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/brand-language-codifierWrote 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/brand-language-codifier)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/brand-language-codifier"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/brand-language-codifier/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/brand-language-codifier"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/brand-language-codifier.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 68 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00183 | $0.03021 |
| Opus 5 | $0.00092 | $0.01510 |
| Sonnet 5 | $0.00037 | $0.00604 |
| Haiku 4.5 | $0.00018 | $0.00302 |
Grade A, and why
brand-language-codifier 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 — 89 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand Language Codifier
Codifies the brand-level language canon — voice (register, tone spectrum, banned phrases, few-shot examples drawn only from the brand's own published material) and the naming tax (product / feature / tier naming rules, approved and banned terms) — as a dual-mode voice+naming step in the TALE Architect phase. It feeds two TALE-A sub-items directly: brand voice codified (register, tone, banned phrases, few-shots from own material only) and naming/lexicon tax defined (product/feature/tier naming rules, approved and banned terms). The voice rules it writes are the brand-level source the channel-registry voice-dossier.md adapts downward — channel voice points up to this canon, never redefines it — and its output seeds memory/events/narrative.ndjson via an authorized operation: propose request to registry-events.py for narrative-registry to promote into the canon. It works one lever — brand language — and hands off.
Scope guard: this skill produces voice and naming rules only. It does not author per-platform adaptations, finished copy, the upstream message hierarchy, claim truth, or TALE gates. Canon-grade output is submitted as a complete authorized Narrative proposal through registry-events.py; narrative-registry alone accepts it. Unresolved claims become separate claims proposals.
Quick Start
Codify the brand voice for [brand] from these published samples: [paste homepage, blog, docs, deck copy]. Give me register, tone spectrum, banned phrases, and few-shots.
Build the naming tax for [product]: rules for product / feature / tier names, plus an approved-terms and banned-terms table. Existing names: [list].
Run both modes — codify voice AND naming rules from our own material — and stage the result for the narrative-registry canon.
Skill Contract
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 · 89 lines · 183 tokens per session scan A b85a647be405
brand-language-codifier is a skill published in the GitHub repository aaron-he-zhu/aaron-marketing-skills (2,767 stars, last pushed yesterday), licensed Apache-2.0. It adds 183 tokens to every session and 3,021 once invoked, about $0.0009 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 2026-09-03.
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.