aaron-marketing-skills is a collection of 120 AI-agent skills covering marketing work such as brand narrative, search optimization, social media, email, advertising, influencer campaigns, and launches. Marketers and agent users can install it as a plugin, use its portable skills, or run its described bot team. The catalogue entries are components of this marketing workflow.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/aaron-he-zhu/aaron-marketing-skillsnpx agentmods add skills/aaron-he-zhu/aaron-marketing-skills/placement-exclusion-managerWrote 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/aaron-he-zhu/aaron-marketing-skills/placement-exclusion-manager)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/placement-exclusion-manager"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/placement-exclusion-manager/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/aaron-he-zhu/aaron-marketing-skills/placement-exclusion-manager"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/placement-exclusion-manager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- NVIDIA SkillSpector pass
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 | $0.00142 | $0.02152 |
| Opus 5 | $0.00071 | $0.01076 |
| Sonnet 5 | $0.00028 | $0.00430 |
| Haiku 4.5 | $0.00014 | $0.00215 |
Grade A, and why
placement-exclusion-manager 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 10d 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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Placement Exclusion Manager
Builds the brand-safety and exclusion lists that gate spend before a campaign goes live — placement/site/app/channel exclusions, network opt-outs, and content-suitability and sensitive-topic blocks — then references the audience exclusion set from audience-segment-builder and packages the placements evidence the auditor reads to judge ROAS A1 (brand/placement safety). It hardens where ads are not allowed to run; it does not build the audiences you do target or the audience exclusions themselves, and it does not score the account or issue the A1 verdict.
Quick Start
Build brand-safety exclusion lists for [goal] on [platforms] before launch. Here is my placements report: [paste/path].
Set placement, network, and content-suitability exclusions for [account]; brand-safety constraints: [no politics/news/UGC, competitor sites, etc.].
Package the A1 brand/placement-safety evidence for the auditor from this placements + campaign export: [paths].
Skill Contract
Expected output: a placement/network exclusion list (sites, apps, channels, network opt-outs), a content-suitability & sensitive-topic block list, a reference to the audience exclusion set consumed from audience-segment-builder (not re-derived here), a packaged A1 evidence file (placements report + exclusion decisions with rationale), and the standard handoff summary.
- Reads: account/campaign goal and brand-safety constraints, exported placements report (own data — where ads served / could serve), campaign + search-terms report, and the targeted-audience set from audience-segment-builder when present.
- Writes: a user-facing exclusion plan, the A1 evidence file, and a reusable summary to
memory/ad/placement-exclusion-manager/. - Promotes: chosen brand-safety constraints, exclusion decisions, and any missing placements report to
memory/hot-cache.mdandmemory/open-loops.md; propose durable brand-safety rules as pending-decision items (never writedecisions.mddirectly). - Done when: placement/network exclusions are specified against a named goal; content-suitability + sensitive-topic blocks are listed; the audience exclusion set is referenced or noted as a dependency; and the A1 evidence file is packaged, or absent placements evidence leaves qualified A1 evidence Unknown and the run
NEEDS_INPUT. - Primary next skill: ad-account-auditor to score the full RQS and issue the A1 verdict on this 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.
- 10d ago Changed a008d7eeba77
- 13d ago First seen · 84 lines · 142 tokens per session scan A e1b90c167bc2
placement-exclusion-manager is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed today), licensed Apache-2.0. It adds 142 tokens to every session and 2,152 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.
Other skills, from other repositories
geo-visibility-check
One-shot GEO audit: does your brand appear in Claude, ChatGPT, and Gemini answers for the buyer questions that matter? Runs a prompt panel through the engines with citation tracing and reports per-prompt verdicts, who wins instead, and which sources the answers come from.
geo-optimizer-skill
Run geo audit first. It scores the site 0–100 across 8 categories and generates a prioritized action list.
geo-loop
Run one bounded eGEOagents loop iteration over a workspace domain - read the charter and fresh collector data, do ONE unit of work, write substrate artifacts, append one Timeline entry and one LOG line. Use for loop mode, /geo:loop, scheduled GEO runs, or continuous monitoring.
content-scoring
Score content against the 10 GEO criteria with evidence and prioritized fixes. Use when users ask to score, rate, evaluate, or estimate ranking strength.
competitive-analysis
Analyze AI-search competitors for a query and recommend ranking strategy. Use when users ask competitor analysis, who ranks, or competitive landscape.
schema-generator
Generate JSON-LD schema markup for pages and content types with an implementation checklist. Use when users ask for schema, structured data, rich snippets, or markup.