/fan-cam

name: fan-cam
description: >
  Create personalized live sports broadcast fan-cam videos with genmedia. Use
  this for realistic spectator cutaways, stadium or arena crowd reactions,
  broadcast screenshots, sports TV shots, scoreboard overlays, TV channel bugs,
  and identity-preserving fan reaction videos from a user photo.

Fan cam production with genmedia

Use this skill when the user wants a personalized spectator video that feels like a real live sports broadcast cutaway. The usual input is one photo of the person, event details, and a desired reaction or situation.

Runtime is the genmedia CLI. Use the `genmedia` skill for command syntax. Load `model-routing`, `fal-prompting`, and `genmedia-workflow` when endpoint choice, model-specific prompt craft, or pipeline execution details matter.

Do not encode private examples, local file paths, user-specific workflow names, or conversation-specific details into prompts or docs. Keep this skill generalized.

References

Load only what is needed:

• `references/prompt-contract.md` for the image prompt and Kling prompt rules.
• `references/genmedia-commands.md` for executable CLI command patterns.
• `references/examples.md` for sport-specific examples.

Required inputs

Ask only for missing information that changes execution:

• User photo: local path or URL. This is the identity reference.
• Event details: sport, matchup, venue, league, broadcast context, wardrobe,

scoreboard idea, crowd behavior, and any specific scenario.

• Reaction or situation: excited, happy, laughing, sad, neutral, angry,

surprised, nervous, focused, eating, distracted, caught on camera, noticing the stadium screen, celebrating, disappointed, or another user-specified moment.

• Budget or quality preference only when the user explicitly asks for economy,

preview, or native 4K final output. Otherwise use the standard fan-cam defaults: GPT Image 2 edit at `quality=high` with a 3840x2160 frame, then Kling v3 Pro.

If the user gives a local image path, upload it once with `genmedia upload` and reuse the returned URL. If the user gives multiple references, treat the first person image as the identity source and later images as optional venue, broadcast, or styling references.

The user photo is an identity reference, not a Kling-ready start frame. Do not skip GPT Image 2 edit just because the user supplied a person's photo. For a personalized fan-cam, first use `openai/gpt-image-2/edit` to place the person inside a realistic 16:9 broadcast scene, then use the approved generated frame as Kling `start_image_url`.

Pipeline

Default graph:

photo URL -> prompt planning -> GPT Image 2 edit frame -> optional compression -> Kling v3 image-to-video -> downloaded video manifest

The GPT Image 2 edit frame is mandatory when the input is an ordinary person photo. Only bypass this step if the user explicitly provides an already approved 16:9 broadcast fan-cam frame and asks to animate that frame.

The planning step is performed by the agent using this skill. Do not call a separate LLM endpoint just to write prompts unless the user explicitly asks for a hosted planner. Write the image prompt and Kling multi prompts directly.

Endpoint selection

Always verify endpoints before use:

genmedia models --endpoint_id openai/gpt-image-2/edit --json
genmedia models --endpoint_id fal-ai/kling-video/v3/standard/image-to-video --json
genmedia models --endpoint_id fal-ai/kling-video/v3/pro/image-to-video --json
genmedia models --endpoint_id fal-ai/kling-video/v3/4k/image-to-video --json

Inspect schemas before running:

genmedia schema openai/gpt-image-2/edit --json
genmedia schema fal-ai/kling-video/v3/pro/image-to-video --format openapi --json

Use `--format openapi` for Kling v3 image-to-video endpoints because compact schema output may omit top-level fields such as `multi_prompt`, `start_image_url`, `duration`, `prompt`, `elements`, `shot_type`, `negative_prompt`, and `cfg_scale`.

Check pricing when cost matters:

genmedia pricing openai/gpt-image-2/edit --json
genmedia pricing fal-ai/kling-video/v3/standard/image-to-video --json
genmedia pricing fal-ai/kling-video/v3/pro/image-to-video --json
genmedia pricing fal-ai/kling-video/v3/4k/image-to-video --json

GPT Image 2 quality choice

• Use `quality=high` by default for personalized fan-cam frames. GPT Image 2

price is strongly affected by `low` vs `high`, but fan-cam identity, broadcast integration, and readable overlays need the stronger default.

• Use `quality=low` only when the user explicitly requests economy, preview,

fast iteration, or lower-cost social drafts.

• Use `output_format=jpeg` for the generated broadcast frame unless the user

needs transparency or lossless output.

• Use 16:9 4K frame size by default:

{"width":3840,"height":2160}

Kling v3 choice

Select the endpoint based on the brief:

• `fal-ai/kling-video/v3/pro/image-to-video`: default fan-cam endpoint. Use it

for normal personalized sports cutaways, public examples, and any request where the user did not explicitly ask for economy.

• `fal-ai/kling-video/v3/standard/image-to-video`: use only when the user

explicitly asks for economy, preview, fastest iteration, or lower cost.

• `fal-ai/kling-video/v3/4k/image-to-video`: use only for final premium 4K

delivery or when the user explicitly asks for 4K video. Check pricing first.

Do not choose from memory alone. Verify model status and schema with genmedia in the current session.

Shot and duration planning

The agent decides the number and duration of multi prompts.

Hard rules:

• Each multi prompt must be at least 3 seconds.
• Total video duration must be 15 seconds or less.
• Use 2 to 5 multi prompts.
• Set the top-level Kling `duration` equal to the sum of all beat durations.
• If a real user-provided or approved reference is supplied through Kling

`elements`, every multi prompt must reference `@