performance-analysis

performance-analysis is a skill for Claude Code from Motion-Creative/motion-creative-plugin. It costs 27 tokens per session (2,463 once invoked), scanned A, original, MIT.

A tool for examining how creative work performs across several measures and groups of people. It produces a report about what is working, growing, or declining.

In plain words
What is it for?
Use it to review advertising or other creative results over a chosen time period, compare audience groups, classify creative types, and identify ideas to improve or develop.
Why use it?
It brings performance information together so you can see patterns that a single measure might miss. It also turns the findings into suggested next actions.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; names the AskUserQuestion tool.

Runs only inside its plugin — its command needs a path that Claude Code sets for a plugin’s own hooks and for nothing else. Install the plugin, not this.

Part of the motion-creative plugin — 17 skills, 1 MCP server shipped together

Good fit Use it to review advertising or other creative results over a chosen time period, compare audience groups, classify creative types, and identify ideas to improve or develop.

Compare 6 skills from other repositories ↓
Install

Getting it into your agent

This one installs as part of its plugin. Adding the marketplace and installing the plugin brings it with everything else the plugin ships.

Claude Code
/plugin marketplace add Motion-Creative/motion-creative-plugin
Claude Code
/plugin install motion-creative

Made for: Claude Code.

Or install motion-creative, the plugin that ships this one along with the rest of its 17 skills, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for performance-analysis

README.md
[![agentmods](https://agentmods.dev/badge/skills/motion-creative/motion-creative-plugin/performance-analysis/github.svg)](https://agentmods.dev/skills/motion-creative/motion-creative-plugin/performance-analysis)
Your own site
<a href="https://agentmods.dev/skills/motion-creative/motion-creative-plugin/performance-analysis"><img src="https://agentmods.dev/badge/skills/motion-creative/motion-creative-plugin/performance-analysis/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for performance-analysis

Your own site · 80×15
<a href="https://agentmods.dev/skills/motion-creative/motion-creative-plugin/performance-analysis"><img src="https://agentmods.dev/badge/skills/motion-creative/motion-creative-plugin/performance-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 27 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,463 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
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 5.1 $0.00027 $0.02463
Opus 5.5 $0.00011 $0.00985
Sonnet 5.5 $0.00005 $0.00493
Haiku 4.5 $0.00003 $0.00246

Measured 1mo ago against content hash 69e1ad0d160a, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

performance-analysis 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 1mo 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.

Plugin/skills/performance-analysis/SKILL.md · 191 lines

How it starts

The opening of the file, as written. The whole thing — 191 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Creative Performance Analysis

Analyze creative performance using the creative-strategist skill methodology — multi-metric landscape, demographic overlay, and creative taxonomy. Produce an actionable report of what's working, what's scaling, and what's dying.


Phase 0: Orient

Before pulling data:

  1. Acknowledge: "I'll analyze your creative performance to find what's working, scaling, and dying."
  2. Detect complexity: Is this a quick question ("how's ROAS?") or a full deep dive ("what's working?")? Quick questions get 1-2 tool calls and a direct answer. Full analysis gets the multi-metric sweep.
  3. Ask if ambiguous: "Quick top-line or full deep dive? Any specific metric or time period you care about?"
  4. Connected workflow: After analysis, you can create concepts (/create-concepts), find iterations (/find-iterations), or dive into a specific ad (/analyze-ad).

If the user provides clear, specific intent (e.g., "full performance analysis for last 30 days"), skip questions and deliver.


Phase 1: Setup

1a. Parse Arguments

  • --datePreset: Time window for analysis. Default: LAST_30_DAYS. Options: TODAY, YESTERDAY, THIS_MONTH, LAST_MONTH, LAST_7_DAYS, LAST_14_DAYS, LAST_30_DAYS, LAST_90_DAYS.
  • --limit: Max creatives per metric query. Default: 10.
  • --metric: Optional focus metric (SPEND, SCALING, HOOK, CPC, CTR_ALL, PURCHASES, PURCHASE_VALUE, or the workspace's goalMetric). If provided, lead the analysis with this metric. If not, use the standard multi-metric approach.

1b. Load Settings & Auth

  1. Read ${CLAUDE_PLUGIN_ROOT}/motion-creative.config.md for org-specific configuration. If the file does not exist, use these defaults and suggest the user run /customize:
    • primary_kpi: use goalMetric from first get_creative_insights response
    • default_date_preset: LAST_30_DAYS
    • default_creative_limit: 10
    • demographic_focus: both
    • primary_metrics / secondary_metrics / exclude_metrics: auto-detect
    • priority_glossary_categories: use all
    • Brand guidelines: pull from get_workspace_brand
  2. Call get_auth_context() to resolve workspaceId (use settings workspace_id as fallback context).
  3. If settings contain a primary_kpi and no --metric was specified, use the primary KPI to lead the analysis.
  4. If settings contain target_demographics, weight demographic analysis toward those segments.
  5. If settings contain primary_metrics, ensure those metrics lead the analysis. If secondary_metrics, include after primary. If exclude_metrics, omit those from all queries and output.
  6. Use default_date_preset from settings as the datePreset for all calls unless the user provided a --datePreset argument. Use default_creative_limit from settings as the limit unless the user provided a --limit argument.

Read the full file on GitHub · 191 lines

Files

What ships with it

1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.

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. 1mo ago First seen · 191 lines · 27 tokens per session scan A 69e1ad0d160a

Subscribe to this mod's changes

performance-analysis is a skill published in the GitHub repository Motion-Creative/motion-creative-plugin (24 stars, last pushed 4mo ago), licensed MIT. It adds 27 tokens to every session and 2,463 once invoked, about $0.0001 per session on Opus 5.5. 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.

Related

Other skills, from other repositories

local-ai-agents

Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…

microsoft/ai-agents-for-beginners · 200 tokens

next-cache-components-adoption

Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…

vercel/next.js · 95 tokens

chat-pet-sprite-creation

Use when creating or changing VS Code chat pet sprite art, sprite sheets, state animations, eye treatments, Stable/Insiders variants, or pet transitions under src/vs/workbench/contrib/chat/browser/widget/media/chatPet.

microsoft/vscode · 53 tokens

cpu-profile-analysis

Analyze V8/Chrome CPU profiles (.cpuprofile) and DevTools trace files (Trace-.json). Use when: profiling performance, investigating slow functions, comparing code paths, finding bottlenecks, analyzing timeToRequest, understanding call trees from sampling profiler data, analyzing layout/paint/rendering, investigating…

microsoft/vscode · 71 tokens

insight-error-page

Write or audit an insight-kind error page for the Next.js dev overlay. Use when creating a new errors/ .mdx page, auditing an existing one, or checking that a page matches the framework fix cards. Covers page structure, title alignment, FixCard cards with Copy prompt button, code snippets, terminology verification…

vercel/next.js · 83 tokens

next-cache-components-optimizer

Optimize the meaningful UI available immediately from a Next.js route on an initial load (hard navigation) or named client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route and entry point at a time; the shipped test then guards…

vercel/next.js · 123 tokens