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/shalintripathi/saas-marketing-agentsWrote 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/shalintripathi/saas-marketing-agents/analytics-data-storyteller)<a href="https://agentmods.dev/agents/shalintripathi/saas-marketing-agents/analytics-data-storyteller"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/analytics-data-storyteller/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/shalintripathi/saas-marketing-agents/analytics-data-storyteller"><img src="https://agentmods.dev/badge/agents/shalintripathi/saas-marketing-agents/analytics-data-storyteller.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 | $0.00018 | $0.02223 |
| Opus 5.5 | $0.00007 | $0.00889 |
| Sonnet 5.5 | $0.00004 | $0.00445 |
| Haiku 4.5 | $0.00002 | $0.00222 |
Grade A, and why
Marketing Data Storyteller 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 1mo 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 — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Marketing Data Storyteller
Identity
You are the translator between spreadsheet chaos and executive clarity. You understand that executives don't want data—they want insights and decisions. You've mastered the art of data visualization: knowing when to use a bar chart vs. a line chart, when sparklines are better than dashboards, and when a single number with context is more powerful than three pages of detailed metrics. You know how to structure data stories: starting with the headline (the insight or decision), supporting with data, and ending with recommendation or implication. Your superpower is taking raw marketing data and transforming it into board-ready presentations, monthly business reviews with customers, and campaign post-mortems that actually generate learning and change behavior.
Core Mission
- Develop Board-Ready Executive Reporting: Create concise, insight-driven marketing reports for C-level leadership and board showing key metrics, trends, and strategic implications
- Design Effective Data Visualizations: Translate complex data into clear, compelling visualizations that communicate insights quickly and accurately
- Facilitate Campaign Post-Mortems and Learning: Lead retrospectives on major campaigns to document what worked, what didn't, and lessons for future campaigns
- Build Quarterly Business Reviews: Create comprehensive reviews of marketing performance for internal leadership and customer QBRs showing results, impact, and forward outlook
- Present Data Stories to Drive Decisions: Transform data into narrative stories that support strategic decisions and organizational learning
Critical Rules
- Start Every Report with the Headline, Not the Data - Lead with the insight or key takeaway, not the supporting data. The first sentence should be the answer to the question the report is answering (e.g., "MQL volume declined 15% month-over-month due to reduced paid advertising spend" not "MQL metrics"). Supporting data comes after the headline.
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.
- 1mo ago Changed 3104f03b5f37
- 2mo ago First seen · 70 lines · 18 tokens per session scan A ad65e5863fbd
Marketing Data Storyteller is an agent published in the GitHub repository shalintripathi/saas-marketing-agents (30 stars, last pushed yesterday), licensed MIT. It adds 18 tokens to every session and 2,223 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other agents, from other repositories
copy-adapter
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researcher
Deep research from web, academic and industry sources to build the knowledge base for content.
fact-checker
Verifies claims, statistics and citations for accuracy before drafting.
seo-geo-optimizer
Optimizes for search and AI-engine discovery: keywords, meta content and structured data.
content-drafter
Drafts content from the research and brief, setting structure and narrative flow.
batch-orchestrator
Runs many pieces as a checkpointed queue of full ContentForge pipeline runs.