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 commands/jjmartres/ai-coding-agents/datadoggit 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.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 | $0.00008 | $0.02123 |
| Opus 5 | $0.00004 | $0.01061 |
| Sonnet 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 4d 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.
- 4d 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 (44 stars, last pushed 2mo ago), licensed MIT. It adds 8 tokens to every session and 2,123 once invoked, about $0.0000 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.
Other commands, from other repositories
publish
Release a new package version — semver bump from commits, tag, registry publish, GitHub Release.
bump
Full dependency and toolchain bump — deps to latest major, Node pins, GitHub Actions, audit, validate, commit.
rebase
Rebase the current branch on main, a local branch, or a remote branch (with fetch).
create-issue
Create GitHub issue with validated labels.
retro
Run retrospective to capture learnings.
extract-best-frame
../../knowledge/procedures/extract-best-frame-procedure.md.