القائمة
Datadog CLI for searching logs, querying metrics, tracing requests, and viewing dashboards. Use this when debugging production issues or working with Datadog observability.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Creates TDD implementation plans where tests ARE the plan. Specifies test specs and structural context per phase — implementation emerges during execution, not planning. Verification is fully automated; no manual verification step. Use when planning features test-first, creating TDD plans from design artifacts, or when the user asks for a test-driven implementation plan. NOT for executing a plan test-first (the Red-Green-Refactor cycle during implementation) — that is practicing-tdd.
Breaks an epic into an ordered backlog of vertical, sprint-sized user stories. Use when splitting an epic into stories, planning a feature's story backlog, or when a story feels too large but isn't ready for task breakdown. Produces 4-8 demoable stories, each independently shippable and grounded in user-visible behavior. NOT for sub-story implementation slicing (see slicing-elephant-carpaccio) or for writing one story's full BDD spec (see writing-agile-stories).
Slices a single feature or story into ultra-thin vertical increments (minutes-to-hours each) using Alistair Cockburn's Elephant Carpaccio methodology. Use during implementation planning when one story or feature is too large to build in a session and you need demoable increments across layers. Produces an ordered backlog of 10-20 thin slices, each independently working, testable, and demoable. NOT for splitting an epic into sprint-sized stories (see decomposing-epics).
Write well-scoped, verifiable development tasks for non-user-facing work — refactors, test work, and dependency/tooling changes. Use to "write a dev task", create a "refactor task", a "tech-debt ticket", a "test task", or a "dependency upgrade task"; to capture "non-user-facing work" or a "technical task"; or to write a "definition of done for a refactor". Produces an implementation-focused task with a verifiable Definition of Done. For user-facing behavior, use writing-agile-stories instead.
Expert guidance for creating, writing, and refining Claude Code Skills. Use when working with SKILL.md files, authoring new skills, improving existing skills, or understanding skill structure and best practices.
Examines a git-machete stack of dependent branches and drafts preemptive PR comments warning reviewers that code in an earlier PR is already refactored, renamed, moved, deleted, or fixed later in the stack. Output is DRAFT ONLY — the user pastes it manually; the skill never posts to GitHub. Use for stacked PRs, a git-machete stack, when reviewers comment on code that is "already fixed/refactored/handled later in the stack", to preempt reviewer comments on superseded code, to draft PR comments for a branch stack, or to find code in an earlier branch that a later branch supersedes.
| name | datadog-cli |
| description | Datadog CLI for searching logs, querying metrics, tracing requests, and viewing dashboards. Use this when debugging production issues or working with Datadog observability. |
A CLI tool for AI agents to debug and triage using Datadog logs and metrics.
You MUST read the relevant reference docs before using any command:
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
npx @leoflores/datadog-cli <command>
For non-US Datadog sites, use --site flag:
npx @leoflores/datadog-cli logs search --query "*" --site datadoghq.eu
| Command | Description |
|---|---|
logs search | Search logs with filters |
logs tail | Stream logs in real-time |
logs trace | Find logs for a distributed trace |
logs context | Get logs before/after a timestamp |
logs patterns | Group similar log messages |
logs compare | Compare log counts between periods |
logs multi | Run multiple queries in parallel |
logs agg | Aggregate logs by facet |
metrics query | Query timeseries metrics |
errors | Quick error summary by service/type |
services | List services with log activity |
dashboards list | List all dashboards |
dashboards get | Get full dashboard definition |
dashboard-lists list | List all dashboard lists |
dashboard-lists get | Get dashboard list details |
dashboard-lists items | List dashboards in a list |
npx @leoflores/datadog-cli logs search --query "status:error" --from 1h --pretty
npx @leoflores/datadog-cli logs tail --query "service:api status:error" --pretty
npx @leoflores/datadog-cli errors --from 1h --pretty
npx @leoflores/datadog-cli logs trace --id "abc123def456" --pretty
npx @leoflores/datadog-cli metrics query --query "avg:system.cpu.user{*}" --from 1h --pretty
npx @leoflores/datadog-cli logs compare --query "status:error" --period 1h --pretty
| 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
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.