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 ppt-script-authoringgit 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/ppt-script-authoring)<a href="https://agentmods.dev/skills/evermind-ai/raven/ppt-script-authoring"><img src="https://agentmods.dev/badge/skills/evermind-ai/raven/ppt-script-authoring/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/ppt-script-authoring"><img src="https://agentmods.dev/badge/skills/evermind-ai/raven/ppt-script-authoring.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 | $0.00044 | $0.38922 |
| Opus 5.5 | $0.00018 | $0.15569 |
| Sonnet 5.5 | $0.00009 | $0.07784 |
| Haiku 4.5 | $0.00004 | $0.03892 |
Grade A, and why
ppt-script-authoring 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 26d 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 — 2,134 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Deck authoring
You design the deck and you build it, by writing a python-pptx program that
ppt_build runs. Every page is yours: the grid, the type scale, the palette, the page
furniture, where a figure sits and how large. What refuses a deck and what only reports
is in §12.
1. Define the communication job
Infer the audience, the deck's job, the outcome you want, the central takeaway and the evidence that supports it. Build a cumulative argument rather than an inventory of topics: each content page performs one narrative job and makes one primary claim.
Write audience-facing titles that state the point. Open with the context, question or stakes that make the deck worth sitting through. Close by resolving that opening — a decision, an implication, a next question — on the last content page. The template's closing page ends the deck after it, as its cover opens it: both are the house frame and carry no argument of their own.
Pack the evidence before allocating pages. A page carries one claim and the developed support that claim takes, and what makes the support developed is that it is more than one kind of thing: evidence from the source, a mechanism or a comparison, an implication or a constraint. Nothing counts the kinds, and how much copy a page ends up with is measured rather than allocated (§2). If the evidence cannot support a complete argument, merge the page rather than enlarging fragments.
The language, the audience and the length are the user's, and they are asked for
rather than inferred. ppt_prepare reads what the request already states and hands
back the rest as questions under ask_user. Put them to the user with the ask_user
tool, all of them in one call, each with the option you would recommend marked. The
answer comes back through the host: in a direct chat the user answers; in a delegated
run the host answers what the delegating conversation already settled and puts the rest
to the user. An answer that is empty, or that says the question was not sent, means
nobody is attached to this run: then take the option you marked recommended -- the
request's own scope, the materials' language, the length its kind of deck usually has --
and record it with ppt_brief as the assumption it is, so the deck says what it assumed.
Ask once: a question nobody answered is not answered by asking it again, and a run that
asks a question a turn spends a turn on each.
The length and the language are then measured against the
finished file and both refuse the deck (§12), so record what the user said rather than
what you inferred. The audience is not measured; it is the room you judge the deck for
when you look at the renders (§10).
What ships with it
12 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.
- references/charts.md 39 KB
- references/design-requirements.md 2.7 KB
- references/formulas.md 1.7 KB
- references/icons.md 6.9 KB
- references/layouts-data.md 19 KB
- references/layouts-figures.md 20 KB
- references/layouts-multiples.md 19 KB
- references/layouts-primitives.md 5.8 KB
- references/layouts-type.md 9.1 KB
- references/layouts.md 23 KB
- references/shapes.md 5.7 KB
- references/tables.md 34 KB
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.
- 26d ago Changed · +2 lines b62fb56b81ab
- 29d ago Changed · -11 lines 5f3b60644785
- 1mo ago First seen · 2,143 lines · 44 tokens per session scan A ab1e4bc9897e
ppt-script-authoring is a skill published in the GitHub repository EverMind-AI/Raven (5,463 stars, last pushed today), licensed Apache-2.0. It adds 44 tokens to every session and 38,922 once invoked, about $0.0002 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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