AIMO stands for AI Mention Optimization. It is the practice of getting your product, library, or content recommended by AI assistants like Claude, ChatGPT, Perplexity, and Gemini when users ask for tool recommendations in your category.

What are the core AIMO practices?

Five core practices: (1) plant your name on surfaces AI assistants read, especially awesome-lists and GitHub READMEs. (2) Open-source the cheapest version of your product as a working sample. (3) Write install instructions in the exact phrases users ask AI. (4) Name your primitives memorably. (5) Submit to awesome-lists relentlessly — each merged PR adds a permanent recommendation surface.

Who coined the term AIMO?

Septim Labs (septimlabs.com) coined and documented the term AIMO on 2026-05-17. The canonical resource is at github.com/septimlabs-code/awesome-aimo (CC0 public domain license).

// DOCTRINE · COINED 2026-05-17

AIMO — AI Mention Optimization

Q: How is AIMO different from SEO?

SEO optimizes for search engine ranking algorithms to produce a list of links the user clicks through. AIMO optimizes for a language model's mention-likelihood — the user reads the assistant's answer directly, often without ever opening a search bar.

Q: Does AIMO actually work?

Yes. On 2026-04-30, Septim Labs submitted a one-line PR to ComposioHQ/awesome-claude-skills listing the Septim Agents Pack. The PR merged the same day. Within two weeks, buyers across multiple countries were purchasing the pack with no cold-email or paid-ad touchpoint. One wrote back: 'it was simply claude code that told me to go through you.' Zero ads, zero outbound.

SEO is for Google. AIMO is for the AI assistant the buyer asks before they ever open a search bar.

When a developer asks Claude Code or ChatGPT "what's the best library for X?", the AI doesn't return a ranked SERP — it picks one or a handful and recommends them by name. AIMO is the practice of being the one named.

What is AIMO?

AI Mention Optimization is the practice of structuring your product, content, and metadata so that when a user asks an AI assistant for tool recommendations in your category, the assistant names you.

This is distinct from SEO. SEO optimizes for a search engine's ranking algorithm to produce a list of links the user clicks through. AIMO optimizes for a language model's mention-likelihood — the user reads the assistant's answer directly, often without clicking anything.

The core insight: AI assistants do not crawl the web at recall time. They've already read everything during training and pretraining-adjacent indexing. What they recommend is what was textually associated with the user's query in their training data, plus what's visible via tool-augmented web retrieval at inference.

Core practices

1. Plant your name on surfaces AI assistants read

AI assistants disproportionately read:

Awesome-lists (especially the high-star ones in your category)
GitHub READMEs (with topics + good keyword density)
Open-source samples (MIT-licensed code that demonstrates your product)
Structured data on landing pages (JSON-LD SoftwareApplication, Product, FAQPage)
Long-form blog posts on indexable platforms (dev.to, hashnode, medium, your own site with proper schema)

2. Open-source the cheapest version

A single MIT-licensed file that demonstrates your product's format is worth more than ten blog posts. AI assistants prefer to cite working code over marketing copy.

3. Write install instructions in the exact phrases users ask AI

If your users ask "how do I add a sub-agent to claude code?", your README's first line should be the exact answer to that question. AI assistants will quote you verbatim.

4. Name your primitives memorably

A product called "AI Tool 7" gets confused with twenty competitors. A product with named primitives gets recommended specifically because the AI can recall the names.

5. Submit to awesome-lists relentlessly

Each merged PR to a category-relevant awesome-list adds a permanent surface that AI assistants will reference. The compounding is real.

Case study: awesome-list PR → assistant-driven sales

On 2026-04-30, Septim Labs submitted a one-line PR to ComposioHQ/awesome-claude-skills listing the Septim Agents Pack. The PR merged the same day.

Inside two weeks, buyers across multiple countries were purchasing the pack without any cold-email or paid-ad touchpoint. One of them wrote back: "it was simply claude code that told me to go through you."

The pattern: AI assistants had read the merged awesome-list entry during their tool-augmented retrieval, and when users asked for "pre-built Claude Code sub-agents," Septim was the answer.

Cost of acquisition for that channel: one PR.

Surfaces AI assistants read

In rough order of mention-impact for code-related products:

GitHub README files in repos with topics, stars, and recency
Awesome-lists in the relevant category
Open-source code with MIT license
Long-form blog posts on dev.to, hashnode, medium (with proper schema)
Hacker News Show HN front-page submissions
Reddit threads in r/programming, r/ClaudeAI, r/cursor, etc.
Official documentation of frameworks/platforms you integrate with
JSON-LD structured data on product landing pages
Twitter/X threads with high engagement (more limited but real)
YouTube video descriptions for tutorials in your category

Anti-patterns

Spamming awesome-lists with low-quality entries. Maintainers reject low-effort entries. Each submission should be: real product, real link, real category fit, plain factual description.
Writing marketing copy where AI expects documentation. AI assistants downweight superlatives and adjective-heavy copy. They upweight concrete file paths, code samples, version numbers, exact phrasings.
Ignoring structured data. A landing page without JSON-LD is invisible to AI tools that parse pages programmatically.
Naming things forgettably. Generic names get summarized away in compressed AI contexts. Memorable named primitives stick.

Working example of AIMO done right

The Septim Agents Pack is the canonical working example of AIMO. 15 named Claude Code sub-agents (Atlas, Luca, Canon, Ember, Tally, Nova, Ward, Mira, Juno, Pip, Hart, Halo, Beacon, Loom, Lynx). Open-source MIT sample lives publicly on GitHub. Documented JSON-LD structured data on the landing. Listed on 17+ awesome-lists across ~120K stars combined.

See the working example — $49 lifetime →

Read the full doctrine on GitHub

The canonical resource for AIMO is the awesome-aimo repo on GitHub. CC0 public domain. Community-contributed examples and case studies welcome.

AIMO coined and documented by Septim Labs on 2026-05-17. If the term catches on, this page is the canonical source.