How to use
- Copy the installation command and review the source package before installing it in your own agent environment.
- Check the source document for required tools, credentials and supported hosts.
- Describe your input and desired deliverable; review the output against the source material.
npx skills add https://github.com/agricidaniel/claude-ads --skill ads-planBefore you start
- Installation does not include a model subscription, API allowance or permission to access your files.
- Repository stars and installation counts describe activity, not an independent quality or security test.
Rendered from the source repository. Headings, examples, code, tables, links, and referenced images are preserved.
Paid Media Plan
- Read the setup profile, business economics, evidence, current account state, and
relevant industry template as optional input.
- Define objective, customer, offer, conversion, value, constraints, geography,
regulated category, time horizon, and success criteria.
- Evaluate channel roles and exclusions from first principles; do not require every
supported platform. For a channel outside the twelve-platform contract, load ads/references/additional-platforms.md (resolve it from the plugin root, the directory that holds ads/ and skills/, or from the skills directory in a standalone install) and return a research lead unless its current buying, eligibility, measurement, and creative evidence is present.
- Before proposing Meta budgets, learning-phase expectations, bidding,
consolidation, or performance forecasts, collect the account, Pixel, and conversion cold-start dimensions per the contract in skills/ads-meta/SKILL.md. When any dimension is cold or unknown, plan for measurement validation, explicit creative hypotheses, staged reversible tests, and confidence labels; do not apply mature-account benchmarks to missing history.
- Specify campaign architecture, audience strategy, creative system, budget and
pacing, measurement, experiments, policy controls, and operating cadence.
- Phase prerequisites before launch, learning, optimization, and scale.
- Assign every action an owner, timing, dependency, guardrail, evidence, success
measure, and rollback or exit condition.
- Return canonical JSON and render the requested human plan.
A plan is advisory. It becomes an account change only through launch or optimize mutation gates.



