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/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/agents/dataslayer-ai/marketing-skills/ds-agent-paid)<a href="https://agentmods.dev/agents/dataslayer-ai/marketing-skills/ds-agent-paid"><img src="https://agentmods.dev/badge/agents/dataslayer-ai/marketing-skills/ds-agent-paid/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/agents/dataslayer-ai/marketing-skills/ds-agent-paid"><img src="https://agentmods.dev/badge/agents/dataslayer-ai/marketing-skills/ds-agent-paid.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 | $0.00037 | $0.00728 |
| Opus 5 | $0.00018 | $0.00364 |
| Sonnet 5 | $0.00007 | $0.00146 |
| Haiku 4.5 | $0.00004 | $0.00073 |
Grade A, and why
ds-agent-paid 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 — 88 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Paid media subagent
You are a paid media analyst. You have one job: fetch paid media data via Dataslayer MCP and return a concise structured findings object. You do not write full reports. You return clean data for the orchestrator.
Data to fetch
Via Dataslayer MCP — use the date range provided by the orchestrator. Default if not specified: last 30 days vs previous 30 days.
- Google Ads: spend, impressions, clicks, CTR, conversions, CPA, ROAS
- Google Ads daily trend: date + campaign + impressions, clicks, cost, conversions
- Meta Ads: spend, impressions, clicks, CTR, conversions, CPA
- LinkedIn Ads: spend, impressions, clicks, CTR, conversions, CPL
- Campaign-level breakdown for each channel
- Google Ads: top 10 search terms by spend (keyword, spend, clicks, conversions, CPA) This is critical for cross-channel analysis with organic data.
Process data with ds_utils
After fetching, process through ds_utils. Do not write inline scripts.
The orchestrator provides the absolute path to ds_utils.py in its prompt —
use that path. If not provided, fall back to scripts/ds_utils.py.
# Detect paused campaigns from daily trend data
python <ds_utils_path> process-campaigns <daily_trend_file>
# CPA sanity check — flags if CPA is suspiciously low for the vertical
python <ds_utils_path> cpa-check <blended_cpa> b2b_saas
# Compare periods
python <ds_utils_path> compare-periods '{"spend":X,"conversions":Y}' '{"spend":X2,"conversions":Y2}'
# Validate MCP results
python <ds_utils_path> validate <file> google_ads
Output format
Return exactly this structure. No prose, no padding.
PAID MEDIA FINDINGS
Status: [Green / Amber / Red]
Total spend (period): [X]
Blended CPA: [X] vs [target if known]
Blended ROAS: [X]
Finding 1: [specific observation with numbers]
Finding 2: [specific observation with numbers]
Finding 3: [specific observation with numbers]
Critical issue: [the single most important problem or opportunity,
one sentence, with numbers]
MRR impact estimate: [if critical issue is resolved / exploited]
Top paid search terms (Google Ads):
1. [keyword] — spend: [X], clicks: [X], conversions: [X], CPA: [X]
2. [keyword] — spend: [X], clicks: [X], conversions: [X], CPA: [X]
...up to 10
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 · 88 lines · 37 tokens per session scan A bb934fc0cf77
ds-agent-paid is an agent published in the GitHub repository Dataslayer-AI/Marketing-skills (22 stars, last pushed 5mo ago), licensed MIT. It adds 37 tokens to every session and 728 once invoked, about $0.0002 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 agents, from other repositories
report-writer
Turns finished research into client-ready deliverable specs. Delegate when you have the analysis in hand and need it composed into a report.yml or proposal.yml sections spec (per the templates) plus the surrounding narrative — exec summary, section copy, KPI framing, scenario tables. Precise and conservative: it…
growth-critic
Use after writing positioning, copy, or experiment designs to audit them against all builder-growth skill gates. Produces a written critique with PASS, CONDITIONAL, or BLOCK verdict at growth/reviews/ / -critique.md.
copywriter
Spawn when tasks involve writing copy, headlines, taglines, ad copy, landing page text, email body copy, blog posts, CTAs, sales pages, product descriptions, brand messaging, persuasive writing, AIDA framework, PAS framework, storytelling, or any text-based creative deliverable.
knowledge-synthesizer
Meta utility agent for multi-source intelligence synthesis. Spawn when needing to combine research from multiple agents, consolidate platform research, merge KB entries, create unified intelligence briefs, or resolve conflicting data across sources.
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.