// Use this skill when you need to search Datadog logs, query metrics, tail logs in real-time, trace distributed requests, investigate errors, compare time periods, find log patterns, check service health, or export observability data.
Compress natural language memory files (CLAUDE.md, todos, preferences) into caveman format to save input tokens. Preserves all technical substance, code, URLs, and structure. Compressed version overwrites the original file. Human-readable backup saved as FILE.original.md. Trigger: /caveman:compress <filepath> or "compress memory file"
Apply Google Style documentation standards to Python, Go, and Terraform code. Use when writing or reviewing code that needs docstrings/comments, when asked to "document this code", "add docstrings", "follow Google Style", or when improving code documentation quality. Supports Python docstrings, Go comments, and Terraform variable/output descriptions. Enforces consistent, professional documentation standards.
Expert guidance for using the GitLab CLI (glab) to manage GitLab issues, merge requests, CI/CD pipelines, repositories, and other GitLab operations from the command line. Use this skill when the user needs to interact with GitLab resources or perform GitLab workflows.
Use this skill whenever the user wants to make HTTP requests, test APIs, call REST endpoints, debug web services, or interact with any HTTP/HTTPS API using the HTTPie CLI tool (`http` command). Trigger this skill for any request involving: sending GET/POST/PUT/PATCH/DELETE requests, testing API endpoints, sending JSON or form data, working with authentication (Bearer token, Basic auth), uploading files, downloading responses, managing sessions, inspecting request/response headers, or piping HTTP calls in shell scripts. Use this skill even when the user just says "call the API", "send a request", "hit this endpoint", or "test with httpie" � don't wait for explicit httpie mention.
Remove signs of AI-generated writing from text. Use when editing or reviewing text to make it sound more natural and human-written. Based on Wikipedia's comprehensive "Signs of AI writing" guide. Detects and fixes patterns including: inflated symbolism, promotional language, superficial -ing analyses, vague attributions, em dash overuse, rule of three, AI vocabulary words, negative parallelisms, and excessive conjunctive phrases.
Use when the user mentions Jira issues (e.g., "PROJ-123"), asks about tickets, wants to create/view/update issues, check sprint status, or manage their Jira workflow. Triggers on keywords like "jira", "issue", "ticket", "sprint", "backlog", or issue key patterns.
| name | datadog |
| description | Use this skill when you need to search Datadog logs, query metrics, tail logs in real-time, trace distributed requests, investigate errors, compare time periods, find log patterns, check service health, or export observability data. |
This skill will help you to interact with Datadog, through the unofficial datadog-cli to search Datadog logs, query metrics, tail logs in real-time, trace distributed requests, investigate errors, compare time periods, find log patterns, check service health, or export observability data.
datadog logs search --query "<query>" [--from <time>] [--to <time>] [--limit <n>] [--sort <order>]
Examples:
datadog logs search --query "status:error" --from 1h
datadog logs search --query "service:api status:error @http.status_code:500" --from 1h
Stream logs as they arrive. Press Ctrl+C to stop.
datadog logs tail --query "<query>" [--interval <seconds>]
Examples:
datadog logs tail --query "status:error"
datadog logs tail --query "service:api" --interval 5
Find all logs for a distributed trace across services.
datadog logs trace --id "<trace-id>" [--from <time>] [--to <time>]
Example:
datadog logs trace --id "abc123def456" --from 24h
Get logs before and after a specific timestamp to understand what happened.
datadog logs context --timestamp "<iso-timestamp>" [--before <time>] [--after <time>] [--service <svc>]
Examples:
datadog logs context --timestamp "2024-01-15T10:30:00Z" --before 5m --after 2m
datadog logs context --timestamp "2024-01-15T10:30:00Z" --service api --before 10m
Quick breakdown of errors by service, type, and message.
datadog errors [--from <time>] [--to <time>] [--service <svc>]
Examples:
datadog errors --from 1h
datadog errors --service payment-api --from 24h
Compare log counts between current period and previous period.
datadog logs compare --query "<query>" --period <time>
Examples:
datadog logs compare --query "status:error" --period 1h
datadog logs compare --query "service:api status:error" --period 6h
Group similar log messages to find patterns (replaces UUIDs, numbers, etc.).
datadog logs patterns --query "<query>" [--from <time>] [--limit <n>]
Examples:
datadog logs patterns --query "status:error" --from 1h
datadog logs patterns --query "service:api" --from 6h --limit 1000
List all services with recent log activity.
datadog services [--from <time>] [--to <time>]
Example:
datadog services --from 24h
datadog logs agg --query "<query>" --facet <facet> [--from <time>]
Common facets: status, service, host, @http.status_code, @error.kind
Examples:
datadog logs agg --query "*" --facet status --from 1h
datadog logs agg --query "status:error" --facet service --from 24h
Run multiple queries in parallel.
datadog logs multi --queries "name1:query1,name2:query2" [--from <time>]
Example:
datadog logs multi --queries "errors:status:error,warnings:status:warn" --from 1h
datadog metrics query --query "<metrics-query>" [--from <time>] [--to <time>]
Query format: <aggregation>:<metric>{<tags>}
Examples:
datadog metrics query --query "avg:system.cpu.user{*}" --from 1h
datadog metrics query --query "avg:system.cpu.user{service:api}" --from 1h
datadog metrics query --query "sum:trace.http.request.errors{service:api}.as_count()" --from 1h
| Flag | Description |
|---|---|
--pretty | Human-readable output with colors |
--output <file> | Export results to JSON file |
--site <site> | Datadog site (e.g., datadoghq.eu) |
30m, 1h, 6h, 24h, 7d2024-01-15T10:30:00Z# 1. Quick error overview
datadog errors --from 1h
# 2. Is this new? Compare to previous period
datadog logs compare --query "status:error" --period 1h
# 3. What patterns are we seeing?
datadog logs patterns --query "status:error" --from 1h
# 4. Narrow down by service
datadog logs search --query "status:error service:payment-api" --from 1h
# 5. Get context around a specific timestamp
datadog logs context --timestamp "2024-01-15T10:30:00Z" --service api --before 5m --after 2m
# 6. Follow the distributed trace
datadog logs trace --id "TRACE_ID"
# Stream errors as they happen
datadog logs tail --query "status:error"
# Watch specific service
datadog logs tail --query "service:api status:error"
# List services
datadog services --from 24h
# Check error distribution
datadog logs agg --query "service:api" --facet status --from 1h
# Check CPU/memory
datadog metrics query --query "avg:system.cpu.user{service:api}" --from 1h
# Save search results
datadog logs search --query "status:error" --from 1h --output errors.json
# Save error summary
datadog errors --from 24h --output error-report.json
| Operator | Example | Description |
|---|---|---|
AND | service:api status:error | Both conditions |
OR | status:error OR status:warn | Either condition |
- | -status:info | Exclude |
* | service:api-* | Wildcard |
>= <= | @http.status_code:>=400 | Numeric comparison |
[TO] | @duration:[1000 TO 5000] | Range |
service - Service namestatus - Log level (error, warn, info, debug)host - Hostname@http.status_code - HTTP status code@error.kind - Error type@trace_id / @dd.trace_id - Trace ID