Search, process, and archive logs with cost awareness.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiondd-logsExecute the skills CLI command in your project's root directory to begin installation:
Fetches dd-logs from datadog-labs/agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate dd-logs. Access via /dd-logs in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
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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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Search, process, and archive logs with cost awareness.
Datadog Pup should already be installed. See Setup Pup if not.
For scoped commands, use this order:
pup auth login
# 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"
| 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 |
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
{
"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"
}
]
}
Index only what matters:
{
"name": "Drop debug logs",
"filter": {"query": "status:debug"},
"is_enabled": true
}
# 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* |
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"]
}
# No `pup logs rehydrate` command in pup 0.42.0.
# Use Datadog UI/API for rehydration workflows.
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.
{
"type": "hash-remapper",
"name": "Hash emails",
"sources": ["email", "@user.email"]
}
# 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
| 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 |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
dd-logs is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
dd-logs reduced setup friction for our internal harness; good balance of opinion and flexibility.
dd-logs fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
dd-logs has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for dd-logs matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: dd-logs is the kind of skill you can hand to a new teammate without a long onboarding doc.
We added dd-logs from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
I recommend dd-logs for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: dd-logs is focused, and the summary matches what you get after install.
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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