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/proof-point-packagerWrote 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/proof-point-packager)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/proof-point-packager"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/proof-point-packager/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/proof-point-packager"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/proof-point-packager.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk pass
- 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 67 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 | $0.03032 |
| Opus 5 | $0.00092 | $0.01516 |
| Sonnet 5 | $0.00037 | $0.00606 |
| Haiku 4.5 | $0.00018 | $0.00303 |
Grade A, and why
proof-point-packager 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 — 87 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proof Point Packager
Turns claims-ledger-approved proofs into reusable proof modules — stat cards, case snippets, testimonial blocks, and comparison proofs — each pinned to a message-house pillar and to the ledger claim ID it substantiates, then flags every pillar that makes a claim with no approved proof behind it. It sits in the Land phase of the TALE loop and feeds two dimensions in tale-benchmark.md: E (proof-point assets exist for each pillar — case, benchmark, demo, or testimonial the user has rights to) and L (proof points are placed where the claim is made — no claim on a surface without its proof). It is a supplier to the E1 evidence-integrity discipline downstream, never its adjudicator: it packages only what the ledger already approved and refuses to invent proof.
Scope guard: this skill packages existing approved proof only. It does not adjudicate or substantiate a claim (offer-claims-registry is the sole writer of memory/claims/claims-ledger.md — unverified proofs are marked [needs source] and routed to memory/events/claims.ndjson via an authorized operation: propose request to registry-events.py), fabricate a benchmark or statistic to fill an empty pillar (a missing proof is flagged, not invented), assemble the raw story units it draws from (story-bank-builder owns those), map proof onto each surface as a message-match spec (narrative-cascade-planner), or compute the TALE profile result (only narrative-quality-auditor scores TALE). It works one lever — proof packaging — and hands off.
Quick Start
Package proof points for [product] from the approved claims ledger. Pillars: [list or "all three"].
Build reusable stat cards and case snippets for each message-house pillar, each pinned to its claim ID.
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 · 87 lines · 183 tokens per session scan A f5fade009615
proof-point-packager 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 3,032 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.