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.
npx agentmods add skills/motion-creative/motion-creative-plugin/analyze-adnpx skills add Motion-Creative/motion-creative-plugin --skill analyze-adgit clone --depth 1 https://github.com/Motion-Creative/motion-creative-pluginWhat 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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00158 | $0.02268 |
| Opus 5 | $0.00079 | $0.01134 |
| Sonnet 5 | $0.00032 | $0.00454 |
| Haiku 4.5 | $0.00016 | $0.00227 |
Grade A, and why
Analyze Ad 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.
How it starts
The opening of the file, as written. The whole thing — 148 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analyze Ad
Deep-dive analysis of a specific creative — the most common customer interaction pattern. Users paste ad names one after another, going 20+ messages deep. Optimize for this multi-turn flow.
References
Read ${CLAUDE_SKILL_DIR}/../creative-strategist/references/evaluation-framework.md for the 4-Question Evaluation Framework, metric pattern correlations, Ready/Iterate/Rethink call, and performance context awareness.
Phase 0: Find the Creative
The user may reference a creative by:
- Exact ad name — match against
adNamein creative insights results - Approximate name — partial/fuzzy match, present candidates
- Ad ID — match against
adIdsarray - Creative asset ID — use
creativeAssetIdsfilter parameter - Description — "the UGC video with the woman unboxing"
Steps:
- Pull
get_creative_insightswith SPEND insightType, limit 15. IncludeincludeCreativeUrls: trueso the user can see the creatives. Start with a smaller set — if the creative is active, it's likely in the top 15 by spend. On the first pull, extractgoalMetricandspendThreshold. IfgoalMetric.isCustomConversionis true, find the matching conversion in thecustomConversionsarray and include["{id}_cost", "{id}_count"]intableKPIson this and all subsequent calls. - Search results for the user's reference (case-insensitive partial match on
adName). - Multiple matches → present all matches with ad name, spend, and top metric. Ask which one. Never guess.
- No match in top 15 → widen the search: re-pull with limit 50. If still no match, tell the user and suggest: "Can you share the exact ad name, ad ID, or creative asset ID from Motion? Sometimes names don't match exactly."
- Single clear match → proceed to analysis.
Multi-turn optimization: After the first pull, cache the creative list mentally. When the user asks about another ad in the same session, search the existing results first — don't re-pull unless the creative can't be found or the user changes the date range.
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.
- 3d ago First seen · 148 lines · 158 tokens per session scan A b9f9bc90ea2a
Analyze Ad is a skill published in the GitHub repository Motion-Creative/motion-creative-plugin (20 stars, last pushed 3mo ago), licensed MIT. It adds 158 tokens to every session and 2,268 once invoked, about $0.0008 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.
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