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.
git clone --depth 1 https://github.com/jjmartres/ai-coding-agentsWrote 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/commands/jjmartres/ai-coding-agents/datadog)<a href="https://agentmods.dev/commands/jjmartres/ai-coding-agents/datadog"><img src="https://agentmods.dev/badge/commands/jjmartres/ai-coding-agents/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/commands/jjmartres/ai-coding-agents/datadog"><img src="https://agentmods.dev/badge/commands/jjmartres/ai-coding-agents/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 | $0.00008 | $0.02123 |
| Opus 5.5 | $0.00003 | $0.00849 |
| Sonnet 5.5 | $0.00002 | $0.00425 |
| Haiku 4.5 | $0.00001 | $0.00212 |
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 1mo 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 — 293 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Datadog CLI Quick Reference
Commands Overview
| Command | Purpose |
|---|---|
logs search |
Search and filter logs |
logs agg |
Aggregate logs by facet |
logs tail |
Stream logs in real-time |
logs trace |
Find logs for a trace ID |
logs context |
Get logs around a timestamp |
logs patterns |
Group similar log messages |
logs compare |
Compare current vs previous period |
logs multi |
Run multiple queries in parallel |
metrics query |
Query timeseries metrics |
errors |
Quick error summary |
services |
List services with log activity |
Log Search
datadog logs search --query "<query>" --from <time> [--limit <n>] [--pretty]
Examples:
# All errors in last hour
datadog logs search --query "status:error" --from 1h --pretty
# Errors for a specific service
datadog logs search --query "service:api status:error" --from 1h --pretty
# HTTP 500 errors
datadog logs search --query "@http.status_code:500" --from 30m --pretty
# Errors containing specific text
datadog logs search --query "status:error connection refused" --from 1h --pretty
# Exclude certain services
datadog logs search --query "status:error -service:healthcheck" --from 1h --pretty
Log Aggregation
datadog logs agg --query "<query>" --facet <facet> --from <time> [--pretty]
Common facets: status, service, host, @http.status_code, @error.kind, @http.method
Examples:
# Count logs by status level
datadog logs agg --query "*" --facet status --from 1h --pretty
# Errors by service
datadog logs agg --query "status:error" --facet service --from 24h --pretty
# Requests by HTTP status code
datadog logs agg --query "service:api" --facet @http.status_code --from 1h --pretty
# Errors by error type
datadog logs agg --query "status:error" --facet @error.kind --from 6h --pretty
Live Tail (Real-time)
datadog logs tail --query "<query>" [--interval <seconds>] [--pretty]
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.
- 1mo ago First seen · 293 lines · 8 tokens per session scan A bc2e071bff01
datadog is a command published in the GitHub repository jjmartres/ai-coding-agents (48 stars, last pushed 4d ago), licensed MIT. It adds 8 tokens to every session and 2,123 once invoked, about $0.0000 per session on Opus 5.5. 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.
Other commands, from other repositories
resolve-review
Resolve reviewer feedback - from GitHub PRs, chat, or any reviewer.
implement.body
Every subagent invocation uses the delegation tool with the appropriate subagenttype (the implementation subagent, the tester subagent, or any specialized subagent the project ships). That tool is named Agent, in an interactive session and in the headless runtime alike. Do not look for a Task tool in your toolbox…
compete
NEVER workspace/[name].md (wrong) ALWAYS workspace/company/[name].md (correct).
ponytail
Switch ponytail intensity level (lite/full/ultra/off).
research-topic
Research a topic using multi-agent-researcher skill.
resolve-review.body
$ARGUMENTS.