跳到正文
FunCoding

搜索

搜索文档、Skill 和 MCP

datadog-cli

Datadog CLI for searching logs, querying metrics, tracing requests, and managing dashboards. Use this when debugging production issues or working with Datadog observability.

代码质量与审查2.5kskills/datadog-cli/SKILL.md

安装

把这段话发给 Claude Code、Codex 或 Cursor。智能体会先检查安全性,你确认后才安装。

读取 https://funcoding.ai/skills/softaworks/agent-toolkit/datadog-cli/install.md ,按里面的步骤帮我安装这个 Skill。

SKILL.md

Datadog CLI

A CLI tool for AI agents to debug and triage using Datadog logs and metrics.

Required Reading

You MUST read the relevant reference docs before using any command:

Setup

Environment Variables (Required)

export DD_API_KEY="your-api-key"
export DD_APP_KEY="your-app-key"

Get keys from: https://app.datadoghq.com/organization-settings/api-keys

Running the CLI

npx @leoflores/datadog-cli <command>

For non-US Datadog sites, use --site flag:

npx @leoflores/datadog-cli logs search --query "*" --site datadoghq.eu

Commands Overview

CommandDescription
logs searchSearch logs with filters
logs tailStream logs in real-time
logs traceFind logs for a distributed trace
logs contextGet logs before/after a timestamp
logs patternsGroup similar log messages
logs compareCompare log counts between periods
logs multiRun multiple queries in parallel
logs aggAggregate logs by facet
metrics queryQuery timeseries metrics
errorsQuick error summary by service/type
servicesList services with log activity
dashboardsManage dashboards (CRUD)
dashboard-listsManage dashboard lists

Quick Examples

Search Errors

npx @leoflores/datadog-cli logs search --query "status:error" --from 1h --pretty

Tail Logs (Real-time)

npx @leoflores/datadog-cli logs tail --query "service:api status:error" --pretty

Error Summary

npx @leoflores/datadog-cli errors --from 1h --pretty

Trace Correlation

npx @leoflores/datadog-cli logs trace --id "abc123def456" --pretty

Query Metrics

npx @leoflores/datadog-cli metrics query --query "avg:system.cpu.user{*}" --from 1h --pretty

Compare Periods

npx @leoflores/datadog-cli logs compare --query "status:error" --period 1h --pretty

Global Flags

FlagDescription
--prettyHuman-readable output with colors
--output <file>Export results to JSON file
--site <site>Datadog site (e.g., datadoghq.eu)

Time Formats

  • Relative: 30m, 1h, 6h, 24h, 7d
  • ISO 8601: 2024-01-15T10:30:00Z

Incident Triage Workflow

# 1. Quick error overview
npx @leoflores/datadog-cli errors --from 1h --pretty

# 2. Is this new? Compare to previous period
npx @leoflores/datadog-cli logs compare --query "status:error" --period 1h --pretty

# 3. Find error patterns
npx @leoflores/datadog-cli logs patterns --query "status:error" --from 1h --pretty

# 4. Narrow down by service
npx @leoflores/datadog-cli logs search --query "status:error service:api" --from 1h --pretty

# 5. Get context around a timestamp
npx @leoflores/datadog-cli logs context --timestamp "2024-01-15T10:30:00Z" --service api --pretty

# 6. Follow the distributed trace
npx @leoflores/datadog-cli logs trace --id "TRACE_ID" --pretty

See workflows.md for more debugging workflows.

相似的 Skill

claude-api
anthropics/skills180k

claude-api

Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file; don't skip because it "looks like a one-liner" — whenever: the prompt names Claude/Anthropic in any form (Claude, Anthropic, Fable, Opus, Sonnet, Haiku, `anthropic`, `@anthropic-ai`, `claude-*`, `us.anthropic.*`, `[1m]`); the user asks about an LLM (pricing/model choice/limits/caching) — never answer from memory; OR the task is LLM-shaped with provider unstated (agent/MCP/tool-definition/multi-agent/RAG/LLM-judge/computer-use; generate/summarize/extract/classify/rewrite/converse over NL; debugging refusals/cutoffs/streaming/tool-calls/tokens). SKIP only when another provider is being worked on (overrides all triggers): OpenAI/GPT/Gemini/Llama/Mistral/Cohere/Ollama named in the query; OR `grep -rE 'openai|langchain_openai|google.generativeai|genai|mistralai|cohere|ollama'` over the project hits (run this grep FIRST if no provider named — don't Read the file).

代码质量与审查

ponytail-review
DietrichGebert/ponytail158k

ponytail-review

Quality review of a change: is the logic right, is it safe, does it hold under real load, is risky code tested, is it fast enough, and is every line needed. Reads the connected code, not only the diff. Each finding is explained in plain English. Use for "review this", "code review", "review the last commit", "review my PR", "is this over-engineered", /ponytail-review.

代码质量与审查

code-review-and-quality
addyosmani/agent-skills103k

code-review-and-quality

Conducts multi-axis code review. Use before merging any change. Use when reviewing code written by yourself, another agent, or a human. Use when you need to assess code quality across multiple dimensions before it enters the main branch. Use when asked to review a diff or a pull request, even when the diff is pasted inline.

代码质量与审查

documentation-and-adrs
addyosmani/agent-skills103k

documentation-and-adrs

Records decisions and documentation. Use when you need to document an architecture decision (ADR) or the reasoning behind a design choice, when changing public APIs, shipping features, or when you need to record context that future engineers and agents will need to understand the codebase.

代码质量与审查

code-simplification
addyosmani/agent-skills103k

code-simplification

Simplifies code for clarity. Use when refactoring code for clarity without changing behavior. Use when code works but is harder to read, maintain, or extend than it should be. Use when reviewing code that has accumulated unnecessary complexity.

代码质量与审查

understand
Egonex-AI/Understand-Anything86k

understand

Analyze a codebase to produce an interactive knowledge graph for understanding architecture, components, and relationships

代码质量与审查