Run Paid Campaigns Apply And Log

OrganizationPopular

gooseworks-ai

run-paid-campaigns-apply-and-log

Step of the run-paid-campaigns playbook: stages the week's approval items, applies only what the marketer approves and keeps the change log. Run run-paid-campaigns instead; not for direct use.

Overview

Publisher	gooseworks-ai
Repository	`goose-skills`
Skill name	`run-paid-campaigns-apply-and-log`
Stars	1.3K
Forks	219
Bundled files	1
License	MIT
Links	Skill source Repository Homepage

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.
1 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 Run Paid Campaigns Apply And Log 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/gooseworks-ai/goose-skills/tree/main/skills/marketing/run-paid-campaigns-apply-and-log
Enable the skill in any chat where you want it available.

Store

Installed

Skills

Add skills

Import one or more skills from a public GitHub folder or a local .skill or .zip archive, or SKILL.md file.

From GitHub URLFrom .skill, .zip, or SKILL.md

GitHub repo or folder

https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/run-paid-campaigns-apply-and-log

Points to a single skill folder, or any folder that contains multiple skills. Public repos only.

CancelContinue

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/gooseworks-ai/goose-skills.git /tmp/goose-skills
mkdir -p .claude/skills
cp -r /tmp/goose-skills/skills/marketing/run-paid-campaigns-apply-and-log .claude/skills/run-paid-campaigns-apply-and-log

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

Use it in TypingMind

Enable Run Paid Campaigns Apply And Log 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 Run Paid Campaigns Apply And Log 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 Run Paid Campaigns Apply And Log 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.

Stage, apply and log (atom)

Load: ../run-paid-campaigns/references/meta-levers.md, ../run-paid-campaigns/references/proposal-and-log.md (exact grammar). Steps by each move's lever (the playbook's Before you start 3): GooseWorks levers — 1–8; connector levers — 1, 2, 4, then 9–11; written instructions — 1, 2, 4 (no tool call), then 7 on the marketer's "done". Local mode has only the last two and calls no gooseworks: tool; its files live under gooseworks-local/<brand-slug>/ (host file tools).

Stage (step 7)

Re-read ads/campaigns/<slug>/state.md. If it doesn't exist, create it with the frontmatter (campaign, stage, goose_campaign_id, meta_push_ids: [], meta_campaign_id, updated_at, updated_by; local mode: no ids), the header lines, ## Open recommendations and ## Meta objects.
Write each open move as one line - R<n> (YYYY-MM-DD): <move> — evidence <…> — awaiting under ## Open recommendations (next free number; never reuse one), and its follow-up as - R<n> check YYYY-MM-DD: <rule> · trip-wire <…> under ## Checks due (the server deletes bullets on close; checks must survive).
GooseWorks levers only. For each Meta budget change, swap or launch without an id yet: gooseworks:propose_meta_budget_change / gooseworks:propose_meta_creative_replacement (with reason, evidence as report:<YYYY-MM-DD> or user request, recommendation_ref), or gooseworks:prepare_meta_ad_push (with goose_campaign_id) then push status until it awaits approval. Put each returned change id in its bullet; add each push id to meta_push_ids immediately — the server finds this folder only through that list or goose_campaign_id. Never re-propose an item that already has an id.
Write the proposal in proposal-and-log.md's one template (numbered moves, no ids) with the real approval links, the pause sentence and the cards; deliver it by its § Delivery, scheduled or not. Stop.

Apply (step 8)

Pauses: gooseworks:pause_meta_ad_push only on the marketer's own words in this turn naming the ads (user_instruction), with push_id and meta_ad_ids from ## Meta objects, and recommendation_ref. A bare "yes" → ask for the sentence. Report each ad's state after; anything not confirmed is named.
Links: read change or push status → approved / declined / waiting. Budget changes, swaps and pauses made with recommendation_ref: the server writes the decisions.md block and removes the bullet — never duplicate it. Launches / graduations (no recommendation_ref on prepare_meta_ad_push): when push status shows approved or declined, remove the bullet yourself and append a block with Evidence: push:<id> if none exists. Never say "done" early. A refused budget change (budget moved in Ads Manager): relay, re-read, never retry blind.
Instructions (cards), holds the marketer chose, declines given in chat, and check results (with the verdict word — worked / didn't work / too early / can't tell — in the Outcome line): re-read decisions.md immediately before writing, append a ### YYYY-MM-DD — <title> block (Recommended / Evidence / Decided / Outcome, one line each, date never earlier than the newest block), then delete that recommendation's bullet from state.md (write None. if the section empties).
Save the run record (../run-paid-campaigns/references/run-memory.md), each change id and push id copied into its money-move row.

Apply through a connector (any ads platform)

The rule is ../run-paid-campaigns/references/meta-levers.md § With an ads-platform connector; follow it exactly. Here only the bookkeeping: 9. Staged by steps 1, 2 and 4: each change is an open R<n> bullet and its card. No connector call yet. 10. Apply each one only on its named yes, by that rule. 11. Log after the read-back: re-read decisions.md, append a ### YYYY-MM-DD — <title> block (Recommended and Evidence as usual; Decided: the marketer's words; Outcome: applied , read back — the applied time the outcome check uses, or the failure) and delete the bullet from state.md. In local mode nothing else writes these files, so every block is yours.

Never: approve on the marketer's behalf; pause on your own judgement or a schedule; revert for a kill; break the connector rule; keep a ledger anywhere but these two files (the run record links them and adds only recipes, results and the scorecard).

Bundled files

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

skill.meta.json1 KB

Frequently asked questions

What does the Run Paid Campaigns Apply And Log AI skill do?

Step of the run-paid-campaigns playbook: stages the week's approval items, applies only what the marketer approves and keeps the change log. Run run-paid-campaigns instead; not for direct use.

Why use Run Paid Campaigns Apply And Log on TypingMind?

Because you install it once and use it with any model. Run Paid Campaigns Apply And Log 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 Run Paid Campaigns Apply And Log in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gooseworks-ai/goose-skills/tree/main/skills/marketing/run-paid-campaigns-apply-and-log. 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 Run Paid Campaigns Apply And Log?

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 Run Paid Campaigns Apply And Log?

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

Is the Run Paid Campaigns Apply And Log 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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Overview

Installation

Install in TypingMind

Install in another agent

Use it in TypingMind

Works with any AI model

Skill instructions

Stage, apply and log (atom)

Stage (step 7)

Apply (step 8)

Apply through a connector (any ads platform)

Bundled files

Frequently asked questions

Related AI Skills

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