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/analyze-performancegit 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 | $0.00011 | $0.00377 |
| Opus 5 | $0.00005 | $0.00188 |
| Sonnet 5 | $0.00002 | $0.00075 |
| Haiku 4.5 | $0.00001 | $0.00038 |
Grade A, and why
analyze-performance 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
Analyze Google Ads and GA4 performance: $ARGUMENTS
1. Pull data (AdLoop MCP)
get_campaign_performance— relevant date range (default: last 30 days)analyze_campaign_conversions— cross-referenced Ads + GA4 data with GDPR gap detection- If specific campaigns mentioned, filter by name
- If keywords are relevant, also pull
get_keyword_performanceandget_search_terms
Context tip: for the account-wide pass, call the performance tools with compact=true — you get totals, top-10 rows, and pre-computed offender lists instead of every row. Only switch to full mode when drilling into a specific campaign/keyword. In harnesses that support subagents, this whole data-pull step can be delegated to a subagent that returns just the summary.
2. Analyze
- Spend, Clicks, Conversions, CPA, CTR per campaign
- Paid vs organic comparison (from non_paid_channels)
- GDPR gap (clicks vs sessions ratio — 2:1 to 5:1 is normal in EU)
- Flag: zero conversions with significant spend, CPA > 3x target, QS < 5, wasteful search terms
If conversion issues found: run attribution_check
If landing page problems suspected: run landing_page_analysis
3. Present results
- Summary table of all campaigns with key metrics
- Highlight what's working and what's not
- Ranked list of recommended actions with priority and estimated impact
- If search terms show waste, quantify the amount and suggest negatives
Keep the GDPR consent gap in mind — never diagnose clicks > sessions as broken tracking without considering consent rejection first.
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 · 35 lines · 11 tokens per session scan A 9e404ca8fdb0
analyze-performance is a command published in the GitHub repository kLOsk/adloop (253 stars, last pushed 1mo ago), licensed MIT. It adds 11 tokens to every session and 377 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
seo-audit
Run a multi-agent SEO/discoverability audit of a page (URL or local .html), grounded in the Google docs KB, and write a scored cited report.
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
Show the current Google Analytics auth status for the ga-mcp-full MCP server.
auth-logout
Clear cached Google Analytics OAuth credentials for the ga-mcp-full MCP server.