Search Discoverability

Community

mia-ppp

search-discoverability

Assess buyer search coverage across traditional SEO, AI/answer engines and programmatic search. Use for SEO audits, rankings, AI citations, visibility gaps, scaled page opportunities, indexation and search/listing presence. Produce recommendations and briefs; content and technical deployment have separate owners.

Overview

Publisher	mia-ppp
Repository	`PMM-Skills`
Skill name	`search-discoverability`
Stars	76
Forks	2
Bundled files	14
License	MIT
Links	Skill source Repository

Markdown instructions
A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.
Works with any LLM
AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.
14 bundled files
Scripts, templates, and references the model can read while it works. Files are read-only and never executed.
Open source
Published by mia-ppp on GitHub. Read the source before you install it.

Installation

Install the Search Discoverability AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

Open the app and go to Plugins → Skills.
Choose "Install from GitHub".
Paste the skill folder URL below and confirm.
https://github.com/mia-ppp/PMM-Skills/tree/main/skills/search-discoverability
Enable the skill in any chat where you want it available.

Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills

git clone --depth 1 https://github.com/mia-ppp/PMM-Skills.git /tmp/PMM-Skills
mkdir -p .claude/skills
cp -r /tmp/PMM-Skills/skills/search-discoverability .claude/skills/search-discoverability

Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Search Discoverability in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Search Discoverability on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Search Discoverability is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

Loaded only when it is needed
The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.
Switch models mid-chat
Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Search Discoverability

Approved context and evidence

Read ../_shared/evidence-gaps.md and ../_shared/ssot-consumption.md. Load only relevant approved manifest context and record sources in Inputs and assumptions. Read ../_shared/segment-selection.md when an audience decision is needed; inherit accepted segments and ICP. Separate sources, interpretation, and proposals. Missing evidence remains Assumed or Gap. Do not edit SSOT. Route proposed canon changes to ssot-context-loop for human review and approval.

Modes

Mode	Load and assess
Traditional search visibility	Traditional search: buyer query coverage, intent, on-page relevance, crawl/index diagnosis, internal linking, cannibalization and search visibility.
AI/answer-engine visibility	Answer engines: documented query sets, platform/date, repeated observations, answer/citation visibility, citation-source analysis, extractable patterns and third-party presence.
Scaled/programmatic opportunities	Scaled search: defensible data, unique buyer value, template briefs, cannibalization, pilot selection and indexation monitoring.

Workflow

Map accepted buyers and evaluation questions to query sets and existing content. Distinguish branded, category, use-case and competitive discovery. Record platform, date, method and observation limits; one answer is not universal visibility.
Diagnose coverage, citations and technical impediments separately. Cite actual evidence; mark unavailable ranking/indexation data as Gap. Do not promise ranking or AI inclusion.
Prioritize recommendations by buyer value, confidence, cost and dependencies. For scale, require unique value and defensible data before a small pilot; stop expansion if quality, cannibalization or indexation guardrails fail.
Brief content-strategy, marketing-copy or competitor-alternatives for content; website-buyer-journey for buyer paths; web/SEO engineering for deployment. PMM approves truth and claims, not markup mechanics.
Specify leading coverage metrics and qualified acquisition outcomes with analytics-tracking, owner and review cadence.

Conditional references

Read technical markup guidance for structured-data requirements and examples. Engineering checks current eligibility, implements and validates. Read listing catalogue and tracker for relevant third-party search presence. Treat catalogue costs, traffic and policies as unverified leads until checked. No universal directory quota. Launch-platform coordination belongs to launch-strategy, customer review programs to customer-marketing, integration marketplaces to partner-marketing, and approved listing descriptions to marketing-copy.

Required output

Inputs and assumptions; mode/query scope; sourced visibility diagnosis; buyer-coverage and citation gaps; prioritized content/technical briefs; scale pilot and safeguards where applicable; owner handoffs; measurement plan; ranked Evidence gaps. Final content passes originating requirements and independent messaging review. This skill does not deploy pages or silently redefine strategy.

Related skills

content-strategy, marketing-copy, competitor-alternatives, website-buyer-journey, analytics-tracking, launch-strategy, customer-marketing, partner-marketing.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Search Discoverability AI skill do?

Why use Search Discoverability on TypingMind?

Because you install it once and use it with any model. Search Discoverability is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Search Discoverability in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mia-ppp/PMM-Skills/tree/main/skills/search-discoverability. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Search Discoverability?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Search Discoverability?

As many as you like. As long as a model supports skills, you can use Search Discoverability with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Search Discoverability AI skill free?

Yes. It is published on GitHub by mia-ppp under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Search Discoverability

Approved context and evidence

Modes

Workflow

Conditional references

Required output

Related skills

Bundled files

Frequently asked questions

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