datadog-insights

datadog-insights is a skill for Claude Code, Codex from speakeasy-api/gram. It costs 17 tokens per session (4,750 once invoked), scanned A, original, AGPL-3.0.

A workflow for investigating Gram production health and posting a summary to Slack, a team messaging service.

In plain words
What is it for?
Checking Gram production health and posting a digest to Slack.
Why use it?
It brings production-health findings and their summary into one investigation and communication flow.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/speakeasy-api/gram/datadog-insights
Any agent
npx skills add speakeasy-api/gram --skill datadog-insights
Clone the repo
git clone --depth 1 https://github.com/speakeasy-api/gram

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for datadog-insights

README.md
[![agentmods](https://agentmods.dev/badge/skills/speakeasy-api/gram/datadog-insights.svg)](https://agentmods.dev/skills/speakeasy-api/gram/datadog-insights)
Your own site
<a href="https://agentmods.dev/skills/speakeasy-api/gram/datadog-insights"><img src="https://agentmods.dev/badge/skills/speakeasy-api/gram/datadog-insights.svg" alt="Measured on agentmods" height="20"></a>
Per session 17 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 4,750 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 1 finding. Scan, not verified.
Origin unknown No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00017 $0.04750
Opus 5 $0.00009 $0.02375
Sonnet 5 $0.00003 $0.00950
Haiku 4.5 $0.00002 $0.00475

Measured 5d ago against content hash 39c376ae15ba, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-05, from the pricing page.

Security

Grade A, and why

datadog-insights 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 5d 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.

import json, urllib.request, os, datetime
.claude/skills/datadog-insights/SKILL.md · 514 lines

The source is not reproduced here

Licensed AGPL-3.0

The repository is licensed AGPL-3.0, which this catalogue does not treat as permission to reproduce the file. Read it at the source.

Read it on GitHub

Changes

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.

  1. 5d ago First seen · 514 lines · 17 tokens per session scan A 39c376ae15ba

Subscribe to this mod's changes

datadog-insights is a skill published in the GitHub repository speakeasy-api/gram (266 stars, last pushed today), licensed AGPL-3.0. It adds 17 tokens to every session and 4,750 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.

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