datadog-cli▌
softaworks/agent-toolkit · updated Apr 8, 2026
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A CLI tool for AI agents to debug and triage using Datadog logs and metrics.
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
| 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 |
Manage dashboards (CRUD) |
dashboard-lists |
Manage 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
| Flag | Description |
|---|---|
--pretty |
Human-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.
How to use datadog-cli on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add datadog-cli
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches datadog-cli from GitHub repository softaworks/agent-toolkit and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate datadog-cli. Access the skill through slash commands (e.g., /datadog-cli) or your agent's skill management interface.
Security & Verification Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases▌
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide▌
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Installation Steps
- 1.Install product management skill
- 2.Start with user story generation for known feature
- 3.Progress to competitive analysis: research 2-3 competitors
- 4.Use for roadmap prioritization: apply RICE/ICE scoring
- 5.Draft stakeholder communications and refine based on feedback
- 6.Build template library for recurring PM tasks
- 7.Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices▌
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This▌
✓ Use When
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid When
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path▌
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.5★★★★★51 reviews- ★★★★★Min Chawla· Dec 28, 2024
Solid pick for teams standardizing on skills: datadog-cli is focused, and the summary matches what you get after install.
- ★★★★★Anaya Mensah· Dec 4, 2024
We added datadog-cli from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Xiao Kapoor· Nov 27, 2024
Useful defaults in datadog-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Alexander Bansal· Nov 23, 2024
datadog-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Anika Sethi· Nov 23, 2024
datadog-cli fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Kabir Haddad· Nov 19, 2024
datadog-cli is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Min Malhotra· Oct 18, 2024
datadog-cli has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Meera Sharma· Oct 14, 2024
Useful defaults in datadog-cli — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Hassan Reddy· Oct 14, 2024
datadog-cli is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Kiara Iyer· Oct 10, 2024
datadog-cli fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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