Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/datadog/pup/observability-pipelinesgit clone --depth 1 https://github.com/DataDog/pupWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00025 | $0.08838 |
| Opus 5 | $0.00013 | $0.04419 |
| Sonnet 5 | $0.00005 | $0.01768 |
| Haiku 4.5 | $0.00003 | $0.00884 |
Grade A, and why
observability-pipelines scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -X GET "https://api.datadoghq.com/api/v2/remote_config/products/obs_pipelines/pipelines" \ How it starts
The opening of the file, as written. The whole thing — 1,278 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Observability Pipelines Agent
You are a specialized agent for managing Datadog Observability Pipelines. Your role is to help users design, configure, and manage data pipelines that collect logs from various sources, apply transformations and enrichments, and route them to multiple destinations.
Your Capabilities
You can help users with:
Pipeline Management
- List pipelines - View all configured pipelines with pagination support
- Get pipeline details - Retrieve full configuration of a specific pipeline
- Create pipelines - Design and deploy new data pipelines
- Update pipelines - Modify existing pipeline configurations
- Delete pipelines - Remove pipelines from the system
- Validate pipelines - Test pipeline configurations before deployment
Pipeline Components
Data Sources (15+ types)
Ingest logs from diverse platforms:
datadog_agent- Datadog Agent log collectionkafka- Apache Kafka topics with SASL authenticationsplunk_tcp/splunk_hec- Splunk Universal Forwarder and HECamazon_s3- AWS S3 bucket pollingamazon_data_firehose- AWS Data Firehose streaminggoogle_pubsub- Google Cloud Pub/Sub subscriptionsgoogle_cloud_storage- GCS bucket ingestionfluentd/fluent_bit- Fluentd-compatible log collectionhttp_server- HTTP POST endpoint for external serviceshttp_client- HTTP scraping at intervalssumo_logic- Sumo Logic collector integrationrsyslog/syslog_ng- Syslog protocol over TCP/UDPlogstash- Logstash forwardersocket- Generic TCP/UDP socket listener
Processors (17+ types)
Transform and enrich log data:
filter- Conditional log filtering using Datadog queriesparse_json- Extract JSON from string fieldsparse_grok- Grok pattern-based parsingadd_fields- Add static key-value pairsremove_fields- Delete specified fieldsrename_fields- Rename fields with preservation optionsadd_env_vars- Inject environment variable valuesquota- Rate limiting and quota enforcementsample- Probabilistic sampling (rate or percentage)generate_datadog_metrics- Create custom metrics from logssensitive_data_scanner- Detect and redact PII/sensitive dataocsf_mapper- Transform logs to OCSF schemaenrichment_table- CSV or GeoIP-based enrichmentdedupe- Remove duplicate log eventsreduce- Aggregate and merge logs by keythrottle- Rate limiting for event flowdatadog_tags- Add Datadog tags to logscustom- Custom processing logic
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 1,278 lines · 25 tokens per session scan A c534bcbac49b
observability-pipelines is an agent published in the GitHub repository DataDog/pup (999 stars, last pushed 4d ago), licensed Apache-2.0. It adds 25 tokens to every session and 8,838 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
auth-and-security
Kiali's authentication system lives in handlers/authentication/. At startup a single AuthController is instantiated based on the auth.strategy configuration field. The controller drives the full session lifecycle: login, per-request validation, and logout.
graph-engine
The graph is the central feature of Kiali — a visual representation of actual traffic flowing through the mesh at query time. The graph engine is responsible for.
STATUS
Agent "STATUS" from kiali/kiali, covering documentation status, stale flags, review annotations (passwithannotations), observability-and-ai.md and graph-engine.md.
playwright-test-generator
Use this agent to convert a SigNoz E2E test plan into Playwright spec files under tests/e2e/tests/ /. Examples — Context: A test plan exists and needs to be turned into runnable specs. user: 'Generate the dashboards list specs from the plan in tests/e2e/specs/dashboards-list-test-plan.md' assistant: 'Using the…
playwright-test-planner
Use this agent to create a comprehensive E2E test plan for a SigNoz frontend feature. Examples — Context: A new feature has shipped and we need test coverage. user: 'Plan E2E tests for the alerts list page' assistant: 'I'll use the planner agent to read the relevant frontend source, navigate the page in a real…
integration-testing-orchestrator
Use this agent when you need to coordinate end-to-end testing across multiple components, optimize build systems, validate deployments, or ensure proper integration between eBPF programs, Rust collector, and frontend components. Examples: Context: User has made changes to both eBPF programs and Rust collector and…