Assemble a two-host fake-podcast skit ad from a config — per-line lipsync clips hard-concatenated in script order, scaled/padded to 1080×1920, WHITE bottom-center captions (up to 5 words per cue, broken on sentence punctuation, word-wrapped to stay in-frame, held at least 0.9s) built from each line's OWN ElevenLabs char-level timestamps (offset by cumulative clip start, never Whisper), and closed on a Playwright/PIL brand end card composited from the real wordmark — never AI-rendered text. This is the FREE deterministic assembly stage (concat + white captions + end card + crf28 encode); the per-line VOs, photoreal gpt-image-2 base stills, expression variants, and lipsync clips come from create-vo-elevenlabs / create-image-gpt-image-fal / create-video-fal. Use for the podcast-skit format.
60
70%
Does it follow best practices?
Run evals on this skill
Adds up to 20 points to the overall score
View guide
Low
Low-risk findings worth noting
Fix and improve this skill with Tessl
tessl review fix ./skills/ads/capabilities/render-podcast-skit/SKILL.mdAssemble a two-host fake-podcast skit ad from a config: a skeptic and a believer at an absurd themed podcast desk do a snappy back-and-forth about the product (the set is deliberately unrelated — that is the joke). Each line is its own lipsync clip so the edit can cut on the dialogue beat (~1.8s avg); this capability is the FREE, deterministic assembly that concatenates those clips, renders the WHITE captions, and appends the brand end card.
scripts/config.example.json is the worked example (Ladder run-02 "Laundromat 2am", ~49s
1080×1920 9:16, ~22 lines); scripts/PIPELINE.md maps every config block to its source step
and scripts/README.md documents the free assembly.
This is the FREE, deterministic assembly stage — it spends nothing. The paid inputs are
separate capabilities: one ElevenLabs with-timestamps VO per line (one voice per host) via
create-vo-elevenlabs; two photoreal base stills at the themed desk plus ~10 expression variants
(mouths NEUTRAL/CLOSED, gpt-image-2 quality=high, not nano-banana) via
create-image-gpt-image-fal; and one lipsync clip per (still, VO) pair via create-video-fal.
Given the per-line clips + their VO timestamps + the brand wordmark SVG, render-podcast-skit
walks the scenes in script order, builds the global caption timeline, renders the WHITE captions,
hard-concats the clips, auto-appends the end card, and final-encodes crf28 → the master. Re-cuts
reuse the existing VOs / stills / clips and cost $0.
words.json by offsetting each line's char-level word timings by the cumulative clip
start, group into ≤5-word cues broken on sentence-final punctuation, and render WHITE
#FFFFFF bottom-center captions (black outline), word-wrapped to stay in-frame and held
≥0.9s — PIL PNG overlays when the host ffmpeg lacks libass (common), else ASS. (Yellow 3-word
karaoke was the old style, rejected in testing.) Whisper on the rendered clips mistimes; the VO
timestamps are ground truth.-preset slow -crf 28 + aac
96k → a 1080×1920 h264+aac master (~6MB for ~28s; the old -crf 20 produced ~16MB). No paid
calls, no keys.8866b2a
If you maintain this skill, you can claim it as your own. Once claimed, you can manage eval scenarios, bundle related skills, attach documentation or rules, and ensure cross-agent compatibility.