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 adilkalam/orca --skill tapestrygit clone --depth 1 https://github.com/adilkalam/orcaWrote 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/adilkalam/orca/tapestry)<a href="https://agentmods.dev/skills/adilkalam/orca/tapestry"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/tapestry/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/adilkalam/orca/tapestry"><img src="https://agentmods.dev/badge/skills/adilkalam/orca/tapestry.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 | $0.00081 | $0.02927 |
| Opus 5.5 | $0.00032 | $0.01171 |
| Sonnet 5.5 | $0.00016 | $0.00585 |
| Haiku 4.5 | $0.00008 | $0.00293 |
Grade A, and why
tapestry scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 447 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tapestry: Unified Content → Action Workflow
This skill combines content extraction with actionable planning - turning any learning resource (YouTube videos, articles, PDFs) into Ship-Learn-Next action plans in one seamless flow.
When to Use This Skill
Activate when the user:
- Says "tapestry [URL]" or "weave [URL]"
- Wants to "extract and plan from [URL]"
- Asks to "make [URL] actionable"
- Says "help me implement [URL]"
- Provides a URL and wants both content + action plan
- Wants to turn learning content into concrete steps
Core Philosophy
Tapestry weaves three threads together:
- Extract - Get clean content from any source
- Synthesize - Identify actionable lessons
- Plan - Create Ship-Learn-Next cycles
Result: From URL to shippable action plan in one flow.
How Tapestry Works
Step 1: Detect Content Type
When user provides a URL, automatically detect:
# YouTube detection
if [[ "$URL" =~ youtube\.com|youtu\.be ]]; then
CONTENT_TYPE="youtube"
# Article/blog detection
elif [[ "$URL" =~ ^https?:// ]]; then
CONTENT_TYPE="article"
# PDF detection (if URL ends in .pdf)
elif [[ "$URL" =~ \.pdf$ ]]; then
CONTENT_TYPE="pdf"
fi
Step 2: Extract Content (Automatic)
Based on content type, use the appropriate extraction method:
For YouTube Videos
Use the youtube-transcript skill:
# Activate youtube-transcript skill
# This handles:
# - Installation check (yt-dlp)
# - Subtitle detection (manual → auto-generated → Whisper)
# - VTT to plain text conversion
# - Deduplication
Result: Clean transcript saved as [Video Title].txt
For Articles/Blogs
Use the article-extractor skill:
# Activate article-extractor skill
# This handles:
# - Tool detection (reader/trafilatura/fallback)
# - Content extraction
# - Clutter removal
# - Clean text output
Result: Clean article saved as [Article Title].txt
For PDFs
Direct extraction:
# Check for PDF tools
if command -v pdftotext &> /dev/null; then
pdftotext "$PDF_URL" output.txt
elif command -v mutool &> /dev/null; then
mutool draw -F txt -o output.txt "$PDF_URL"
else
echo "PDF extraction requires pdftotext or mutool"
echo "Install: brew install poppler (macOS) or apt install poppler-utils (Linux)"
fi
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.
- 1mo ago First seen · 447 lines · 81 tokens per session scan A 791de01a93e6
tapestry is a skill published in the GitHub repository adilkalam/orca (2 stars, last pushed 2mo ago), licensed MIT. It adds 81 tokens to every session and 2,927 once invoked, about $0.0003 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 2026-09-03.
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