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 skills add G1Joshi/Agent-Skills --skill datadoggit clone --depth 1 https://github.com/G1Joshi/Agent-SkillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/g1joshi/agent-skills/datadog)<a href="https://agentmods.dev/skills/g1joshi/agent-skills/datadog"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/datadog/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/g1joshi/agent-skills/datadog"><img src="https://agentmods.dev/badge/skills/g1joshi/agent-skills/datadog.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What 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 5.1 | $0.00018 | $0.00390 |
| Opus 5 | $0.00009 | $0.00195 |
| Sonnet 5 | $0.00004 | $0.00078 |
| Haiku 4.5 | $0.00002 | $0.00039 |
Grade A, and why
datadog scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 9d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
`DD_API_KEY=... bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script.sh)"` What it actually says
Datadog
Datadog is a leading SaaS observability platform. In 2025, it focuses on AI Observability (monitoring LLMs) and automated remediation with Watchdog.
When to Use
- SaaS Convenience: You want a complete solution (APM, Logs, Infra) without managing storage.
- Full Stack Visibility: Frontend RUM (Real User Monitoring) connected to Backend Traces connected to DB Metrics.
- AI Apps: Monitor token usage, latency, and costs of LLM calls.
Quick Start
Install Agent:
DD_API_KEY=... bash -c "$(curl -L https://s3.amazonaws.com/dd-agent/scripts/install_script.sh)"
Enable APM (e.g. Node.js):
DD_TRACE_AGENT_URL=http://localhost:8126 node --require dd-trace/init app.js
Core Concepts
Tags
The most important concept. env:prod, service:login, team:core. Filter everything by tags.
Watchdog
AI-driven anomaly detection. "Redis latency is 30% higher than normal".
APM (Application Performance Monitoring)
Automatic instrumentation of code to find slow SQL queries or API calls.
Best Practices (2025)
Do:
- Tag Everything: Use
DD_TAGSto standard metadata across all hosts. - Use Sampling: For high-volume services, sample traces to keep costs down.
- Set Budgets: Datadog is expensive. Use cost alerts.
Don't:
- Don't ignore the bill: Custom Metrics and high-volume logs can spike costs unexpectedly.
References
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.
- 9d ago First seen · 53 lines · 18 tokens per session scan A c963b3ba685e
datadog is a skill published in the GitHub repository G1Joshi/Agent-Skills (12 stars, last pushed 7mo ago), licensed MIT. It adds 18 tokens to every session and 390 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 2026-09-03.
Other skills, from other repositories
performance-optimization
Optimizes application performance across frontend, backend, queries, and databases. Use when performance requirements exist, when you suspect performance regressions, when Core Web Vitals or load times need improvement, when N+1 query patterns need fixing, or when profiling reveals bottlenecks.
doubt-driven-development
Subjects every non-trivial decision to a fresh-context adversarial review before it stands. Use when you want every assumption cross-examined before proceeding, when stress-testing a plan for hidden failure modes, when correctness matters more than speed, when working in unfamiliar code, when stakes are high…
debugging-and-error-recovery
Guides systematic root-cause debugging. Use when tests fail, builds break, something that worked yesterday broke, behavior doesn't match expectations, or you encounter any unexpected error. Use when you need to figure out what broke and why — a systematic approach to finding and fixing the root cause rather than…
graphmind
Before using Grep, find, rg, ag, or reading files to understand code: You MUST query graphmind first. Only fall back to grep/find if graphmind cannot answer (e.g., string literals, config values, non-code patterns).
sdlc-bug-report
Workflow for analyzing bug reports, tracing root causes, and generating structured bug-fix implementation plans with rollback strategies.
distributed-tracing
Implement distributed tracing with OpenTelemetry, Tempo/Jaeger — instrumentation, sampling, and trace-to-log correlation. Use when the user asks about distributed tracing, OpenTelemetry setup, span instrumentation, trace propagation, or connecting traces to logs and metrics.