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SKILL.md

ads-plan

Agent skill fromAgriciDaniel/claude-ads

Create a professional paid-advertising strategy covering objectives, economics, platform selection, campaign architecture, audiences, budget, creative, measurement, experiments, governance, rollout, and reporting. Use for ad plan, media plan, PPC strategy, paid-social strategy, campaign architecture, advertising roadmap, or channel planning.

How to install ads-plan

install ads-plan
# install the claude-ads pack with the skills CLI
npx skills add AgriciDaniel/claude-ads

# or add only this skill to Claude Code: copy the folder

skills/ads-plan/

# into

~/.claude/skills/ads-plan/ (all projects)

.claude/skills/ads-plan/ (one project)

skills/ads-plan/SKILL.md

MITFull file
1# Paid Media Plan
2
31. Read the setup profile, business economics, evidence, current account state, and
4 relevant industry template as optional input.
52. Define objective, customer, offer, conversion, value, constraints, geography,
6 regulated category, time horizon, and success criteria.
73. Evaluate channel roles and exclusions from first principles; do not require every
8 supported platform. For a channel outside the twelve-platform contract, load
9 `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
10 its current buying, eligibility, measurement, and creative evidence is present.
114. Before proposing Meta budgets, learning-phase expectations, bidding,
12 consolidation, or performance forecasts, collect the account, Pixel, and
13 conversion cold-start dimensions per the contract in
14 `skills/ads-meta/SKILL.md`. When any dimension is cold or `unknown`, plan
15 for measurement validation, explicit creative hypotheses, staged reversible
16 tests, and confidence labels; do not apply mature-account benchmarks to
17 missing history.
185. Specify campaign architecture, audience strategy, creative system, budget and
19 pacing, measurement, experiments, policy controls, and operating cadence.
206. Phase prerequisites before launch, learning, optimization, and scale.
21…

Excerpt shown under the repository's MIT license.

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