agentleFS
letscooktech-studioletscooktech-studio/ai-engineering-arsenal/skills/ai-search-optimizer/SKILL.md

ai-search-optimizer

Skill7 starsChanged 7 days agoMITView on GitHub

Improve source content for accurate discovery and citation by AI answer engines and search assistants. Use for documentation, product pages, knowledge bases, entity clarity, answerability, and measuring AI-search visibility without making ranking guarantees.

1 other project uses a version of this.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

2 rules. About 0 are common, 2 are distinctive, and 0 are very specific to this project.

  • ≈0

    Common · estimate

    Many files like this have a rule of this kind.

  • ≈2

    Distinctive · estimate

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Run controlled content improvements.

    line 18
  2. Distinctive:

    Do not promise placement in any AI answer product.

    line 26

≈ “Common” is an estimate: at least 1 in 5 of the 350,495 skill files we read have a rule of the same kind, not the same words. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

AI Search Optimizer

Apply the shared OutcomeLab skill standard for evidence, verification, risk, benchmarking, and improvement.

Make useful claims easy to retrieve, verify, quote, and keep current.

Workflow

  1. Define target questions, audiences, entities, and conversion actions. Inspect search and referral evidence when available.
  2. Audit whether authoritative first-party pages answer questions directly with dated claims, clear terminology, and supporting evidence.
  3. Improve information architecture, entity consistency, concise answer blocks, source links, authorship, freshness, crawlability, and structured data where appropriate.
  4. Build a measurement panel: referrals, branded/unbranded queries, assisted conversions, citation checks, and qualitative answer accuracy.
  5. Run controlled content improvements; avoid attributing changes to AI search without a comparison period.

Output contract

Return a query-to-page map, content gaps, prioritized edits, technical blockers, measurement plan, and risks. Mark speculative platform behavior separately from observed data.

Quality gates

  • Do not promise placement in any AI answer product.
  • Preserve factual accuracy; never add unsupported claims for retrieval.
  • Identify the canonical source for each important fact.

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.