grammarly-incident-runbook
Follow Grammarly incident response runbook for API outages. Use when Grammarly API is down, experiencing errors, or when investigating service degradation. Trigger with phrases like "grammarly down", "grammarly outage", "grammarly incident", "grammarly not responding".
What this skill does
# Grammarly Incident Runbook
## Overview
Incident response procedures for Grammarly writing API integration failures. Covers text check timeouts, suggestion quality degradation, OAuth token failures, and rate limit storms. Grammarly powers real-time writing assistance, so API incidents directly impact user-facing text checking, scoring workflows, and content quality pipelines. Classify severity immediately using the matrix below and follow the corresponding playbook.
## Severity Levels
| Level | Definition | Response Time | Example |
|-------|-----------|---------------|---------|
| P1 - Critical | Full API outage, all scoring requests fail | 15 min | 5xx on `/v2/scores` for all requests |
| P2 - High | OAuth token failures or sustained timeouts | 30 min | All authenticated requests return 401 |
| P3 - Medium | Rate limit storms or elevated latency | 2 hours | 429 responses, scoring takes 10s+ per request |
| P4 - Low | Suggestion quality drift or single endpoint issue | 8 hours | Scores returning but correctness values seem off |
## Diagnostic Steps
```bash
# Test API health (unauthenticated)
curl -s -o /dev/null -w "HTTP %{http_code}\n" \
https://api.grammarly.com/ecosystem/api/v2/scores
# Test authenticated scoring
curl -s -w "\nHTTP %{http_code}\n" \
-H "Authorization: Bearer $GRAMMARLY_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-X POST https://api.grammarly.com/ecosystem/api/v2/scores \
-d '{"text": "Test sentence for Grammarly API diagnostic health check."}'
# Check OAuth token validity
curl -s -o /dev/null -w "HTTP %{http_code}\n" \
-H "Authorization: Bearer $GRAMMARLY_ACCESS_TOKEN" \
https://api.grammarly.com/ecosystem/api/v2/account
```
## Incident Playbooks
### API Outage
1. Confirm with unauthenticated health check (see diagnostics)
2. Check Grammarly status page and developer announcements
3. Activate fallback mode — return placeholder scores to avoid blocking users
4. Queue text submissions for retry when API recovers
5. Notify downstream consumers that scores are unavailable
### Authentication Failure
1. Test token validity with the account endpoint diagnostic above
2. If 401: OAuth access token has expired — trigger token refresh flow
3. If refresh token also fails: re-authorize via OAuth consent flow
4. Verify client ID and client secret are correct in environment config
5. Deploy refreshed tokens and confirm scoring requests succeed
### Data Sync Failure
1. Identify if scoring results are stale or inconsistent across requests
2. Check if Grammarly updated their scoring model (review changelog)
3. Compare current scores against known baseline text samples
4. If quality drift confirmed: log evidence and file support ticket
5. Consider pinning API version if Grammarly supports versioned endpoints
## Communication Template
```markdown
**Incident**: Grammarly Integration [Outage/Degradation]
**Status**: [Investigating/Identified/Mitigating/Resolved]
**Started**: YYYY-MM-DD HH:MM UTC
**Impact**: [Text scoring unavailable / elevated latency / OAuth failure affecting N users]
**Current action**: [Fallback scores active / token refresh in progress / rate limit backoff enabled]
**Next update**: HH:MM UTC
```
## Post-Incident
- [ ] Document timeline from detection to resolution
- [ ] Identify root cause (Grammarly outage / token expiry / rate limit exceeded)
- [ ] Verify all scoring pipelines resumed with accurate results
- [ ] Add proactive token refresh before expiry (buffer by 10 min)
- [ ] Implement rate limit monitoring with alerting at 80% threshold
- [ ] Update fallback logic if edge cases were discovered
## Error Handling
| Incident Type | Detection | Resolution |
|--------------|-----------|------------|
| Text check timeout | Requests exceeding 10s SLA | Enable timeout + retry with exponential backoff |
| Suggestion quality degradation | Scores deviate from baseline samples | Log evidence, pin API version, file support ticket |
| OAuth token failure | 401 on all authenticated endpoints | Trigger refresh flow, re-authorize if refresh fails |
| Rate limit storm | 429 responses with Retry-After header | Implement request queuing, reduce concurrency |
## Resources
- Grammarly Developer Docs
- Grammarly API Support
## Next Steps
See `grammarly-observability` for monitoring setup and alerting thresholds.
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.