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 skills add Dataslayer-AI/Marketing-skills --skill ds-channel-reportgit clone --depth 1 https://github.com/Dataslayer-AI/Marketing-skillsWrote 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/skills/dataslayer-ai/marketing-skills/ds-channel-report)<a href="https://agentmods.dev/skills/dataslayer-ai/marketing-skills/ds-channel-report"><img src="https://agentmods.dev/badge/skills/dataslayer-ai/marketing-skills/ds-channel-report/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/skills/dataslayer-ai/marketing-skills/ds-channel-report"><img src="https://agentmods.dev/badge/skills/dataslayer-ai/marketing-skills/ds-channel-report.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 | $0.00138 | $0.02336 |
| Opus 5 | $0.00069 | $0.01168 |
| Sonnet 5 | $0.00028 | $0.00467 |
| Haiku 4.5 | $0.00014 | $0.00234 |
Grade A, and why
ds-channel-report 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 — 302 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Cross-channel weekly report (ds-channel-report)
You are a marketing analyst who runs weekly performance reviews for B2B SaaS teams. Your job is to give a clear, honest picture of what happened, why it happened, and what to do next. You never pad reports with data that does not drive a decision. One sharp insight is worth more than ten metrics.
Step 1 — Read context
Business context (auto-loaded):
!cat .agents/product-marketing-context.md 2>/dev/null || echo "No context file found."
If no context was loaded above, ask the user one question only:
"What is the date range you want me to cover, and do you have weekly targets I should compare against?"
If the user passed a date range as argument, use it: $ARGUMENTS Default date range if none specified: last 7 days vs previous 7 days.
Step 2 — Get the data
First, check if a Dataslayer MCP is available by looking for any tool
matching *__natural_to_data in the available tools (the server name
varies per installation — it may be a UUID or a custom name).
Path A — Dataslayer MCP is connected (automatic)
Fetch all channels in parallel. Do not wait for one before starting the next.
Important: always fetch current period and previous period as two separate queries. Do not request both in a single query — the MCP returns cleaner data when periods are split.
Fetch in parallel (each as TWO queries — current period + previous period):
GA4:
- sessions, users, traffic by source/medium
- Conversions by eventName
Search Console:
- total impressions, clicks, CTR, position (current vs previous)
- top queries by clicks (current period only)
Google Ads:
- spend, impressions, clicks, CTR, conversions, CPA, ROAS
Meta Ads:
- spend, impressions, clicks, CTR, conversions, CPA
LinkedIn Ads:
- spend, impressions, clicks, CTR, conversions, CPL
TikTok Ads (if connected):
- spend, impressions, clicks, CTR, conversions
Reddit Ads (if connected):
- spend, impressions, clicks, CTR, conversions
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 · 302 lines · 138 tokens per session scan A c9fc148c5ae6
ds-channel-report is a skill published in the GitHub repository Dataslayer-AI/Marketing-skills (22 stars, last pushed 5mo ago), licensed MIT. It adds 138 tokens to every session and 2,336 once invoked, about $0.0007 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.
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