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 michalparkola/tapestry-skills --skill learn-thisgit clone --depth 1 https://github.com/michalparkola/tapestry-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/michalparkola/tapestry-skills/learn-this)<a href="https://agentmods.dev/skills/michalparkola/tapestry-skills/learn-this"><img src="https://agentmods.dev/badge/skills/michalparkola/tapestry-skills/learn-this/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/michalparkola/tapestry-skills/learn-this"><img src="https://agentmods.dev/badge/skills/michalparkola/tapestry-skills/learn-this.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 | $0.00085 | $0.03324 |
| Opus 5.5 | $0.00034 | $0.01330 |
| Sonnet 5.5 | $0.00017 | $0.00665 |
| Haiku 4.5 | $0.00009 | $0.00332 |
Grade C, and why
learn-this scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Downloads and executes remote codehighSupply chain
curl | sh runs whatever the server returns today, which is not necessarily what it returned when this was reviewed.
curl -s "$URL" | python3 -c " Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
elif curl -sI "$URL" | grep -i "Content-Type: application/pdf" > /dev/null; then Copies of this mod
1 near-identical copy found in the catalogue:
- tapestry — 89% identical, 13 lines differ
How it starts
The opening of the file, as written. The whole thing — 483 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tapestry: Unified Content Extraction + Action Planning
This is the master skill that orchestrates the entire Tapestry workflow:
- Detect content type from URL
- Extract content using appropriate skill
- Automatically create a Ship-Learn-Next action plan
When to Use This Skill
Activate when the user:
- Says "learn-this [URL]" or "learn this [URL]"
- Says "weave [URL]"
- Says "help me plan [URL]"
- Says "extract and plan [URL]"
- Says "make this actionable [URL]"
- Says "turn [URL] into a plan"
- Provides a URL and asks to "learn and implement from this"
- Wants the full Tapestry workflow (extract → plan)
Keywords to watch for: learn-this, learn this, weave, plan, actionable, extract and plan, make a plan, turn into action
How It Works
Complete Workflow:
- Detect URL type (YouTube, article, PDF)
- Extract content using appropriate skill:
- YouTube → youtube-transcript skill
- Article → article-extractor skill
- PDF → download and extract text
- Create action plan using ship-learn-next skill
- Save both content file and plan file
- Present summary to user
URL Detection Logic
YouTube Videos
Patterns to detect:
youtube.com/watch?v=youtu.be/youtube.com/shorts/m.youtube.com/watch?v=
Action: Use youtube-transcript skill
Web Articles/Blog Posts
Patterns to detect:
http://orhttps://- NOT YouTube, NOT PDF
- Common domains: medium.com, substack.com, dev.to, etc.
- Any HTML page
Action: Use article-extractor skill
PDF Documents
Patterns to detect:
- URL ends with
.pdf - URL returns
Content-Type: application/pdf
Action: Download and extract text
Other Content
Fallback:
- Try article-extractor (works for most HTML)
- If fails, inform user of unsupported type
Step-by-Step Workflow
Step 1: Detect Content Type
URL="$1"
# Check for YouTube
if [[ "$URL" =~ youtube\.com/watch || "$URL" =~ youtu\.be/ || "$URL" =~ youtube\.com/shorts ]]; then
CONTENT_TYPE="youtube"
# Check for PDF
elif [[ "$URL" =~ \.pdf$ ]]; then
CONTENT_TYPE="pdf"
# Check if URL returns PDF
elif curl -sI "$URL" | grep -i "Content-Type: application/pdf" > /dev/null; then
CONTENT_TYPE="pdf"
# Default to article
else
CONTENT_TYPE="article"
fi
echo "📍 Detected: $CONTENT_TYPE"
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 · 483 lines · 85 tokens per session scan C fed5cc714699
learn-this is a skill published in the GitHub repository michalparkola/tapestry-skills (562 stars, last pushed 7mo ago), licensed MIT. It adds 85 tokens to every session and 3,324 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it C with 2 findings (downloads and executes remote code, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
hr-onboarding
A new-hire onboarding plan as a single page — first week schedule, buddy + manager intro, learning track, equipment checklist, and "you're set when…" outcomes. Use when the brief mentions "onboarding", "new hire", "first week plan", or "入职".
book-mirror
Take any book (EPUB/PDF), produce a personalized chapter-by-chapter analysis. Each chapter is preserved in detail (The Chapter) and mirrored back to the reader's actual life (The Mirror) using brain context. The mirror observes and resonates — a friend pointing out parallels, NOT a consultant rearranging the reader's…
lean-formalize
Develop and verify a mathematical proof in Lean, continue an incomplete Lean project, or audit whether it proves the original statement. Connect actual inputs to intermediate lemmas, assemble the target theorem, check its transitive axioms, and provide a reproducible handoff. Use when Lean is requested or a specific…
exam-forecast
Analyze past exams from the same professor to surface patterns — subject weighting, recurring issue-spot traps, favored hypo types, policy-vs-doctrine mix — and forecast likely emphases for the upcoming exam. Use when the user says "what's on the exam", "analyze past exams", "predict the exam", or shares past exams.
eli5
Explain research, papers, or technical ideas in plain English with minimal jargon, concrete analogies, and clear takeaways. Use when the user says "ELI5 this", asks for a simple explanation of a paper or research result, wants jargon removed, or asks what something technically dense actually means.
deck-course-module
A course or workshop slide template with persistent learning goals, teaching pages, multiple-choice self-tests, and a wrap-up.