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/competitor-watchnpx skills add Motion-Creative/motion-creative-plugin --skill competitor-watchgit 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 | $0.00062 | $0.05890 |
| Opus 5 | $0.00031 | $0.02945 |
| Sonnet 5 | $0.00012 | $0.01178 |
| Haiku 4.5 | $0.00006 | $0.00589 |
Grade A, and why
competitor-watch 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 — 538 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Competitor Watch — Weekly Competitive Intelligence
A self-contained weekly competitive scan that any Motion customer can run. Tracks competitor ad strategies, messaging shifts, creative patterns, and new launches — then compares against last week's baseline to surface what actually changed.
No external context required. This skill uses only the Motion MCP and stores its own baselines. No repo, no strategy docs, no internal knowledge base needed.
Core principle: The value is in the delta, not the state. "Foreplay launched 6 new Lens-focused ads this week" is intelligence. "Foreplay has 41 active ads" is a fact sheet. Every finding answers: "What should I pay attention to this week?"
Baseline model: First run establishes a baseline. Every subsequent run compares current state to the previous week's baseline, reports what changed, and updates the baseline. Baselines are stored locally as markdown files.
Phase 1: Setup
1a. Parse Arguments
--competitor: Optional. A domain (e.g.,foreplay.co) to scan a single competitor. If omitted, scan all saved competitors.--baseline-only: Establish baselines without producing a delta report. Use for first run.
1b. Resolve Workspace
Call get_auth_context().
If multiple workspaces exist, ask the user which one to scan. Store the workspaceId for all subsequent calls.
1c. Load or Create Competitor Watchlist
Check for a saved watchlist at ~/.claude/competitor-watch/watchlist.md.
If watchlist exists: Read it. It contains brand names, domains, and resolved brandIds from a previous session. Use these directly — no need to ask again.
If no watchlist exists (first run): Ask the user to set up their competitor watchlist:
"This is your first competitor watch. Who are the 3-5 brands you want to track? Give me their names or website domains and I'll set everything up."
Use AskUserQuestion to collect this. Accept brand names, domains, or both.
Then resolve each brand:
- Try
get_brand_by_domain(brandUrl)if a domain was provided - If that fails or a name was given, try
search_brands(query)with the brand name - If multiple results, show the matches and ask which one
- If no match, tell the user the brand wasn't found in Motion's ad library and skip it
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 · 538 lines · 62 tokens per session scan A 3bc0e9d9d79c
competitor-watch is a skill published in the GitHub repository Motion-Creative/motion-creative-plugin (20 stars, last pushed 3mo ago), licensed MIT. It adds 62 tokens to every session and 5,890 once invoked, about $0.0003 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.
Other skills, from other repositories
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
brainstorming
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
auto-perf-optimize
Run agent-driven VS Code performance or memory investigations. Use when asked to launch Code OSS, automate a VS Code scenario, run the Chat memory smoke runner, capture renderer heap snapshots, take workflow screenshots, compare run summaries, or drive a repeatable scenario before heap-snapshot analysis.
chat-perf
Run chat perf benchmarks and memory leak checks against the local dev build or any published VS Code version. Use when investigating chat rendering regressions, validating perf-sensitive changes to chat UI, or checking for memory leaks in the chat response pipeline.
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.
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…