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 skills add palmier-io/palmier-skills --skill podcast-adgit clone --depth 1 https://github.com/palmier-io/palmier-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/palmier-io/palmier-skills/podcast-ad)<a href="https://agentmods.dev/skills/palmier-io/palmier-skills/podcast-ad"><img src="https://agentmods.dev/badge/skills/palmier-io/palmier-skills/podcast-ad/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/palmier-io/palmier-skills/podcast-ad"><img src="https://agentmods.dev/badge/skills/palmier-io/palmier-skills/podcast-ad.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00089 | $0.02550 |
| Opus 5 | $0.00044 | $0.01275 |
| Sonnet 5 | $0.00018 | $0.00510 |
| Haiku 4.5 | $0.00009 | $0.00255 |
Grade A, and why
podcast-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 8d 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 — 222 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Podcast Ad
The ad is two people, one room, one conversation. Viewer overhears; nobody pitches the lens. Script is ~80% of the result.
Palmier does not generate a full multi-speaker room in one pass — segment the exchange into model-length beats, lock room/identity with matching reverse-shot stills, then assemble on the timeline.
Pair with video-prompting. For faces that must match brand talent across variants, run character-pipeline first. Finish captions/layout with ugc-editing patterns when helpful.
Why this format works
- She never addresses camera — kills “ad argument in the head”
- His doubts are the buyer’s doubts — conversion when he clicks
- Pattern-matches podcast clips in the feed, not classic ad shapes
Break overheard staging (lens eye contact, hard CTA, cheerleading sceptic) and you lose the format.
Step 1 — Write the exchange (sceptic first)
| Role | Job |
|---|---|
| Believer | Transformation. Casual certainty. Talks to a mate, not a customer. |
| Sceptic | Doubt → convert on camera. Write him first — his arc is the ad. |
Sceptic’s four beats (required)
- Open doubt — names the quiet part out loud
- Honest question — hands her the floor without agreeing
- The click — mechanism connects to his life (not a parrot of her claim)
- Concession — stops arguing; does not gush
Believer craft: human comparison over feature list; disarm the scary version, then educate with a specific mechanism; callback close; no hard CTA inside the dialogue.
Map the script onto generation beats that fit list_models durations (often 4–10s). Prefer tight durations (often 5–6s). One conversational turn (or half-turn) per clip is safer than cramming. Flux 3 minimum is 5s.
Backchannels (reactive ad-libs)
Agent judgment (this skill): on longer believer turns, decide whether 1–2 quiet off-screen listener murmurs earn a place — sparse, woven through her speech (not parked only at the end), not after every clause, not a reverse-shot cutaway. Skip short callbacks and all sceptic-speaking beats.
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.
- 8d ago First seen · 222 lines · 89 tokens per session scan A 142e1b1ecc16
podcast-ad is a skill published in the GitHub repository palmier-io/palmier-skills (66 stars, last pushed 11d ago), licensed Apache-2.0. It adds 89 tokens to every session and 2,550 once invoked, about $0.0004 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 2026-09-03.
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