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/ivanovzlatan2/data-arsenal-v1Wrote 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/ivanovzlatan2/data-arsenal-v1/ga4-audit)<a href="https://agentmods.dev/commands/ivanovzlatan2/data-arsenal-v1/ga4-audit"><img src="https://agentmods.dev/badge/commands/ivanovzlatan2/data-arsenal-v1/ga4-audit/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/ivanovzlatan2/data-arsenal-v1/ga4-audit"><img src="https://agentmods.dev/badge/commands/ivanovzlatan2/data-arsenal-v1/ga4-audit.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 | $0.02599 |
| Opus 5 | $0.00004 | $0.01300 |
| Sonnet 5 | $0.00002 | $0.00520 |
| Haiku 4.5 | $0.00001 | $0.00260 |
Grade A, and why
ga4-audit 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 11d 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 — 230 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ga4-audit - Health Checks
Runs health checks on a GA4 property. Uses cloud API (50 checks) or local Data API (20 checks), depending on setup mode.
Usage
/ga4-audit <property>
<property> can be: numeric property ID, project slug, client name, or URL. See Property Resolution below.
Steps
Step 0: Language & setup
Language check: Read ~/.config/data-arsenal/config.json.
- If
languageis set → use that language for all commentary and recommendations. Data labels and GA4 metric names stay in English. - If
languageis NOT set (first run) → output as plain text (NOT AskUserQuestion): "What language should I use? (English / Bulgarian / other)" Wait for user response, interpret it, save to config.json so it's never asked again.
Property resolution: Resolve <property> to a numeric property_id:
- If numeric → use as property_id directly
- If matches a project slug in
~/.config/data-arsenal/projects/→ read property_id from that project's config.json - If neither → run
resolve_property()from_ga4_lib.pyto search GA4 account by name - If still not found → ask user to clarify
Pre-flight questions — Ask all upfront before running anything (AskUserQuestion, single round):
a. Ecommerce: "Is this an ecommerce property? (yes / no / auto-detect) [default: auto-detect]"
Map to --ecommerce flag value.
b. PDF report: "Generate a PDF report? (yes / no) [default: yes]"
If yes, will run with --format pdf which outputs both markdown to stdout and PDF to ~/Desktop/.
Step 1: Run the audit script
Find the script in order:
~/.config/data-arsenal/scripts/ga4-audit(post-setup, primary)scripts/ga4-audit(CWD is the repo)- If neither exists, tell the user to run
/ga4-setupfirst
Read ~/.config/data-arsenal/config.json to check the mode field:
- If
modeiscloud: script calls dataarsenal.com API (50 checks) - If
modeislocalor missing: script runs 20 local checks
SCRIPT=""
if [ -x ~/.config/data-arsenal/scripts/ga4-audit ]; then
SCRIPT=~/.config/data-arsenal/scripts/ga4-audit
elif [ -f scripts/ga4-audit ]; then
SCRIPT=scripts/ga4-audit
fi
if [ -z "$SCRIPT" ]; then
echo "ERROR: Script not found. Run /ga4-setup first."
exit 1
fi
FORMAT="markdown"
# If user said yes to PDF:
# FORMAT="pdf"
$SCRIPT <property_id> --lang <language_code> --ecommerce <auto|yes|no> --format $FORMAT
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.
- 11d ago First seen · 230 lines · 8 tokens per session scan A 01e41b024d21
ga4-audit is a command published in the GitHub repository ivanovzlatan2/data-arsenal-v1 (5 stars, last pushed 5mo ago), licensed MIT. It adds 8 tokens to every session and 2,599 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 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.