Agent skill

Ads North Star Strategy

by gooseworks-ai in gooseworks-ai/goose-skills

Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different…

MITAuto-check passedMarketing & SEO

Install Ads North Star Strategy

skills CLI
$ npx skills add gooseworks-ai/goose-skills --skill ads-north-star-strategy -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install gooseworks-ai/goose-skills ads-north-star-strategy --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/gooseworks-ai/goose-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/ads/composites/ads-north-star-strategy .claude/skills/ads-north-star-strategy && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
ads-north-star-strategy
GitHub stars
1.2k
Token cost
~5k tokens
SKILL.md length
3,011 words
Files
6 (incl. references)
Skills in repo
273
Repo updated
First seen
Licence
MIT

At a glance

Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different…

  • Works in 6 steps: Goal → objective (the honest-objective… → Optimization goal, test size, structure → Audience and placements → …
  • Tasks that involve Paid advertising
  • SKILL.md covers Where this skill's files are, Choose one mode, Purpose and Inputs, plus 6 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Ads North Star Strategy is an agent skill from gooseworks-ai/goose-skills. Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or…

Its SKILL.md is about 5k tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including reference files (for example `agents/openai.yaml`, `references/direct-meta-adapter.md` and `references/gooseworks-adapter.md`).

It sits in Marketing & SEO, covering Paid advertising and Product metrics. The repository describes itself as: Library of Growth & GTM skills + data APIs for Claude Code, Codex, Cursor to run ads, social, content, lead gen, seo and data scraping. The licence is MIT.

When your agent uses it

  • Tasks that involve Paid advertising
  • Tasks that involve Product metrics

Example prompts

  • “/ads-north-star-strategy”

Workflow steps

6 steps, taken from the step headings in SKILL.md.

  1. Goal → objective (the honest-objective rule)
  2. Optimization goal, test size, structure
  3. Audience and placements
  4. Creative plan: count, diversity, and the 3x rule
  5. Test frame and success metric
  6. Safety rules to propose

What it can do on your machine

Read from SKILL.md and the folder at commit c650c6d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Ads North Star Strategy loads about 5k tokens when it runs, and up to ~7.5k if it reads all its reference files. Until then it costs about 141 tokens; SKILL.md has 3,011 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~141
When it runs · the whole SKILL.md, loaded when a task matches
~5k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~7.5k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from gooseworks-ai/goose-skills at commit c650c6d, republished under its MIT licence (© gooseworks-ai). 3,011 words, ~4,991 tokens.

Download SKILL.mdSave it as .claude/skills/ads-north-star-strategy/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
ads-north-star-strategy
description
Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept. Decides what launch-meta-ad-campaign then executes, and re-reads the strategy after a deep check to show which assumption changed. Use it after intake and before any creative is generated or any campaign is built.

Ads North Star Strategy

What this is for. Intake produced facts: what the business sells, what a customer is worth, how long they take to buy, what the user wants from this money. This skill turns them into a strategy the user can read in a minute. Each choice comes with its reason, because the deep check re-reads this file weeks later and has to see what was assumed.

This skill decides. launch-meta-ad-campaign executes. The launch skill already holds the rules for what to optimize for, how big a test the budget buys, and how to structure the campaign. This skill applies those rules and records the answer. It does not restate them. When they change, they change in one place.

The three things most strategies get wrong:

  1. Optimizing for a cheaper action than the real goal. Traffic is cheaper than leads. Clicks are cheaper than both. A strategy that picks the cheap action because it is available has decided to buy the wrong people.
  2. Splitting a small budget. One audience with 2–3 genuinely different ads beats three audiences that each starve.
  3. Ads that differ in colour, not in argument. Three versions of one message teach nothing. The angles have to differ in what they say.

Where this skill's files are

Paths are relative to this skill's own folder, not your working directory (in GooseWorks: agent-config/skills/ads-north-star-strategy/). The harness docs contract ships with the meta-ad-manager skill and the Stage 2 and Stage 4 rules with launch-meta-ad-campaign; both are installed beside this one.

Choose one mode

Use the first mode that is available:

  1. GooseWorks when the GooseWorks tools are callable. Read references/gooseworks-adapter.md.
  2. Direct Meta when there is no GooseWorks, but a Meta token and ad account are available. Read references/direct-meta-adapter.md.
  3. Planning only when neither is available. Read references/planning-only.md.

"The adapter" below means the file for the mode you chose.

Purpose

Write strategy.md for one campaign: goal, audience, objective and placements, budget, creative plan, test frame, success metric and reasoning. Then propose safety rules for the user to accept. Use it after intake and before any creative is generated. Use it again after a deep check, to revise the strategy and say what changed.

<!-- shared:ads-north-star-strategy start -->
<!-- This block is maintained in ONE place and copied byte-for-byte into both the
     maintainers' source copy and the published goose-skills copy. Edit one, copy
     it to the other, and run the parity script before shipping either. -->

Inputs

InputWhere it comes fromIf missing
Brand facts: business type, unit economics (ACV or AOV, margin, LTV, CAC target), sales cycle, other channels, claims rules, safety limits, the user's familiarity with Metathe brand file intake wroteread brand research; ask only per the asking rule below
Campaign brief: the goal in the user's words, the offer, the ICP, the destination, total budget and durationthe campaign briefask
Product: what is sold, price pointthe catalog, by product idask which product
Account facts: lifetime spend, tracking present, weekly volume of the target eventread from the ad account (launch skill, Stage 2)assume a cold account with no tracking, and say so
Existing customers (a customer list or custom audience)the ad account's audiencesno exclusion; say so
Brand record ids: brand id, the coworker agent that owns the filesthe brand recordask the host; never invent one
What the executing host can launch: which objectives, and how many ads per ad setthe host's launch toolassume Traffic only, 2–3 ads
Deep-check findings (re-read only)the deep check's reportnot a re-read

The asking rule. Ask only when both are true: the answer cannot be inferred from research, the catalog or existing campaigns, and a wrong guess changes money, claims or legal exposure. State everything else as an assumption: "I'm assuming X, Y, Z. Tell me if any are wrong." Ask at most four questions per turn, in plain words.

Things that usually pass the rule at strategy time: the budget ceiling, what a lead or sale is worth (when the brief has no ACV/AOV), and whether a named result may be claimed. Things that never pass it: the objective, the optimization goal, the structure and the placements. Those are yours to decide.

Facts that came from intake, not from a tool. An account fact the user or intake stated (lifetime spend, weekly events) has no sync time. Write it as an assumption ("per intake, not yet read from the account"), never as an - Observed: line with an invented sync time. Once the account is read, the observation replaces the assumption.

Composed Atoms

  • launch-meta-ad-campaign: its Stage 2 tables (choose the optimization goal, size the test honestly, structure follows the budget) and its Stage 4 test design. Read them before deciding. Do not copy them.
  • meta-ads-analyzer: produces the deep-check findings that trigger a re-read.
  • The host's campaign-planning and creative-generation tools (the adapter names them).

Workflow

  1. Read the brand file, the campaign brief and the product. On a re-read, also read the current strategy.md, the deep-check findings and the campaign's decision log.
  2. Fill the gaps with the asking rule. Record every assumption. It goes into Reasoning and, if it matters, into the launch brief's "what I'm unsure about".
  3. Decide, in the order of the decision rules below. Write the "because" for each.
  4. Write strategy.md in the contract format (below). Increment version on any change.
  5. Show the user a one-minute summary in their words. For a novice, explain each term the first time. For an expert, drop the explanations and show the numbers.
  6. Propose the safety rules (decision rule 6) and ask the user to accept, change or decline each. Save the accepted ones as pre-approved rules in the campaign state. Record the proposal and the answer in the decision log, including declined rules.
  7. Hand off: concepts carry the angles → request creatives sized by the plan (3x) → the judge loop and the user pick the ads that run → launch-meta-ad-campaign.

Decision Rules

Apply them in order. Each writes its reason into the strategy.

1. Goal → objective (the honest-objective rule)

Decide the objective from the business outcome, not from what is easiest to buy:

The businessThe outcome that paysRight objective
Software, sales-led, cycle over ~30 days or ACV over ~$2kqualified pipeline: demo requests, sales callsLeads
Software, self-serve with a free trialtrial starts that activateLeads (a signup event), or Sales if paid checkout is the first step
E-commercepurchasesSales
Appinstalls that payApp promotion
"Does this work at all?"learning, not revenueTraffic, as a stated plumbing test

Say why the cheaper objective is wrong for this business. For a long-cycle software brand, Traffic buys people who tap ads. Those people rarely book a call and never become a $10k contract. A low cost per click hides the fact that nothing in the pipeline moved.

When the host cannot launch the right objective, do not quietly downgrade. Write three things, in this order:

  1. The right objective and why (above).
  2. The bridge the host can launch: a Traffic campaign that pays for landing-page views (people who arrive, not just tap) to the page where the real action happens. It is judged on cost per lead from the tracking, never on cost per click. Give it a stopping point: after the evidence threshold, if leads cost more than the target, stop.
  3. The limit, in one line: "To have Meta optimize for leads directly, the campaign has to be created in Ads Manager until the host can launch it."

Never write a strategy whose success metric is a click for a business that makes money on a lead or a sale.

The right objective and the right optimization goal are two different claims. Sales can be the right objective while the account is still too thin to optimize for purchases (under ~50 a week, launch Stage 2). Write both: "the right objective is Sales; at 6 purchases a week Meta could not optimize for them yet, so this run pays for landing-page views and is judged on cost per purchase."

Bridge stopping point. Stop the bridge early when it has spent 3× the target cost per result with zero results. Otherwise, judge cost per result against the target at the end of the run, or at the test frame's threshold if that comes first.

2. Optimization goal, test size, structure

Apply the three tables in launch-meta-ad-campaign Stage 2: "choose the optimization goal from those numbers", "size the test honestly" and "structure follows the budget". Record:

  • the optimization goal and the account fact that decided it (tracking present? weekly event volume?);
  • what this budget honestly buys (plumbing test, traffic test, or real-action optimization). The table has gaps between its rows. A spend that falls between two rows gets the lower row: $20–50 a day is still a traffic test for comparing ads, though it counts results for the campaign as a whole. If the budget cannot measure the success metric, say so in the strategy. Don't let the launch brief discover it;
  • one ad set unless there is a concrete business reason to split, such as prospecting versus remarketing, or countries that need separate money. Write the reason for one, or the reason for two.
3. Audience and placements
  • Audience: broad (location, age band, exclusions) on a cold or small account, and let delivery find the buyers. Limits the user stated, such as an age range, a gender or a country, are business choices: keep them. "Broad" means no interest targeting inside them. Use interests or lookalikes only when the account has the history to seed them. Always exclude existing customers when that list exists.
  • Placements: automatic placements by default. Restrict them only for a reason, such as no vertical (9:16) creative, a linked Instagram account that is wrong, or a claims rule that one surface breaks. The creative formats follow from the placements: 1:1 or 4:5 for feeds, 9:16 for Stories and Reels.
4. Creative plan: count, diversity, and the 3x rule
  • Ads that run = what the structure supports. At small budgets that is 2–3 per ad set. More ads split the learning. Fewer leave nothing to compare. If the host caps it, cap to the host.
  • Diversity: each running ad argues something different. Use a different angle (the reason to care: speed, cost, fear of missing out, proof, identity), not a different colour or crop. Name the angles. Keep format and destination the same, so the test has one variable.
  • Generate 3x. Creatives requested = 3 × ads that run. The judge loop throws out the weak ones, and the user chooses from the rest. Request it as the plan's count, so the tripling is explicit. If the user named a number of creatives, use their number as is and never triple it. Write both numbers, e.g. "3 ads run, chosen from 9 generated (3x rule)", or "user asked for 12; used as is".
  • Each angle becomes a campaign concept (person, message, proof). A claim goes into an ad only if it passes the brand's claims rules. When those rules allow no results or named customers, proof is a verifiable product fact from the rendered page: the price, what is included, how long it takes, the guarantee. Never a result.
Show full SKILL.md (1,172 more words)Show less
5. Test frame and success metric

Apply launch-meta-ad-campaign Stage 4: what is being compared, the single variable, how the budget splits, the metric that decides, and the evidence needed before judging. The success metric follows the goal from rule 1: cost per qualified lead, cost per purchase, cost per paying install. Measure it with what the tracking actually counts, e.g. a booked demo. When quality is judged downstream, e.g. the sales team qualifies demos, say how the two are reconciled: "cost per booked demo, checked weekly against the demos sales marked qualified". Give a threshold drawn from the unit economics, e.g. "a lead is worth it at up to $150, because at a 10% close rate and a $12k ACV, a customer at $1,500 pays back in the first two months." If the budget cannot reach the evidence threshold, the test frame says so.

6. Safety rules to propose

Propose these. Save them only after the user accepts:

RuleDefault thresholdWhy
Pause everything if the destination is downthe page fails to load on two checks in a rowevery tap on a broken page is paid for and lost
Pause an ad set that has spent X with zero resultsX = the larger of 2× the target cost per result and 3 days of its budgetpast that point the money is not buying learning; the 3-day floor stops a small budget from pausing before a normal gap between results

With one ad set, pausing the ad set pauses the campaign. Say so when you propose the rule. Stay inside the brand's safety limits, such as the maximum daily budget and excluded audiences. Budget changes are never pre-approved; they always go back to the user.

Re-reading after a deep check

The deep check brings evidence. The strategy says which assumption the evidence overturned, not just which number moved.

  1. Bump version, and set based_on: deep check YYYY-MM-DD.
  2. Judge each comparison against its own threshold. The test frame's evidence bar applies per comparison, not to the whole check. Two angles past the bar can be judged while a third that is below it stays unjudged. Change only what the evidence reaches.
  3. Add ## What changed after Reasoning. A bullet belongs there for either of two reasons: the evidence overturned an assumption vN stated, or it answered a question the test frame said this run would answer. Each bullet has four parts: the assumption or question, what version N said, the evidence (a tagged - Observed: line), and the new decision. A finding that only confirms the plan working does not belong here. Neither does a finding still below its bar: put it under a short "could not judge yet" line instead.
  4. Never cut an audience on a segment's average cost. "9 of 11 purchases came from 45–54" from a handful of results is the breakdown effect (see meta-ads-analyzer). Delivery was already concentrating on the buyers. Narrowing to that segment usually raises the cost. Leave the audience alone unless the segment finding clears the bar and a business reason agrees.
  5. A budget change is its own recommendation. Put it in the campaign state as a separate open recommendation (awaiting). It is never part of the strategy revision and never decided inside it.
  6. Append a decision-log entry for the revision. The user approves the new version like the first one.

If no comparison clears its bar, change nothing, and say what the check could not yet judge.

Output

ads/campaigns/<slug>/strategy.md, in the contract format (the meta-ad-manager skill's contract/templates/campaign/strategy.md), where the adapter keeps the ads/ files:

  • Frontmatter: campaign, product_ids (catalog references, never copies), version, updated_at, based_on.
  • Sections: Goal · Audience · Objective and placements · Budget · Creative plan · Test frame · Success metric · Reasoning, plus ## What changed on a re-read.
  • Goose's plan numbers are written plainly ("$20 per day for 14 days", "3 ads from 9"). Anything read from a tool goes on an - Observed: line with its window, sync time and source. Never call an observation current.
  • At most 8 KB. It is read in a minute, not studied.

The skill also writes, in the same turn:

  • The campaign state file: an open recommendation R<next free n>: approve strategy vN — awaiting, and, only once accepted, the safety rules under ## Pre-approved rules. On a first run, set stage strategy. On a re-read, leave the stage as it is: a live campaign stays live.
  • The decision log, which is append-only: one entry for the strategy (Decided: shown to the user; awaiting approval, Outcome: pending) and one for the safety-rule proposal and its answers. When the user answers, record it by filling that entry's - Outcome: pending line (the only line that may change), e.g. "approved v1 on 2026-09-28; launched". Never edit the Decided line afterwards. A re-read that finds the v1 entry still pending fills in its Outcome in the same way.
  • The campaign index row: its stage and next action. It creates the index or the campaign folder only when the orchestrator has not.
  • The brand file: only the facts the user answered during this skill. Other brand facts are intake's.
<!-- shared:ads-north-star-strategy end -->

Quality Checks

  • The objective follows the business outcome. If the host can't launch it, the bridge and the limit are written out.
  • Reasoning explains why the obvious cheaper alternative is wrong.
  • One ad set, or a written business reason for more.
  • 2–3 running ads with named angles that differ in message; the generated count is 3x, or the user's own number, and the strategy says which.
  • The success metric is the goal's result, with a threshold from the unit economics.
  • The budget's honest limit is stated when it cannot measure the metric.
  • Every assumption made in place of an answer is listed.
  • No safety rule was written as pre-approved before the user accepted it.
  • The file passes the contract validator.
  • On a re-read: the version is bumped, ## What changed names the assumption, and there is a decision-log entry.

Failure Modes

SymptomCauseFix
The strategy for a B2B brand optimizes for clicks and reports a great CPCThe objective was chosen from what the host can launchApply rule 1: name Leads, write the bridge judged on cost per lead, and state the limit
Three ad sets at $10/day eachSplitting by audience "to test audiences"Rule 2: one ad set; the test is between angles
Nine generated ads, all the same headline in different coloursDiversity read as visual varietyRule 4: angles differ in message; name them
Thirty creatives when the user asked for tenThe 3x rule was applied to the user's own numberTriple only the plan's count; a user's number is used as is
A safety rule in state before the user said yesProposal treated as acceptanceSave only accepted rules; log the answer
The re-read strategy just has new numbersEvidence recorded, assumption not named## What changed: assumption → old → Observed evidence → new decision
The strategy promises an answer the budget can't produceTest size not checkedLaunch skill Stage 2 "size the test honestly"; write the limit

© gooseworks-ai, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (references) in skills/ads/composites/ads-north-star-strategy of gooseworks-ai/goose-skills.

  • SKILL.md
  • agents/openai.yaml
  • references/direct-meta-adapter.md
  • references/gooseworks-adapter.md
  • references/planning-only.md
  • skill.meta.json

Open the folder on GitHubat commit c650c6d

Compare with similar skills

Ads North Star Strategy next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Ads North Star Strategy compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Ads North Star Strategy this skillgooseworks-ai/goose-skills1.2k—~5kAutomated safety check: PassMIT
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First 50 UsersAIDevGTM/gtm-cofounder310—~977Automated safety check: PassMIT
Growth StrategyOpenClaudia/openclaudia-skills713—~2.1kAutomated safety check: PassMIT
07 Marketing Report Globalminhnv0807/ai-business-skills609—~2.9kAutomated safety check: PassMIT
28 Community Building Globalminhnv0807/ai-business-skills609—~3.6kAutomated safety check: PassMIT

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Questions about Ads North Star Strategy

What does Ads North Star Strategy do?

Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different…. Ads North Star Strategy is an agent skill from gooseworks-ai/goose-skills. Turn a brand's facts, a campaign brief and the product into a one-minute Meta ad strategy with its reasoning — goal, audience, objective and placements, budget split, how many ads and how different they must be (generate 3x so the user can choose), the test frame, the success metric, and safety rules for the user to accept.

When should I use Ads North Star Strategy?

Ads North Star Strategy fits situations like: tasks that involve Paid advertising; tasks that involve Product metrics.

How do I install Ads North Star Strategy in Claude Code?

Run `npx skills add gooseworks-ai/goose-skills --skill ads-north-star-strategy -a claude-code`. Or copy the skill folder (skills/ads/composites/ads-north-star-strategy in gooseworks-ai/goose-skills) into .claude/skills/ads-north-star-strategy in your project. Claude Code loads it when a task matches its description.

How do I install Ads North Star Strategy in Codex?

Run `npx skills add gooseworks-ai/goose-skills --skill ads-north-star-strategy -a codex`. Or copy the skill folder (skills/ads/composites/ads-north-star-strategy in gooseworks-ai/goose-skills) into .agents/skills/ads-north-star-strategy in your project. Codex loads it when a task matches its description.

Can I use Ads North Star Strategy in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add gooseworks-ai/goose-skills --skill ads-north-star-strategy -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/ads-north-star-strategy, .gemini/skills/ads-north-star-strategy, .github/skills/ads-north-star-strategy and .opencode/skills/ads-north-star-strategy in your project.

What does Ads North Star Strategy need to run?

SKILL.md names no scripts, command-line tools or credentials: Ads North Star Strategy is instructions for the agent only.

Does Ads North Star Strategy access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Ads North Star Strategy safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Ads North Star Strategy use?

Ads North Star Strategy is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Ads North Star Strategy use?

About 5k tokens (SKILL.md is roughly 20k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2.5k tokens, read only when the agent opens those files.

What are the alternatives to Ads North Star Strategy?

Skills that share tags, products or a category with Ads North Star Strategy: Define Hypothesis (product-on-purpose/pm-skills, 716 stars), First 50 Users (AIDevGTM/gtm-cofounder, 310 stars), Growth Strategy (OpenClaudia/openclaudia-skills, 713 stars) and 07 Marketing Report Global (minhnv0807/ai-business-skills, 609 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Ads North Star Strategy?

gooseworks-ai (a GitHub organization) maintains it in gooseworks-ai/goose-skills, which has 1,240 GitHub stars. The repository holds 273 skills in this directory. The repository was last updated on October 8, 2026.

Source: gooseworks-ai/goose-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.