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 aayushostwal/nexus --skill tutorialgit clone --depth 1 https://github.com/aayushostwal/nexusWrote 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/aayushostwal/nexus/tutorial)<a href="https://agentmods.dev/skills/aayushostwal/nexus/tutorial"><img src="https://agentmods.dev/badge/skills/aayushostwal/nexus/tutorial/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/aayushostwal/nexus/tutorial"><img src="https://agentmods.dev/badge/skills/aayushostwal/nexus/tutorial.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 | $0.00053 | $0.01365 |
| Opus 5.5 | $0.00021 | $0.00546 |
| Sonnet 5.5 | $0.00011 | $0.00273 |
| Haiku 4.5 | $0.00005 | $0.00136 |
Grade A, and why
tutorial 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 19d 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 — 131 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Tutorial Generation Protocol
Produce complete, executable Jupyter Notebooks that work on the first run.
Compatibility
- Language: Python 3.10+
- Output:
.ipynbfile +Makefile - Style: PEP 8, GitHub-renderable Markdown, clean saved outputs
Workflow
Step 1 — Reproducibility Block
Begin every notebook with:
- Shell commands cell:
python -m venv .venv && source .venv/bin/activate pip installcell with all required libraries (pin versions:langchain==0.2.0)- Kernel check instructions as a Markdown cell
Makefilewithmake jupytertarget and Python version pin (e.g..python-versionfile)
Step 2 — Production-Ready Configuration
- Create a Pydantic
BaseSettingsclass to validate all environment variables on startup - Load secrets with
python-dotenv; raise an explicitValueErrorwith a helpful message if a key is missing - Document every production failure mode: missing keys, rate limits, model errors, network timeouts
Step 3 — Structural Outline
Create Markdown cells with headers H1–H3:
- H1 — Title: What the tutorial builds in one sentence
- H2 — Objective: One paragraph "what you will build and why"
- H2 — Prerequisites: List exact API keys, GPU requirements, or account setup steps
- H2 — Architecture Diagram: Mermaid diagram showing end-to-end system flow
Step 4 — Code Implementation
Rules for every code cell:
- Self-contained or explicitly references previously defined variables
- Python type hints on all function signatures
- Comments explain why — not what (bad:
# create list, good:# dedup before sending to avoid API double-charge) - One concept per cell; max ~30 lines per cell — split into functions if longer
- Never hardcode credentials; always use
settings.api_keyfrom the Pydantic config
Step 5 — Explanatory Narrative
Between every pair of code cells, add a Markdown cell that:
- States the "why" behind the implementation choice (e.g., "We use a ReAct loop here because it lets the agent decide when to call tools vs. answer directly")
- Describes the expected output when the cell runs
- Lists one or two common errors with their fixes (e.g., "RateLimitError → add
time.sleep(1)between calls")
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 19d ago Changed · -1 lines · -11 tokens per session 97a1550b3efd
- 1mo ago First seen · 132 lines · 64 tokens per session scan A 1bc56890c63d
tutorial is a skill published in the GitHub repository aayushostwal/nexus (19 stars, last pushed 10d ago), licensed MIT. It adds 53 tokens to every session and 1,365 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 2026-08-30.
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