SuperCMO Creative Skills
Turn product photos and briefs into campaign-ready video ads, UGC videos, and product imagery.
- Source repo
- SupercmoHQ/superCMO-skills
- Stars
- ★ 196
- Last updated
- 2d ago
- License
- Apache-2.0
- Primary language
- Python
- FA score
- 58/100 · Major gaps
At a glance
- How it runs
- Works with
- Universal · cross-platformCodex · Claude Code
- Cost
- Free software; you pay for model usage
- Setup effort
- Low · running in minutes
- You'll need
- Typical use
- An ecommerce marketer turns a product photo into an unboxing, try-on, or review-style UGC ad.
- Not a fit if
- Teams unwilling to configure model keys or buy managed credits
- Teams that require generation without network access
- Source review
- 58/100 · Major gaps
What does this agent do, and when should you use it?
SuperCMO Skills is a set of marketing production skills for AI agents such as Claude Code, Cursor, Codex, and Hermes, accompanied by a local MCP server. Given a product photo or URL and a brief, the skills can analyze product and brand details, plan scripts and storyboards, generate images, video, and voiceover, then edit and stitch the result. The repository also covers ad copy, competitor ad research, ad analysis, format adaptation, and campaign planning. Its installer uses npx to place the skills and connect the MCP server to supported agents; the server launches locally through uvx and calls configured model services. Users can supply their own vendor keys or use SuperCMO-managed keys with usage-based credits; generation involves external services.
Skills such as analyzing-products and analyzing-brand read product photos, product URLs, and brand websites. generating-ugc-videos, generating-ad-videos, and planning-campaigns develop concepts, scripts, and storyboards. generating-images, generating-videos, and generating-audio produce imagery, video clips, and voiceovers, which the workflow can trim and stitch into a finished video. Users can review plans before rendering, and generation tools support dry_run to preview a request without making an API call. The MCP server reads environment variables from ~/.supercmo/.env or the host configuration and can use providers such as Wavespeed or Fal, with optional integrations for ElevenLabs, Gemini, and Firecrawl.
- An ecommerce marketer turns a product photo into an unboxing, try-on, or review-style UGC ad.
- A creative team develops a multi-shot commercial from a product brief and reviews its script and storyboard before rendering.
- A social media team produces ad assets in multiple formats for channels such as Instagram, TikTok, or YouTube.
- A brand team extracts brand details from its website to guide later ad production.
- A marketer studies competitor or in-house ads to map creative angles, hooks, and long-running assets.
- An ad team adapts existing creative to multiple placement sizes and drafts platform-specific copy.
How do you install or deploy this agent?
Install uv and ensure it is on PATH. Use the npx installer to connect the skills to detected hosts; for a Cursor project, specify its directory. After installation, run the supercmo-setup skill to configure the environment.
How do you use this agent?
Choose a credential path: authorize SuperCMO-managed keys and buy credits, or add vendor keys to ~/.supercmo/.env. WAVESPEED_API_KEY supports image and video generation; FAL_KEY is an alternative. Then run supercmo-setup in your agent, attach a product photo or provide a URL, and describe the campaign—for example, request a 30-second unboxing UGC ad. Use dry_run to preview a request before generation.
What are this agent's strengths and limitations?
- Covers the production workflow from product analysis, scripting, and storyboarding through media generation and video stitching.
- Includes focused skills for UGC, commercials, cartoon videos, product photography, competitor research, and ad copy.
- Uses storyboards and references to help keep actors and products consistent across shots, with review before costly rendering.
- Supports direct vendor requests with bring-your-own keys as well as a usage-based managed-key option.
- Generation depends on network access and external model services; BYOK use requires the relevant vendor accounts and credentials.
- Installation requires npx from Node.js and uv on PATH, and the agent host must support Agent Skills and MCP.
- Managed-key requests pass through SuperCMO's proxy and consume purchased credits.
- Anonymous usage telemetry is enabled by default, though the README documents opt-out settings.
How does this agent compare with similar options?
The README presents the project as an open-source alternative to closed AI marketing agents and describes a hosted SuperCMO service; it does not provide a feature comparison with a named competitor.
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| SuperCMO Creative Skills This agent | 58 · Major gaps | Agent plugin / skillFree + model costs | ★ 196 | 2d ago | Python | Codex · Claude Code |
| Wonda CLI | 56 · Major gaps | CLIPaid | ★ 158 | today | TypeScript | Codex · Claude Code |
| Xiaohei Hand-Drawn Illustrations Skill | 34 · Major gaps | Agent plugin / skillFree + model costs | ★ 13k | 28d ago | — | Codex |
| md2wechat: AI-Agent CLI for WeChat Official Account Publishing | 47 · Major gaps | CLIFreemium | ★ 3.8k | 9d ago | Go | Codex · Claude Code |
How does FollowAgents rate this agent?
Why each dimension lost points
README and SECURITY.md describe credential locations, the BYOK direct-to-vendor path versus the managed proxy, telemetry fields, excluded data, and opt-out controls, providing meaningful data-flow and sensitive-data guidance. README also says generation waits for approval and offers dry runs. Deductions: the supplied files do not establish permission scope, enforcement of confirmation, or rollback design; dependency security is only summarized as stdlib-only even though pyproject lists calendar dependencies, with no vulnerability review evidence; paid generation and publishing are external effects, and confirmation guidance does not cover every operation; attribution evidence is limited to repository and security contact details, while publisher identity is unverified.
The materials show validation scripts, calendar tests, and MCP error conventions, but README's stdlib-only server description does not fully match the icalendar and python-dateutil dependencies in pyproject. The evaluation harness uses keyword matching and canned responses, so it cannot validate real agent behavior. Deductions: consistency and dependency availability are only partially supported; structured MCP error tests and a security reporting process exist, but failure behavior across integrations is not documented here.
The skill catalog covers UGC, ads, product photography, competitor research, copywriting, and several agent hosts, with concrete audiences and scenarios. Documentation also explains key requirements, optional capabilities, and the fact that skills.sh does not install the MCP server. Deductions: the supplied evidence does not systematically establish trigger rules or false-positive boundaries, and environment guidance covers listed hosts and prerequisites rather than all environments.
The README has navigation, installation, configuration, security, and telemetry sections with multiple installation routes and examples; Apache-2.0 is accompanied by a complete license, package manifests identify versions, release workflows describe the version path, and SECURITY.md provides a reporting address and supported-version policy. Deductions: the supplied files do not provide a changelog or fully establish ongoing maintenance responsibility; installation mechanisms and skill names are understandable, but some implementation descriptions do not align completely.
The skills combine product analysis, scripting, image/video generation, audio, and editing into a marketing workflow. Examples and dry-run previews indicate practical output guidance and cost control. Deductions: claims about finished quality, consistency, speed, and value are largely promotional in the supplied materials and do not establish typical results or user savings.
The README makes specific claims about key paths, telemetry fields, and install commands, with limited corroboration from the license, security policy, package configuration, and tests. It also says unconfirmed brand information should be labeled as unconfirmed. Deductions: core generation and consistency claims lack independent support; the evaluation harness simulates keyword triggering and fixed responses, and the relationship between some capability claims and static configuration is not fully demonstrated.
- Managed-key requests go through SuperCMO's proxy, BYOK requests go directly to the selected vendor, and anonymous telemetry is described as opt-out; review the telemetry details and opt-out controls before use.
- Paid generation and publishing involve external services; inspect dry-run previews and confirm the approval boundary for each action.
- README's server dependency description differs from the dependencies listed in pyproject; verify actual runtime requirements, including calendar features, before deployment.