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/klosk/adloop/diagnose-trackinggit clone --depth 1 https://github.com/kLOsk/adloopWhat 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 | $0.00012 | $0.00396 |
| Opus 5 | $0.00006 | $0.00198 |
| Sonnet 5 | $0.00002 | $0.00079 |
| Haiku 4.5 | $0.00001 | $0.00040 |
Grade A, and why
diagnose-tracking 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 2d 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.
What it actually says
Diagnose tracking and conversion issues for: $ARGUMENTS
1. GDPR check first
Before investigating anything technical, consider:
- Are Ads clicks > GA4 sessions? This is likely GDPR consent rejection (normal in EU, 2:1 to 5:1 ratio)
- State this upfront before deeper investigation
2. Attribution check
- Run
attribution_checkwith relevantconversion_events(e.g., sign_up, purchase, form_submit) - Review the
insights[]— the tool already factors in GDPR consent gaps - Only proceed to deeper investigation if insights suggest a real tracking problem
3. Codebase analysis
- Search for
gtag('event'anddataLayer.push({event:to find tracking code - Search for
gtag('consent'to check Consent Mode v2 implementation - Extract all event names from code
4. Validate tracking
- Run
validate_trackingwith the extracted event names - Compare results: matched (working), missing (in code but not firing), unexpected (in GA4 but not in code)
- Missing events = code not deployed or behind untriggered conditions
- Unexpected events = likely from tag managers
5. Landing page check
- Run
landing_page_analysisto check pages with traffic but zero conversions - If codebase is accessible, read flagged pages for UX issues
6. Diagnosis
- Only diagnose tracking as BROKEN when discrepancy can't be explained by consent
- Signs of real issues: zero sessions for ALL sources, organic also anomalous, events in code but never fire
- If tracking code needs to be added, use
generate_tracking_codeto produce the snippet
Present unified diagnosis: GDPR impact + tracking status + specific recommendations.
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.
- 2d ago First seen · 45 lines · 12 tokens per session scan A 5f07c7cbb6b2
diagnose-tracking is a command published in the GitHub repository kLOsk/adloop (255 stars, last pushed 1mo ago), licensed MIT. It adds 12 tokens to every session and 396 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.
Other commands, from other repositories
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google-docs
Answer a question from the local Google docs knowledge base (Search/SEO, Search Console, Ads, GA4) with citations to the original sourceurl.
setup
First-run setup for the ga-mcp-full MCP server — install the CLI if needed, then complete the browser login.
auth-login
Run the Google Analytics OAuth browser flow and cache credentials for the ga-mcp-full MCP server.
auth-status
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auth-logout
Clear cached Google Analytics OAuth credentials for the ga-mcp-full MCP server.