analyze

A skill for retrieving and examining advertising campaign performance data from connected ad platforms. It covers measures such as spending, clicks, conversions, return on ad spend, and search terms.

In plain words
What is it for?
Use it to review campaign, ad-group, ad, or keyword results; investigate budgets and ads that are not spending; and examine search terms or negative keywords. A connected platform is required.
Why use it?
It helps explain campaign results and delivery problems using platform data instead of guesswork.

Skill for Claude CodeCodex

Part of the ads plugin — 5 skills, 2 MCP servers shipped together

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/adkit/ads-mcp/analyze
Any agent
npx skills add adkit/ads-mcp --skill analyze
Clone the repo
git clone --depth 1 https://github.com/adkit/ads-mcp

Made for: Claude Code, Codex.

Or install ads, the plugin that ships this one along with the rest of its 5 skills, 2 MCP servers.

Per session 80 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 745 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00080 $0.00745
Opus 5 $0.00040 $0.00373
Sonnet 5 $0.00016 $0.00149
Haiku 4.5 $0.00008 $0.00075

Measured 3d ago against content hash 6d7c43a653e1, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

analyze scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/analyze/SKILL.md · 70 lines

How it starts

The opening of the file, as written. The whole thing — 70 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyze Performance

Requires a connected platform — run /ads:setup first if needed.

1. Fetch results

Use adkit_manage with entity: "results" and action: "list" on any connected platform:

adkit_manage platform: "<platform>" entity: "results" action: "list" params: { level: "campaigns", period: "30d" }

Common params (all platforms)

Param Description
level campaigns, adsets/ad-groups, ads (Meta also: no level = account; Google also: keywords)
period today, yesterday, 7d, 14d, 30d, this_month, last_month, this_quarter, last_year, maximum
from / to YYYY-MM-DD custom date range (use instead of period)
sort cost, clicks, impressions, conversions
limit Max rows to return

Use period for presets, or from + to for custom ranges. Don't combine both.

Platform-specific features

Available breakdowns, metrics, and additional actions vary by platform. Call adkit_help path: "manage <platform> results" for the full list. Key differences:

  • Meta: breakdown (day, age, gender, country, device), attribution-window, actions filter
  • Google: breakdown (device, network), plus separate search-terms, placements, and keywords actions
  • TikTok: dimensions and metrics pass through to TikTok reporting API

Filtering by entity

Filter results to a specific campaign or ad group:

Platform Param
Meta campaign: "<id>", adset: "<id>", ad: "<id>"
Google campaignId: "<id>", adGroupId: "<id>"
TikTok Uses dimensions — check adkit_help

2. Google-specific entities

Google Ads has additional entities beyond standard results:

Entity Action Use case
results search-terms Actual queries that triggered ads
results placements Where ads appeared (Display/YouTube)
keywords list Managed keyword list for an ad group
keywords create Add keywords (including negative match types)

Read the full file on GitHub · 70 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 3d ago First seen · 70 lines · 80 tokens per session scan A 6d7c43a653e1

Subscribe to this mod's changes

analyze is a skill published in the GitHub repository adkit/ads-mcp (12 stars, last pushed 6d ago), licensed MIT. It adds 80 tokens to every session and 745 once invoked, about $0.0004 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.