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Finding Google Skills

OrganizationPopular
google
finding-google-skills

Locates and loads the right Google product skill on demand from a remote catalog index, instead of preloading every skill. Use at the START of any request touching a Google product, API, or developer platform - including Google Cloud (GKE, Cloud Run, IAM, BigQuery, Vertex AI, Spanner), Google Ads, Google Analytics, Google Workspace (Gmail, Drive, Admin SDK), Chrome and Chrome extensions, Android, Firebase, YouTube, Google Maps, Gemini and the Gemini API, Google Play, and Flutter. Consult the index before answering from memory or searching the web. Don't use for non-Google products.

Overview

Publishergoogle
Repositoryskills
Skill namefinding-google-skills
Stars
20.1K
Forks
1.6K
Bundled files
Instructions only
LicenseApache-2.0
Links
  • 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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by google on GitHub. Read the source before you install it.

Installation

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

1

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.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. 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.
2

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/google/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/plugins/cloud/google-cloud-developer/skills/finding-google-skills .claude/skills/finding-google-skills
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Finding Google Skills 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 Finding Google Skills 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 Finding Google Skills 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.

Google Skill Finder

Routes a request to the published Google skills that apply to it. The catalog lives outside this file and is fetched on demand, so loading this skill costs almost nothing until a lookup actually happens.

Workflow

  1. Fetch the catalog byte-exactly. Retrieve https://raw.githubusercontent.com/google/skills/main/index.json with a raw shell fetch (curl, wget; curl.exe on Windows PowerShell). It must arrive byte-for-byte, every entrypoint URL intact and unaltered.

    With no shell fetch tool but Node present, node -e "fetch(process.argv[1]).then(r=>r.text()).then(t=>console.log(t))" {url} also returns bytes.

    The catalog is about 75 KB and may not fit in a single tool result; a truncated preview is alphabetical, so it reads as though only the first few products exist. Prefer narrowing it before reading. With jq: curl -sSL {url} | jq -r '.skills[] | select((.name+" "+.description)|test("gke";"i")) | "\(.name)\t\(.entrypoint)"'. In Windows PowerShell: (Invoke-RestMethod {url}).skills | Where-Object { $_.description -match "gke" } | Select-Object name, entrypoint -First 3. With neither, a plain grep -o over the raw JSON still isolates candidate names.

    Where no filtering tool exists, write the catalog to a file and read it in parts (curl -sSL {url} -o skills-index.json, or Invoke-WebRequest {url} -OutFile skills-index.json). This is often the better option regardless: it survives truncation, and re-reading a local file costs nothing. Delete it when the request is done.

    If only a summarizing fetch tool is available, phrase the request as extraction, not transcription: "List every entrypoint field in this document, one per line, exactly as written." Requesting it verbatim returns nothing usable.

  2. Confirm the retrieval worked before using it. A tool call that returns without raising is not a success. It succeeded only if the body parses as JSON and holds a skills array. A 404 page, an HTML error page, a TLS or connection error, an empty body, or anything that fails to parse is a FAILED retrieval even though the tool reported no error. A certificate failure is a FAILED retrieval and is final. Never retry it with verification disabled. Not curl -k or --insecure. Not -SkipCertificateCheck, and on Windows PowerShell 5.1, where that parameter does not exist, not the ServicePointManager certificate callback either. Not any equivalent in any language. You are about to follow instructions from whatever comes back, so an unverified catalog is worse than no catalog. On a failed retrieval, stop here and go to "When the fetch fails".

  3. Match the request against the descriptions. Every description states what the skill does, when to use it, and often when not to. Read them as routing criteria, not as summaries. Shortlist at most three entries whose description covers the request. When more than three look equally relevant, prefer the most specific over the more general.

  4. Fetch only the matches. Retrieve the entrypoint URL of each shortlisted entry, the same way, and follow that skill's instructions. Do not fetch entries that merely look related.

  5. Report an empty result honestly. If no description covers the request, say that no published Google skill applies and continue without one. Never invent a skill name or an entry point URL.

Routing ends once the matches are fetched. From the point you begin following a fetched skill's instructions, this skill is finished with the request and is not re-entered for it.

Rules

  • Fetch once per session; never keep it past the session. Reusing a catalog you retrieved successfully earlier in this session is fine. Carrying one into a later run is not, in any form: the catalog changes regularly and a stored copy goes stale silently. Session reuse never substitutes for a failed fetch.

  • Never carry the catalog beyond the request. A working copy on disk while you filter it is fine. Keeping it as a saved reference, or summarizing it back into the conversation, is not. It exists so the full text of 100-plus skills does not have to be carried in context.

  • Prefer the fetched SKILL.md over prior knowledge. The catalog is generated from the skills as they are published, so an entry point is the current text even when it contradicts what you remember.

  • Do not treat this skill as a prerequisite. If a specific Google skill is already loaded and covers the request, use it directly.

When the fetch fails

Reached from step 2. Work through these in order, stopping at the first that succeeds:

  1. Retry once with curl -sSL. If the first attempt used a summarizing fetch tool or hit a transport error, this alone usually fixes it.

  2. List the repository tree instead. Run

    bash
    curl -sSL 'https://api.github.com/repos/google/skills/git/trees/main?recursive=1'

    and read the paths ending in SKILL.md. Each is a candidate. Fetch the two or three whose directory names best match the request from https://raw.githubusercontent.com/google/skills/main/{path}, checking each one the way step 2 describes.

  3. Say so in the reply. If neither worked, state plainly that you could not reach the Google skills catalog and are answering without it. One line is enough, and it belongs in the reply to the user, not only in your reasoning.

A failed retrieval is never licence to answer as though it had succeeded. Until you have parsed a skills array in this session you do not know which skills exist: do not name one, do not describe one, and do not state that none applies. Recalling a skill from memory and presenting it as a catalog result is the worst outcome available, because nothing in the reply distinguishes it from a real lookup.

Frequently asked questions

What does the Finding Google Skills AI skill do?

Locates and loads the right Google product skill on demand from a remote catalog index, instead of preloading every skill. Use at the START of any request touching a Google product, API, or developer platform - including Google Cloud (GKE, Cloud Run, IAM, BigQuery, Vertex AI, Spanner), Google Ads, Google Analytics, Google Workspace (Gmail, Drive, Admin SDK), Chrome and Chrome extensions, Android, Firebase, YouTube, Google Maps, Gemini and the Gemini API, Google Play, and Flutter. Consult the index before answering from memory or searching the web. Don't use for non-Google products.

Why use Finding Google Skills on TypingMind?

Because you install it once and use it with any model. Finding Google Skills 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 Finding Google Skills in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/google/skills/tree/main/plugins/cloud/google-cloud-developer/skills/finding-google-skills. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Finding Google Skills?

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 Finding Google Skills?

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

Is the Finding Google Skills AI skill free?

Yes. It is published on GitHub by google under the Apache-2.0 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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