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
This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.
/plugin marketplace add aaron-he-zhu/aaron-marketing-skills/plugin install aaron-marketingWrote 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/fit-scorer)<a href="https://agentmods.dev/skills/aaron-he-zhu/aaron-marketing-skills/fit-scorer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/fit-scorer/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/fit-scorer"><img src="https://agentmods.dev/badge/skills/aaron-he-zhu/aaron-marketing-skills/fit-scorer.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- Socket pass
- Snyk warn
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 | $0.00096 | $0.03686 |
| Opus 5 | $0.00048 | $0.01843 |
| Sonnet 5 | $0.00019 | $0.00737 |
| Haiku 4.5 | $0.00010 | $0.00369 |
Grade A, and why
fit-scorer 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 — 113 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Fit Scorer
Score each shortlisted creator on the typed STAR Suitability (S) dimension, then keep deal-specific commercial fit in a separate prioritization matrix. Suitability includes the STAR-S8 brand/category and audience-brand evidence that is independent of any single deal; deal terms, availability, and campaign orchestration stay outside it. The commercial matrix is not a Suitability score and never enters the SQS.
Quick Start
Score one influencer:
Score @[handle] for [brand/campaign] and tell me if they're a good fit
Compare and rank a shortlist:
Compare and rank these influencers for [campaign]: @influencer1, @influencer2, @influencer3
Skill Contract
- Reads: brand/campaign context, target audience definition, campaign goal, and shortlist entries carrying a stable opaque
creator_refplus either transient handles/profile URLs or resolvable opaque handle refs (supplied by the user or carried over frominfluencer-discovery). Optional prior audience profiles frommemory/influencer/audience-mapper/, competitor partner benchmarks frommemory/influencer/competitor-tracker/, and a WARM Campaign Retro Card'sevidence_refsplusnext_campaign_hypothesiswhen the user supplies or authorizes that handoff. For rostered creators, read partnership history and audience-stat provenance frommemory/creators/<aggregate-id>.md— the creator-registry roster record — as Partnership Potential inputs. - Writes: return the typed Suitability (S) read and separately labeled commercial-fit comparison inline by default; when a Retro Card is supplied, preserve its hypothesis as a separately labeled next-cycle test constraint with no score or verdict effect. Save the report to
memory/influencer/fit-scorer/YYYY-MM-DD-<topic>.mdonly with exact WARM-save authorization. Saved reports and handoffs retain the stable opaquecreator_refand opaque evidence refs, never a raw handle, name, profile URL, email, provider ID, or deterministic hash increator_ref. - Promotes: only with separate exact authorization, promote evidence-backed top picks and their exact Suitability (S) read and catalog version to
memory/hot-cache.md; never promote an unscored/provisional result or the Retro Card's qualitative decision/hypothesis as scored truth. - Done when:
- Every creator has all 10 Suitability items
S1–S10explicitly Pass/Partial/Fail/Unknown/N/A with dated evidence or a gap reason. - Every creator's stable opaque
creator_refis preserved from discovery/registry or generated once for this lineage; raw identity locators remain transient. - The typed goal/context and the Suitability item states are preserved for the gate; Unknown prevents a Suitability read.
- Any commercial-fit ranking is visibly separate from the Suitability read and cannot override a veto or missing evidence.
- If a Retro Card is supplied, its
next_campaign_hypothesisis visible only as a falsifiable test constraint/commercial-matrix context; itsevidence_refsare pointers for fresh investigation, not STAR item evidence or an automatic selection rule.
- Every creator has all 10 Suitability items
- Primary next skill: campaign-planner — turn the ranked shortlist into an approved campaign plan. If that plan is already approved and outreach-ready, hand off to outreach-manager instead; competitor benchmarking is optional.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · +6 lines 70449e24699e
- 13d ago First seen · 107 lines · 96 tokens per session scan A 7f413edf8cac
fit-scorer 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 96 tokens to every session and 3,686 once invoked, about $0.0005 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.