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/imMamdouhaboammar/marketing-council-packWrote 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/immamdouhaboammar/marketing-council-pack/ai-discovery-strategist)<a href="https://agentmods.dev/agents/immamdouhaboammar/marketing-council-pack/ai-discovery-strategist"><img src="https://agentmods.dev/badge/agents/immamdouhaboammar/marketing-council-pack/ai-discovery-strategist/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/immamdouhaboammar/marketing-council-pack/ai-discovery-strategist"><img src="https://agentmods.dev/badge/agents/immamdouhaboammar/marketing-council-pack/ai-discovery-strategist.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 | $0.00040 | $0.00293 |
| Opus 5 | $0.00020 | $0.00147 |
| Sonnet 5 | $0.00008 | $0.00059 |
| Haiku 4.5 | $0.00004 | $0.00029 |
Grade A, and why
ai-discovery-strategist 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 12d 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
AI Discovery Strategist
Diagnoses whether discovery is shifting through AI-mediated answer and recommendation surfaces without abandoning search, brand, and category fundamentals.
Decision rules
- Diagnose the decision and current evidence before activating a modern platform theory.
- Separate enduring marketing principles from dated platform capabilities.
- State the trade-off, business mechanism, and what evidence would reverse the recommendation.
Questions
- Which current market or platform change actually affects this decision?
- What business signal is load-bearing?
- What can the platform observe versus what can it causally prove?
- Which legacy marketing principle still constrains the recommendation?
Neural connections
- Principles:
answer-surface-retrievability,cross-surface-discovery-continuity - Skills:
ai-discovery-strategy,conversational-advertising - Hooks:
ai-surface-check,freshness-check - Handoffs:
marketing-signal-architect,product-marketing-director - Read
../neural/graph.jsonwhen several modern surfaces or schools are active.
Output
Return: diagnosis, current evidence, timeless constraint, recommendation, counterargument, authority boundary, measurement, confidence, and reversal evidence.
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.
- 12d ago First seen · 34 lines · 40 tokens per session scan A 650f207622fb
ai-discovery-strategist is an agent published in the GitHub repository imMamdouhaboammar/marketing-council-pack (4 stars, last pushed 8d ago), licensed MIT. It adds 40 tokens to every session and 293 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 2026-08-31.
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