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 agentmods add skills/minhlucvan/notebookmd/reportnpx skills add minhlucvan/notebookmd --skill reportgit clone --depth 1 https://github.com/minhlucvan/notebookmdWhat 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 | $0.00046 | $0.00758 |
| Opus 5 | $0.00023 | $0.00379 |
| Sonnet 5 | $0.00009 | $0.00152 |
| Haiku 4.5 | $0.00005 | $0.00076 |
Grade A, and why
report 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 2d 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 — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Markdown Report Generator
Generate a structured, professional Markdown report using the notebookmd library.
Input
Report topic: $ARGUMENTS
Instructions
1. Setup
from notebookmd import nb, NotebookConfig
n = nb("dist/report.md", title="<descriptive title>")
2. Report Structure
Adapt the structure to the topic. Common patterns:
Executive Summary Pattern:
n.section("Executive Summary")
n.metric_row([...]) # Top-line KPIs
n.write("Brief overview...")
n.section("Background")
n.write("Context and motivation...")
n.section("Methodology")
with n.expander("Details"):
n.write("Step-by-step approach...")
n.section("Results")
n.table(results_df, name="Key Results")
n.kv(metrics_dict, title="Performance Metrics")
n.section("Conclusion")
n.success("Summary of outcomes")
Technical Report Pattern:
n.section("Overview")
n.kv(config_dict, title="Configuration")
n.section("Implementation")
n.code(code_snippet, lang="python")
n.note("Key technical detail...")
n.section("Results")
n.table(benchmarks_df, name="Benchmarks")
n.section("Next Steps")
n.write("1. Item one\n2. Item two")
Status Report Pattern:
n.section("Status Overview")
n.metric_row([
{"label": "Complete", "value": "73%", "delta": "+5%"},
{"label": "On Track", "value": "12/15"},
{"label": "Blocked", "value": "2"},
])
n.section("Completed This Period")
n.write("- Task 1\n- Task 2")
n.section("In Progress")
n.progress(0.73, "Overall completion")
n.write("Current work items...")
n.section("Risks & Blockers")
n.warning("Risk description...")
n.error("Blocker description...")
3. Available Widgets
Text: n.title(), n.header(), n.subheader(), n.write(), n.md(), n.caption(), n.code(), n.latex(), n.text()
Data: n.metric(), n.metric_row(), n.table(), n.dataframe(), n.kv(), n.json(), n.summary()
Status: n.success(), n.error(), n.warning(), n.info(), n.progress(), n.badge()
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.
- 2d ago First seen · 110 lines · 46 tokens per session scan A 51970695f0f9
report is a skill published in the GitHub repository minhlucvan/notebookmd (10 stars, last pushed 4mo ago), licensed MIT. It adds 46 tokens to every session and 758 once invoked, about $0.0002 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 2026-08-31.
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