dd-logs▌
datadog-labs/agent-skills · updated Apr 8, 2026
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Search, process, and archive logs with cost awareness.
Datadog Logs
Search, process, and archive logs with cost awareness.
Prerequisites
Datadog Pup should already be installed. See Setup Pup if not.
Command Execution Order (Token-Efficient)
For scoped commands, use this order:
- Check context first (prior outputs, conversation, saved values).
- If a required value is missing, run a discovery command first.
- If still ambiguous, ask the user to confirm.
- Then run the target command.
- Avoid speculative commands likely to fail.
Quick Start
pup auth login
Search Logs
# Basic search
pup logs search --query="status:error" --from="1h"
# With filters
pup logs search --query="service:api status:error" --from="1h" --limit 100
# JSON output
pup logs search --query="@http.status_code:>=500" --from="1h"
Search Syntax
| Query | Meaning |
|---|---|
error |
Full-text search |
status:error |
Tag equals |
@http.status_code:500 |
Attribute equals |
@http.status_code:>=400 |
Numeric range |
service:api AND env:prod |
Boolean |
@message:*timeout* |
Wildcard |
Configuration APIs
Available log configuration commands in pup 0.42.0:
# List log archives
pup logs archives list
# List log restriction queries
pup logs restriction-queries list
# List custom log destinations
pup logs custom-destinations list
Common Processors
{
"name": "API Logs",
"filter": {"query": "service:api"},
"processors": [
{
"type": "grok-parser",
"name": "Parse nginx",
"source": "message",
"grok": {"match_rules": "%{IPORHOST:client_ip} %{DATA:method} %{DATA:path} %{NUMBER:status}"}
},
{
"type": "status-remapper",
"name": "Set severity",
"sources": ["level", "severity"]
},
{
"type": "attribute-remapper",
"name": "Remap user_id",
"sources": ["user_id"],
"target": "usr.id"
}
]
}
⚠️ Exclusion Filters (Cost Control)
Index only what matters:
{
"name": "Drop debug logs",
"filter": {"query": "status:debug"},
"is_enabled": true
}
High-Volume Exclusions
# Find noisiest log sources
pup logs search --query="*" --from="1h" | jq 'group_by(.service) | map({service: .[0].service, count: length}) | sort_by(-.count)[:10]'
| Exclude | Query |
|---|---|
| Health checks | @http.url:"/health" OR @http.url:"/ready" |
| Debug logs | status:debug |
| Static assets | @http.url:*.css OR @http.url:*.js |
| Heartbeats | @message:*heartbeat* |
Archives
Store logs cheaply for compliance:
# List archives
pup logs archives list
# Archive config (S3 example)
{
"name": "compliance-archive",
"query": "*",
"destination": {
"type": "s3",
"bucket": "my-logs-archive",
"path": "/datadog"
},
"rehydration_tags": ["team:platform"]
}
Rehydrate (Restore)
# No `pup logs rehydrate` command in pup 0.42.0.
# Use Datadog UI/API for rehydration workflows.
Log-Based Metrics
Create metrics from logs (cheaper than indexing):
# List log-based metrics
pup logs metrics list
# Get one metric by ID
pup logs metrics get api.errors.count
⚠️ Cardinality warning: Group by bounded values only.
Sensitive Data
Scrubbing Rules
{
"type": "hash-remapper",
"name": "Hash emails",
"sources": ["email", "@user.email"]
}
Never Log
# In your app - sanitize before sending
import re
def sanitize_log(message: str) -> str:
# Remove credit cards
message = re.sub(r'\b\d{4}[-\s]?\d{4}[-\s]?\d{4}[-\s]?\d{4}\b', '[REDACTED]', message)
# Remove SSNs
message = re.sub(r'\b\d{3}-\d{2}-\d{4}\b', '[REDACTED]', message)
return message
Troubleshooting
| Problem | Fix |
|---|---|
| Logs not appearing | Check agent, pipeline filters |
| High costs | Add exclusion filters |
| Search slow | Narrow time range, use indexes |
| Missing attributes | Check grok parser |
References/Documentation
How to use dd-logs 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 dd-logs
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches dd-logs from GitHub repository datadog-labs/agent-skills 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 dd-logs. Access the skill through slash commands (e.g., /dd-logs) 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
4.6★★★★★70 reviews- ★★★★★Jin Okafor· Dec 20, 2024
dd-logs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Evelyn Desai· Dec 16, 2024
dd-logs reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Dev Okafor· Dec 12, 2024
dd-logs fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Amina Zhang· Dec 12, 2024
dd-logs has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Evelyn Chawla· Dec 8, 2024
Registry listing for dd-logs matched our evaluation — installs cleanly and behaves as described in the markdown.
- ★★★★★Jin Ndlovu· Nov 11, 2024
Keeps context tight: dd-logs is the kind of skill you can hand to a new teammate without a long onboarding doc.
- ★★★★★Tariq Verma· Nov 7, 2024
We added dd-logs from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- ★★★★★Meera Ndlovu· Nov 3, 2024
I recommend dd-logs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Amina Diallo· Nov 3, 2024
Solid pick for teams standardizing on skills: dd-logs is focused, and the summary matches what you get after install.
- ★★★★★Zara Perez· Oct 26, 2024
Keeps context tight: dd-logs is the kind of skill you can hand to a new teammate without a long onboarding doc.
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