Spend Gate logo

Spend Gate

OrganizationPopular
gooseworks-ai
spend-gate

Shared step of the marketing playbooks (make-static-ads, research-market, run-paid-campaigns, work-with-creators): gets the human's yes before any credits are spent. Shows the price exactly as quoted, or asks for one yes to a credit ceiling for paid data reads, stays under it and logs each charge. Also covers scheduled runs. Loaded by those playbooks; not for direct use.

Overview

Publishergooseworks-ai
Repositorygoose-skills
Skill namespend-gate
Stars
1.3K
Forks
219
Bundled files
2
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.

  • 2 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by gooseworks-ai on GitHub. Read the source before you install it.

Installation

Install the Spend Gate 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/gooseworks-ai/goose-skills/tree/main/skills/marketing/spend-gate
  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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/marketing/spend-gate .claude/skills/spend-gate
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Spend Gate 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 Spend Gate 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 Spend Gate 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.

spend-gate

No credit is spent before the human's yes in this conversation. Money is always a credit number exactly as GooseWorks gives it — never dollars, never your own total presented as a price. Never name tools, endpoints or vendors to the marketer: describe the work in plain words ("Reddit threads about dog allergies, newest first").

Pick the branch by the tool:

A. Engine-quoted work (ads, photos, video, any tool with a dry run or a price check)

Managed marketing data: marketing_data_describe is free. marketing_data_lookup defaults to quote_only: true. Read the returned maximum credits and expiry, then request approval for that exact bounded work. Submit the same request with its quote token, the approved ceiling and the user’s words. A new request or expired quote needs a fresh quote. An existing approval may cover later calls only when it explicitly named that same bounded plan and total ceiling: quote each call, preserve the original approval words, and set its maximum no higher than the remaining approved credits. Ask again only for changed scope or more budget. Log every returned charge and deduct it before the next call; the tool’s per-call cap is not a cumulative plan limit. Free continuation pages inside that approved plan need no second spending approval. A capped quote is a maximum, not a claim about final usage. Never treat discovery, a successful quote or a fixture as proof that a paid data read ran.

  1. Ask the tool for its quote (its dry run, quote_only, or gooseworks:data_price_provider for fal and ElevenLabs calls).
  2. Show each quote exactly as given. Several quotes are shown one by one, never added up by you.
  3. Ask for a yes to that quote. Submit only after it; a changed plan needs a new quote and a new yes.
  4. If an answer comes back as a card waiting for a press, follow the server's card rule and wait for the human.

B. Proxy data reads (no price check exists) — one yes

Covers gooseworks:data_call_provider / gooseworks:data_post_provider with ScrapeCreators, gooseworks:competitor_research with mentions, and capped imports (gooseworks:knowledge_sync, gooseworks:knowledge_import with a source, gooseworks:ads_library_scrape for Google). Endpoint paths, query keys and recency options: proxy-endpoints.md in this folder.

Paid reads deepen a free result; they are never the starting point. Offer them only after the playbook's free pass, in the same message as the draft answer (../marketing-guide/research-pattern.md (in the marketing-guide skill)), and ask once.

  1. Read the price history: research/credits-log/<YYYY-MM>.md for this month and last (the scope in the marketing-guide skill §2).
  2. Plan the top-up with a cap on every row (pages per query, items per page, comments per post, profiles per list) and say in plain words what it adds to the free result.
  3. Estimate and ask one yes to "up to N credits", labelled an estimate:
    • with history: N from the logged charges for these kinds of reads;
    • with no history: never invent a price. Say what the top-up adds and roughly how many reads it takes, that the first read shows the real price, and let the human name the ceiling in the same yes ("up to how many credits?"); with no number given, run only the probe call and report its price before anything else. The first call runs as the probe inside that same ceiling — never a separate yes. Caps that tools take in their own unit (a few import tools take a small dollar cap) are described in words — "a small capped import" — and the credits charged are reported afterwards; never quote the dollar figure.
  4. Run inside it. Run the probe call first and keep its results. If the real per-call charge means the plan cannot fit, trim the plan to fit the ceiling — never cross it; ask again only if the human wants more. Then the cheapest and most decisive rows first; stop a row when it saturates. Calls that depend on earlier results (comments on the posts a search finds) fit inside the same ceiling. A playbook may reserve part of the ceiling for a named purpose (research-market's crisis reserve for its listening).
  5. Log every charge as it comes back, in research/credits-log/<YYYY-MM>.md (same scope as step 1; read it, add the row, write the whole file back): date | playbook | tool/endpoint | query kind | items | credits "Query kind" describes the read ("brand name search, TikTok, past month"), never a private person's name or contact.
  6. Stop at the ceiling. If the next call would cross it, stop. A failed call is retried once only when its error says that is safe; a bad path or key is logged, never guessed again.
  7. Report: credits charged against the ceiling, in plain words. Endpoint detail stays in the log.

If the human declines or trims the top-up, the free result stands; name what the paid reads would have added as a gap ("not read: TikTok comments — top-up not approved").

C. Scheduled runs (automations)

  • Create every automation paused. Switch it on only on the human's yes in this conversation.
  • The yes names any standing per-run data ceiling ("up to N credits per run"); the run writes that ceiling into its prompt and logs every charge. Without one, a scheduled run uses free sources only and never spends on data.
  • The coworker's own run cost is not priced in advance: say so, and after the first run show the credits it used (gooseworks:automation_list) so the human can pause it.
  • A yes given in chat never carries over to a scheduled run unless it was given as a standing per-run ceiling.

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 Spend Gate AI skill do?

Shared step of the marketing playbooks (make-static-ads, research-market, run-paid-campaigns, work-with-creators): gets the human's yes before any credits are spent. Shows the price exactly as quoted, or asks for one yes to a credit ceiling for paid data reads, stays under it and logs each charge. Also covers scheduled runs. Loaded by those playbooks; not for direct use.

Why use Spend Gate on TypingMind?

Because you install it once and use it with any model. Spend Gate 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 Spend Gate in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/spend-gate. 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 Spend Gate?

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 Spend Gate?

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

Is the Spend Gate AI skill free?

Yes. It is published on GitHub by gooseworks-ai 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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