** YYYY-MM-DD
**Duration:** X hours Y minutes
**Severity:** P[1-4]
### Summary
[1-2 sentence description of what happened]
### Timeline
- HH:MM UTC — Alert fired: adobe_api_errors_total spike
- HH:MM UTC — On-call acknowledged, began triage
- HH:MM UTC — Root cause identified: [description]
- HH:MM UTC — Mitigation applied: [action taken]
- HH:MM UTC — Full recovery confirmed
### Root Cause
[Technical explanation — was it Adobe-side, credential issue, our bug?]
### Impact
- Users affected: N
- API calls failed: N
- Revenue impact: $X (if applicable)
### Action Items
- [ ] [Preventive measure] — Owner — Due date
- [ ] [Monitoring improvement] — Owner — Due date
- [ ] [Documentation update] — Owner — Due date
```
## Output
- Incident severity classified
- Root cause identified via decision tree
- Recovery procedure executed
- Stakeholders notified with template
- Evidence collected for postmortem
## Error Handling
| Issue | Cause | Solution |
|-------|-------|----------|
| Can't reach status.adobe.com | Network issue | Use mobile data or check @AdobeCare on Twitter |
| kubectl auth expired | Token timeout | Re-authenticate with cloud provider |
| Secret manager access denied | IAM policy | Use break-glass admin account |
| Fallback mode not implemented | Missing code path | Return cached/default data |
## Resources
- [Adobe Status Page](https://status.adobe.com)
- [Adobe Developer Support](https://developer.adobe.com/support)
- [Adobe Developer Console](https://developer.adobe.com/console)
## Next Steps
For data handling, see `adobe-data-handling`.
Related in Backend & APIs
jfrog
IncludedInteract with the JFrog Platform via the JFrog CLI and REST/GraphQL APIs. Use this skill when the user wants to manage Artifactory repositories, upload or download artifacts, manage builds, configure permissions, manage users and groups, work with access tokens, configure JFrog CLI servers, search artifacts, manage properties, set up replication, manage JFrog Projects, run security audits or scans, look up CVE details, query exposures scan results from JFrog Advanced Security, manage release bundles and lifecycle operations, aggregate or export platform data, or perform any JFrog Platform administration task. Also use when the user mentions jf, jfrog, artifactory, xray, distribution, evidence, apptrust, onemodel, graphql, workers, mission control, curation, advanced security, exposures, or any JFrog product name.
cupynumeric-migration-readiness
IncludedPre-migration readiness assessor for porting NumPy to cuPyNumeric. Use BEFORE substantial porting work begins when the user asks whether code will scale on GPU, whether they should migrate to cuPyNumeric, which NumPy patterns transfer cleanly, what must be refactored before porting, or mentions pre-port assessment, scaling analysis, or refactor planning. Inspect the user's source code, look up NumPy usage, cross-reference the cuPyNumeric API support manifest, and distinguish distributed-scaling-friendly patterns from blockers such as unsupported APIs, scalar synchronization, host round-trips, Python/object-heavy control flow, shape/data-dependent branching, and in-place mutation hazards. Produce a verdict of READY, LIGHT REFACTOR, SIGNIFICANT REFACTOR, or NOT RECOMMENDED, with concrete refactor pointers.
alibabacloud-data-agent-skill
IncludedInvoke Alibaba Cloud Apsara Data Agent for Analytics via CLI to perform natural language-driven data analysis on enterprise databases. Data Agent for Analytics is an intelligent data analysis agent developed by Alibaba Cloud Database team for enterprise users. It automatically completes requirement analysis, data understanding, analysis insights, and report generation based on natural language descriptions. This tool supports: discovering data resources (instances/databases/tables) managed in DMS, initiating query or deep analysis sessions, real-time progress tracking, and retrieving analysis conclusions and generated reports. Use this Skill when users need to query databases, analyze data trends, generate data reports, ask questions in natural language, or mention "Data Agent", "data analysis", "database query", "SQL analysis", "data insights".
token-optimizer
IncludedReduce OpenClaw token usage and API costs through smart model routing, heartbeat optimization, budget tracking, and native 2026.2.15 features (session pruning, bootstrap size limits, cache TTL alignment). Use when token costs are high, API rate limits are being hit, or hosting multiple agents at scale. The 4 executable scripts (context_optimizer, model_router, heartbeat_optimizer, token_tracker) are local-only — no network requests, no subprocess calls, no system modifications. Reference files (PROVIDERS.md, config-patches.json) document optional multi-provider strategies that require external API keys and network access if you choose to use them. See SECURITY.md for full breakdown.
resend-cli
IncludedUse this skill when the task is specifically about operating Resend from an AI agent, terminal session, or CI job via the official resend CLI: installing/authenticating the CLI, sending/listing/updating/cancelling emails, batch sends, domains and DNS, webhooks and local listeners, inbound receiving, contacts, topics, segments, broadcasts, templates, API keys, profiles, or debugging Resend CLI/API failures. Trigger on mentions of Resend CLI, `resend`, `resend doctor`, `resend emails send`, `resend domains`, `resend webhooks listen`, `resend emails receiving`, or agent-friendly terminal automation.
alibabacloud-odps-maxframe-coding
IncludedUse this skill for MaxFrame SDK development and documentation navigation on Alibaba Cloud MaxCompute (ODPS). Helps answer MaxFrame API, concept, official example, and supported pandas API questions; create data processing programs; read/write MaxCompute tables; debug jobs (remote or local); and build custom DPE runtime images. Trigger when users mention MaxFrame, MaxCompute with MaxFrame, ODPS table processing, DPE runtime, MaxFrame docs/examples, DataFrame/Tensor operations, or GPU runtime setup. Works for both English and Chinese queries about Alibaba Cloud data processing with MaxFrame.