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 Arvo-AI/aurora --skill datadoggit clone --depth 1 https://github.com/Arvo-AI/auroraWrote 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/arvo-ai/aurora/datadog)<a href="https://agentmods.dev/skills/arvo-ai/aurora/datadog"><img src="https://agentmods.dev/badge/skills/arvo-ai/aurora/datadog.svg" alt="Measured on agentmods" 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 | $0.00028 | $0.02205 |
| Opus 5 | $0.00014 | $0.01103 |
| Sonnet 5 | $0.00006 | $0.00441 |
| Haiku 4.5 | $0.00003 | $0.00220 |
Grade A, and why
datadog scanned grade A with 0 findings 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 7d 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.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 163 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datadog Integration
Overview
Datadog integration for querying observability data during Root Cause Analysis. Datadog is a REMOTE service. Use ONLY the query_datadog API tool. All data is accessed via a single unified tool with resource_type parameter.
Instructions
Tool Usage
query_datadog(resource_type=TYPE, query=QUERY, time_from=START, time_to=END, limit=N, interval=MS)
Resource Types
'logs'-- Search log entries. query=Datadog log query syntax e.g."service:web status:error"'metrics'-- Query metric timeseries (raw points). query=metric query e.g."avg:system.cpu.user{*}"'metric_stats'-- Percentile summary per series (p50/p95/p99/max/mean). Same metric query syntax as'metrics', but returns one compact row per series instead of raw points. Use this for capacity and right-sizing questions over long windows.'monitors'-- List monitors with status. query=name filter (optional)'events'-- Platform events. query=source filter (optional)'traces'-- APM spans/traces. query=span query e.g."service:web @http.status_code:500"'hosts'-- Infrastructure hosts. query=host filter (optional)'incidents'-- Datadog incidents. Lists active/recent incidents (requires Incident Management; may 403 if not enabled).
The interval Parameter
interval is the rollup granularity in milliseconds, and applies to 'metrics' and
'metric_stats'. Omit it and a granularity is auto-picked that keeps each series under
~1000 points -- a 30-day window auto-picks 3600000 (1 hour, 720 points). Values are
clamped to 60000..14400000. Datadog caps a series at 1500 points, so a long window
with a fine interval returns less than you asked for; prefer the auto-pick.
Percentiles
Datadog cannot compute a time-percentile. Do not attempt any of these -- every one is rejected or silently empty:
.rollup(percentile, 95, 3600)and.rollup(p95, 3600)--400 Unrecognized rollup method..rollup()accepts onlyavg,sum,min,max,count.p95:my.metric{...}-- returns200with zero series for gauges. ThepXX:prefix needs distribution metrics;kubernetes.cpu.usage.totalandkubernetes.memory.usageare gauges, so it can never apply to them.formula: "p95(a)"--400 function "p95()" does not exist.formula: "percentile(a, 95, 3600)"--percentile()exists but is a space aggregator: arguments 2 and 3 are group tags, not a percentile value and window.- scalar
aggregator: "percentile"or"p95"--400.
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.
- 7d ago First seen · 163 lines · 28 tokens per session scan A 37f1e716dde8
datadog is a skill published in the GitHub repository Arvo-AI/aurora (405 stars, last pushed 2d ago), licensed Apache-2.0. It adds 28 tokens to every session and 2,205 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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