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 shaharsha/claude-skills --skill self-presenting-decksgit clone --depth 1 https://github.com/shaharsha/claude-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/shaharsha/claude-skills/self-presenting-decks)<a href="https://agentmods.dev/skills/shaharsha/claude-skills/self-presenting-decks"><img src="https://agentmods.dev/badge/skills/shaharsha/claude-skills/self-presenting-decks/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/shaharsha/claude-skills/self-presenting-decks"><img src="https://agentmods.dev/badge/skills/shaharsha/claude-skills/self-presenting-decks.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00105 | $0.02193 |
| Opus 5.5 | $0.00042 | $0.00877 |
| Sonnet 5.5 | $0.00021 | $0.00439 |
| Haiku 4.5 | $0.00011 | $0.00219 |
Grade A, and why
self-presenting-decks 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 today.
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 — 84 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Self-Presenting Decks (orchestration)
Overview
Producing a deck that presents itself is a three-skill pipeline with a fixed order. This skill is the map — each stage's depth lives in its own skill.
deck (pptx) ──▶ narration (audio + autoplay pptx) ──▶ video (mp4)
content depends on final slides depends on final slides + final audio
Order is the whole game: content → narration → video. Narration is written against the finished slides, so slides must be final first. The video embeds both the rendered slides and the audio, so it's always last. Editing upstream invalidates downstream — see the update matrix.
The stages
| Stage | Skill to use | Output |
|---|---|---|
| 0. Intake | — | Where it will be watched (PowerPoint file, Google Slides, a video, or all three; it decides which artifacts to build); voice and language/register (these live in narrating-pptx; the voice, model, key and tags in elevenlabs-tts) |
| 1. Build/edit the deck | document-skills:pptx (pptxgenjs or template editing); presentation-generator for AI-image decks | deck.pptx (the clean copy — keep it) |
| 2. Validate rendering | office-render — real PowerPoint → PDF → images | layout proof + the PDF the video will reuse — always from the CLEAN pptx (the narrated copy renders speaker icons onto slides) |
| 3. Narrate | narrating-pptx — scripts → human approves scripts BEFORE TTS (it's real credit spend, often in a cloned voice) → TTS via elevenlabs-tts (Eleven v4, sample one slide first) → for a pptx: embed → PowerPoint-authored autoplay → its own validation pass; for Google Slides: one autoplaying Drive video per slide on the deck synced from the clean pptx | narration/scripts.json, narration/slideNN.mp3, then deck-narrated.pptx and/or the narrated Google Slides deck |
| 4. Video | deck-to-video — the stage-2 clean-deck PDF + the same narration/ mp3s → mp4 (progress bar + slide counter are defaults; flags turn them off) |
deck.mp4 |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- today Changed · +8 lines · +7 tokens per session 4deb94de139c
- 1mo ago Changed · +1 lines 100d56f47a81
- 1mo ago Changed · +1 lines 427979a52a90
- 2mo ago First seen · 74 lines · 98 tokens per session scan A 29972edef36c
self-presenting-decks is a skill published in the GitHub repository shaharsha/claude-skills (6 stars, last pushed today), licensed MIT. It adds 105 tokens to every session and 2,193 once invoked, about $0.0004 per session on Opus 5.5. 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 2026-08-31.
self-presenting-decks compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| self-presenting-decksshaharsha/claude-skills | Skill | 6 | today | MIT | A | 105 | Claude Code |
| imagegenopenai/codex | Skill | 133k | yesterday | Apache-2.0 | A | 113 | Codex |
| playwright-climicrosoft/playwright | Skill | 99k | yesterday | Apache-2.0 | A | 19 | Claude Code |
| local-ai-agentsmicrosoft/ai-agents-for-beginners | Skill | 79k | 4d ago | MIT | A | 200 | Claude Code, Codex |
| systematic-debuggingobra/superpowers | Skill | 313k | 3d ago | MIT | A | 21 | Claude Code |
| next-cache-components-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 95 | Claude Code |
| next-partial-prefetching-adoptionvercel/next.js | Skill | 143k | yesterday | MIT | A | 103 | Claude Code |