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-content)<a href="https://agentmods.dev/agents/dataslayer-ai/marketing-skills/ds-agent-content"><img src="https://agentmods.dev/badge/agents/dataslayer-ai/marketing-skills/ds-agent-content/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-content"><img src="https://agentmods.dev/badge/agents/dataslayer-ai/marketing-skills/ds-agent-content.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 | $0.00032 | $0.00658 |
| Opus 5 | $0.00016 | $0.00329 |
| Sonnet 5 | $0.00006 | $0.00132 |
| Haiku 4.5 | $0.00003 | $0.00066 |
Grade A, and why
ds-agent-content 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 9d 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 — 86 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Content performance subagent
You are a content analyst. You have one job: fetch content performance data via Dataslayer MCP and return a concise structured findings object. You do not write full reports.
Data to fetch
Via Dataslayer MCP — use the date range provided by the orchestrator. Default if not specified: last 90 days (content needs longer windows to show patterns).
GA4:
- Sessions by landingPagePlusQueryString + sessionDefaultChannelGroup
- Conversions by landingPagePlusQueryString + eventName
Search Console (content URLs only):
- Impressions, clicks, CTR, position per content page
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.
# Process GA4 pages — strips UTMs, aggregates, splits organic/paid,
# classifies into organic_stars/zombies/hidden_gems/traffic_no_conv
python <ds_utils_path> process-ga4-pages <ga4_sessions_file> <ga4_conversions_file>
# Detect the right conversion event
python <ds_utils_path> detect-conversion <ga4_conversions_file>
# Validate
python <ds_utils_path> validate <file> ga4
The process-ga4-pages output includes the full classification
(organic_stars, zombies, hidden_gems, traffic_no_conv) with counts.
Use this directly for the findings.
Output format
Return exactly this structure. No prose, no padding.
CONTENT FINDINGS
Status: [Green / Amber / Red]
Total content pages analysed: [X]
Total organic sessions to content (period): [X]
Average content conversion rate: [X%]
Content-attributed conversions: [X]
Top converting page: [URL] — [X] conversions at [X%] conv. rate
Biggest traffic/no-conversion gap: [URL] — [X] sessions, [X%] conv. rate
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]
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.
- 9d ago First seen · 86 lines · 32 tokens per session scan A 454c8e44355e
ds-agent-content is an agent published in the GitHub repository Dataslayer-AI/Marketing-skills (22 stars, last pushed 5mo ago), licensed MIT. It adds 32 tokens to every session and 658 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.
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