Borrowing it
Nothing to install: this file belongs to minhlucvan/notebookmd. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/minhlucvan/notebookmd/main/.claude/skills/notebookmd-guide/SKILL.mdgit clone --depth 1 https://github.com/minhlucvan/notebookmdWrote 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/minhlucvan/notebookmd/notebookmd-guide)<a href="https://agentmods.dev/skills/minhlucvan/notebookmd/notebookmd-guide"><img src="https://agentmods.dev/badge/skills/minhlucvan/notebookmd/notebookmd-guide/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/minhlucvan/notebookmd/notebookmd-guide"><img src="https://agentmods.dev/badge/skills/minhlucvan/notebookmd/notebookmd-guide.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 | $0.00049 | $0.01934 |
| Opus 5.5 | $0.00020 | $0.00774 |
| Sonnet 5.5 | $0.00010 | $0.00387 |
| Haiku 4.5 | $0.00005 | $0.00193 |
Grade A, and why
notebookmd-guide 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 2mo 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 — 236 lines — stays where its author put it; the contents beside it link to each section on GitHub.
notebookmd API Reference Guide
This is the complete API reference for the notebookmd library. Use this when you need
to generate Markdown reports, data analysis, dashboards, or structured documents.
Installation
pip install notebookmd # Core (zero deps)
pip install "notebookmd[pandas]" # + tables, DataFrames, CSV
pip install "notebookmd[plotting]" # + matplotlib charts
pip install "notebookmd[all]" # Everything
Factory
from notebookmd import nb, NotebookConfig
cfg = NotebookConfig(
max_table_rows=30, # Default: 30
float_format="{:.4f}", # Default: "{:.4f}"
)
n = nb("output/report.md", title="Report Title", cfg=cfg)
Text Elements
n.title("Title") # # Title
n.header("Header", divider=False) # ## Header (+ --- if divider=True)
n.subheader("Sub", divider=False) # ### Sub
n.caption("Caption text") # _Caption text_
n.text("Preformatted text") # ```text\nPreformatted text\n```
n.latex(r"\sum_{i=1}^{n} x_i") # $$\sum_{i=1}^{n} x_i$$
n.md("**Bold** and _italic_") # Raw markdown passthrough
n.code("print('hello')", lang="python") # ```python\nprint('hello')\n```
n.divider() # ---
n.write("Any text or value") # Auto-formatted
n.note("Important note") # > **Note:** Important note
Data Display
# Single metric with delta
n.metric("Revenue", "$1.2M", delta="+12%")
n.metric("Churn", "2.1%", delta="-0.3%", delta_color="inverse")
# Multiple metrics in a row
n.metric_row([
{"label": "Revenue", "value": "$1.2M", "delta": "+12%"},
{"label": "Profit", "value": "$340K", "delta": "+8%"},
{"label": "Users", "value": "3,400", "delta": "+200"},
])
# DataFrames and tables
n.table(df, name="Table Title", max_rows=30)
n.dataframe(df, name="DataFrame Title")
n.summary(df, title="Auto Summary") # shape, nulls, numeric stats
# Key-value dictionaries
n.kv({"Key1": "Value1", "Key2": "Value2"}, title="Metrics")
# JSON display
n.json({"key": "value", "nested": {"a": 1}}, expanded=True)
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.
- 2mo ago First seen · 236 lines · 49 tokens per session scan A 54c3013fed5c
notebookmd-guide is a skill published in the GitHub repository minhlucvan/notebookmd (10 stars, last pushed 6mo ago), licensed MIT. It adds 49 tokens to every session and 1,934 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 2026-08-31.
notebookmd-guide compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| notebookmd-guideminhlucvan/notebookmd | Skill | 10 | 6mo ago | MIT | A | 49 | Claude Code |
| reporting-pipelinesbobmatnyc/claude-mpm-skills | Skill | 78 | 3mo ago | MIT | A | 25 | Claude Code |
| html-artifact-reportDeepCogNeural/html-artifact-report-skill | Skill | 2 | 1mo ago | MIT | A | 24 | Claude Code, Codex |
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| publishingdanjdewhurst/story-skills | Skill | 392 | 2d ago | MIT | A | 204 | Claude Code |