Skill Compose is an open-source platform for building and running AI agents that use modular skills. It is intended for creating skill-powered agents without workflow graphs or a command-line interface, and the catalogue skills are examples of those agent capabilities.
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 dp-archive/archive --skill storyboard-to-slidesgit clone --depth 1 https://github.com/dp-archive/archiveWrote 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/dp-archive/archive/storyboard-to-slides)<a href="https://agentmods.dev/skills/dp-archive/archive/storyboard-to-slides"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/storyboard-to-slides/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/dp-archive/archive/storyboard-to-slides"><img src="https://agentmods.dev/badge/skills/dp-archive/archive/storyboard-to-slides.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 | $0.00116 | $0.02901 |
| Opus 5 | $0.00058 | $0.01451 |
| Sonnet 5 | $0.00023 | $0.00580 |
| Haiku 4.5 | $0.00012 | $0.00290 |
Grade A, and why
storyboard-to-slides 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 13d 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 — 308 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Storyboard to Slides
Assemble a polished PPTX from a storyboard CSV + image files using python-pptx.
Dependencies
# pip install python-pptx Pillow
Input Format
Expects storyboard.csv with columns:
slide_no, slide_type, title, bullet_points, image_prompt, speaker_notes, layout
And image files named slide_{no}.png for each row.
Workflow
1. Read the Storyboard
import csv
with open("storyboard.csv", "r", encoding="utf-8") as f:
reader = csv.DictReader(f)
slides = list(reader)
2. Initialize the Presentation
from pptx import Presentation
from pptx.util import Inches, Pt, Emu
from pptx.dml.color import RGBColor
prs = Presentation()
prs.slide_width = Inches(13.333) # 16:9
prs.slide_height = Inches(7.5)
For 4:3 slides: Inches(10) x Inches(7.5).
3. Define Theme Constants
# Customize per project
THEME = {
"bg_color": RGBColor(0x1A, 0x1A, 0x2E), # dark navy
"title_color": RGBColor(0xFF, 0xFF, 0xFF),
"text_color": RGBColor(0xE0, 0xE0, 0xE0),
"accent_color": RGBColor(0x64, 0xB5, 0xF6),
"title_font": "Arial",
"body_font": "Arial",
"title_size": Pt(36),
"body_size": Pt(18),
}
Choose colors that contrast well with the generated images. For light images use dark text overlay with semi-transparent background; for dark images use white text.
4. Layout Implementations
full_bg — Full-screen background image + overlay text
from pptx.util import Inches, Pt, Emu
from pptx.dml.color import RGBColor
from pptx.enum.text import PP_ALIGN, MSO_ANCHOR
def add_full_bg_slide(prs, img_path, title, subtitle="", theme=THEME):
slide = prs.slides.add_slide(prs.slide_layouts[6]) # blank
# Background image
slide.shapes.add_picture(img_path, 0, 0, prs.slide_width, prs.slide_height)
# Semi-transparent overlay
overlay = slide.shapes.add_shape(
1, 0, 0, prs.slide_width, prs.slide_height # MSO_SHAPE.RECTANGLE = 1
)
overlay.fill.solid()
overlay.fill.fore_color.rgb = RGBColor(0, 0, 0)
overlay.shadow.inherit = False
overlay.line.fill.background()
# Set overlay transparency via XML (access the shape's XML element, not the fill object)
from pptx.oxml.ns import qn
solid = overlay._element.find(f'.//{qn("a:solidFill")}')
if solid is not None:
srgb = solid.find(qn("a:srgbClr"))
if srgb is not None:
alpha = srgb.makeelement(qn("a:alpha"), {"val": "40000"}) # 40% opacity
srgb.append(alpha)
# Title
txBox = slide.shapes.add_textbox(Inches(1), Inches(2.5), Inches(11), Inches(2))
tf = txBox.text_frame
tf.word_wrap = True
p = tf.paragraphs[0]
p.text = title
p.font.size = Pt(44)
p.font.bold = True
p.font.color.rgb = theme["title_color"]
p.alignment = PP_ALIGN.CENTER
if subtitle:
p2 = tf.add_paragraph()
p2.text = subtitle
p2.font.size = Pt(24)
p2.font.color.rgb = theme["text_color"]
p2.alignment = PP_ALIGN.CENTER
return slide
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.
- 13d ago First seen · 308 lines · 116 tokens per session scan A 261559953a8e
storyboard-to-slides is a skill published in the GitHub repository dp-archive/archive (1,107 stars, last pushed 6mo ago), licensed Apache-2.0. It adds 116 tokens to every session and 2,901 once invoked, about $0.0006 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 2026-08-30.
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