Raven is an open-source agent harness for running long-term AI work with terminal execution, tracing, memory, skills, evaluation, and reusable workflows. People use the current release to operate and improve persistent AI workflows, while its described future direction is a multi-agent system that combines specialized harnesses.
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 EverMind-AI/Raven --skill deck-to-pptxgit clone --depth 1 https://github.com/EverMind-AI/RavenWrote 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/evermind-ai/raven/deck-to-pptx)<a href="https://agentmods.dev/skills/evermind-ai/raven/deck-to-pptx"><img src="https://agentmods.dev/badge/skills/evermind-ai/raven/deck-to-pptx/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/evermind-ai/raven/deck-to-pptx"><img src="https://agentmods.dev/badge/skills/evermind-ai/raven/deck-to-pptx.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 | $0.00157 | $0.02924 |
| Opus 5.5 | $0.00063 | $0.01170 |
| Sonnet 5.5 | $0.00031 | $0.00585 |
| Haiku 4.5 | $0.00016 | $0.00292 |
Grade A, and why
deck-to-pptx 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 24d 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 — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deck to .pptx
The deliverable is a .pptx file on disk. Not a web page, an image, a PDF, or a Markdown
outline. Name its absolute path in the reply.
What this deployment has
| Need | Call | Note |
|---|---|---|
| Facts the material does not carry | web_search, then web_fetch the page |
there is no other search |
| A real logo, product shot, published chart | image_search |
direct image URL, pixel size and source page per hit; see references/assets.md |
| A picture that does not exist yet | image_generate |
reference pictures go in images, up to six |
| An icon | raven_ppt.services.assets.icons |
1304 outline icons, see references/assets.md |
| A figure or table from a paper | PyMuPDF on raven-python |
from the PDF you were given, else one you downloaded; crop the region or pull the embedded image; see references/assets.md |
| A formula | add_formula from raven_ppt.services.assets.formulas, on raven-python |
one line of TeX in, a picture in the deck's ink at true size out; see references/assets.md |
| Symbols inside a sentence | math_runs from the same module |
_A, ^2, θ* become real sub- and superscript runs; see references/assets.md |
| Render a page to look at it | soffice --headless --convert-to pdf, then pdftoppm |
With no image key configured, image_generate says so. Say which pages would have had a
picture and carry them on type, grid, rule and colour.
The pages are yours to draw
Every page is drawn here, with python-pptx: the grid, the title row, the type, the marks, the
diagrams, the pictures and their treatment. Nothing about a page is settled by a default.
raven_ppt.services.assets holds the icon set, add_formula and math_runs; take those and
draw the rest.
On this route there is no ANTI-SLOP-CHECK.md, no contract and no Task State. Do not initialize or update one.
Read two skills first
local/deck-to-pptx (this file) and local/design-editorial-and-presentations, which owns
the content order and the editorial judgement. Read both before the first page.
What ships with it
3 files 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.
- 24d ago Changed · +5 lines b970f8ecbc3a
- 26d ago Changed · -12 lines 6ba7e4853fba
- 29d ago Changed · +56 lines af084b509253
- 1mo ago First seen · 133 lines · 157 tokens per session scan A dcd3bee42ec2
deck-to-pptx is a skill published in the GitHub repository EverMind-AI/Raven (5,463 stars, last pushed today), licensed Apache-2.0. It adds 157 tokens to every session and 2,924 once invoked, about $0.0006 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 2026-09-12.
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