Glasser Research logo

Glasser Research

Organization
OpenClaudia
glasser-research

Use Glasser to retrieve live marketing data through one prepaid account when a task needs SERPs, keyword metrics, ad libraries, community posts, company data, or another external API and no suitable free source or existing integration is available. Prefer the user's own integrations; Glasser fills data gaps and every run has a disclosed cost.

Overview

PublisherOpenClaudia
Repositoryopenclaudia-skills
Skill nameglasser-research
Stars
714
Forks
60
Bundled files
Instructions only
LicenseMIT
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 OpenClaudia on GitHub. Read the source before you install it.

Installation

Install the Glasser Research 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.
    https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/glasser-research
  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/OpenClaudia/openclaudia-skills.git /tmp/openclaudia-skills
mkdir -p .claude/skills
cp -r /tmp/openclaudia-skills/skills/glasser-research .claude/skills/glasser-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Glasser Research 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 Glasser Research 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 Glasser Research 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.

Glasser Research

Use Glasser to find and run paid third-party data API endpoints through one account. Glasser provides access and billing; the selected provider supplies the response. Use the provider's name when attributing data.

This is a data collection skill. Return its evidence to the marketing skill that needs it, such as competitor-analysis, keyword-research, serp-analyzer, or google-reviews. Keep that skill's analysis method and output format.

Source selection

Follow an explicit tool choice from the user. Otherwise use sources in this order:

  1. A suitable free tool already available in the agent.
  2. A working integration or API key the user already configured.
  3. Glasser for the specific remaining data gap.

Do not activate Glasser only because an API appears in its catalog. Use it when the task needs live data that the current environment cannot retrieve. A failed search can mean that the topic has no results; distinguish that from missing access before offering a paid fallback.

Setup

Check the CLI and authentication before planning a paid run:

bash
glasser --version
glasser balance

If the CLI is missing and the user asks to configure Glasser, install it with Node.js 22 or later:

bash
npm install -g @glasser-ai/cli@latest

For an interactive session, run glasser login (current setup instructions). Keep the command active while the user approves the matching code in the browser. Relay the fallback URL and code when the browser does not open. Never ask the user to paste a key into chat. After login, run glasser balance again in the environment that will make calls.

For unattended environments, the user can configure GLASSER_API_KEY through a secret manager. Do not write keys to project files, reports, command arguments, or chat. If the environment key overrides a saved login and fails, fix or remove that override instead of repeating login.

If the host exposes Glasser MCP tools, use balance, search, inspect, run, and runs_get instead. Follow the current MCP setup instructions for client setup.

Research workflow

  1. Define the evidence needed, target market, result volume, and freshness window. Avoid broad collection when a small result set answers the question.
  2. Run glasser search -q "<capability>". This searches the endpoint catalog, not the web or social platform. Endpoint descriptions are not research evidence.
  3. Compare relevant providers, then run glasser inspect -p <provider> -e <endpoint>. Read the current price, charge clauses, input schema, volume controls, and run mode. Never guess fields from a provider's direct API documentation because Glasser's contract can differ.
  4. Tell the user the endpoint, provider, request size, and expected cost. Get approval before spending unless the user already authorized this exact scope or an adequate task budget. Do not make speculative, repeated, or bulk calls.
  5. Write the inspected input as JSON to a task-specific temporary file. Use -f rather than interpolating user text into a shell command. Choose an unused output file so existing research is not overwritten.
  6. Run the endpoint. Use --wait for asynchronous work and -o for large output:
bash
glasser run -p <provider> -e <endpoint> -f <input.json> --wait -o <output.json>
  1. Read the provider response. COMPLETED means the provider answered; it does not guarantee a useful result. Missing fields stay unknown. Do not infer a zero value or invent a metric.
  2. Return the evidence to the calling marketing workflow. Report the provider, query and market, retrieval date, result limitations, final charge, and the private Glasser Run URL. Cite public source URLs from the provider response; the Run URL is an audit record for workspace members, not a public citation.

Common marketing data

These examples were inspected on 2026-09-12. Search and inspect again before a run because coverage, schemas, and prices can change.

NeedExample provider and endpointUse in OpenClaudia
Current Google resultsSerper /searchserp-analyzer, content briefs, competitor discovery
Google Ads keyword volumeDataForSEO /v3/keywords_data/google_ads/search_volume/livekeyword-research, content planning
Reddit posts or commentsScrapeCreators /v1/reddit/searchcustomer language, pain points, competitor sentiment
Google Ads advertisersScrapeCreators /v1/google/adLibrary/advertisers/searchidentify an advertiser before inspecting its ad endpoints
Other marketing dataSearch the live data source catalogcompanies, people, places, news, social posts, ads, and web pages

SERP example

After inspecting serper /search, write an input file such as:

json
{"q":"project management software","gl":"us","hl":"en","num":10}

Use returned organic positions, titles, snippets, and public links. A SERP result does not establish keyword volume, organic difficulty, traffic, or conversion.

Community language example

After inspecting scrapecreators /v1/reddit/search, write an input file such as:

json
{"query":"project management software complaints","timeframe":"month","sort":"top","filter":"posts"}

Preserve the post date, author, subreddit, URL, and engagement fields only when the provider returns them. Treat posts as opinions from their authors, not facts about the market. Retrieve comments only for selected posts and only within the approved budget.

Recovery and limits

  • For QUEUED or RUNNING, wait on the existing run with glasser runs get -r <runId> --wait; do not submit the same job again.
  • On an ambiguous timeout or dropped connection, retry once with the same Idempotency-Key. JSON mode and MCP run require an explicit UUID.
  • On rate limiting, wait for the returned retry delay and retry the same request once. Do not loop.
  • On insufficient balance, stop and ask the user to top up. Do not reduce the request in a way that changes the agreed research question without saying so.
  • Money values are exact decimal strings. Do not sum them using binary floats.
  • Send only the query and public identifiers needed for the approved task. Do not send private CRM exports, local files, or personal data unless the user clearly authorized that data and the selected endpoint requires it.

Frequently asked questions

What does the Glasser Research AI skill do?

Use Glasser to retrieve live marketing data through one prepaid account when a task needs SERPs, keyword metrics, ad libraries, community posts, company data, or another external API and no suitable free source or existing integration is available. Prefer the user's own integrations; Glasser fills data gaps and every run has a disclosed cost.

Why use Glasser Research on TypingMind?

Because you install it once and use it with any model. Glasser Research 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 Glasser Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/OpenClaudia/openclaudia-skills/tree/main/skills/glasser-research. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Glasser Research?

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 Glasser Research?

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

Is the Glasser Research AI skill free?

Yes. It is published on GitHub by OpenClaudia 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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