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 adriannoes/awesome-agentic-ai --skill detecting-typosquatting-packages-in-npm-pypigit clone --depth 1 https://github.com/adriannoes/awesome-agentic-aiWrote 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/adriannoes/awesome-agentic-ai/detecting-typosquatting-packages-in-npm-pypi)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-typosquatting-packages-in-npm-pypi"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-typosquatting-packages-in-npm-pypi/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/adriannoes/awesome-agentic-ai/detecting-typosquatting-packages-in-npm-pypi"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-typosquatting-packages-in-npm-pypi.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 3 findings, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 148 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
- medium Data Exfiltration · line 95 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 128 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
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 | $0.00132 | $0.02994 |
| Opus 5 | $0.00066 | $0.01497 |
| Sonnet 5 | $0.00026 | $0.00599 |
| Haiku 4.5 | $0.00013 | $0.00299 |
Grade A, and why
detecting-typosquatting-packages-in-npm-pypi 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 9d 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 — 177 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting Typosquatting Packages in npm and PyPI
When to Use
- Auditing project dependencies to identify packages whose names are suspiciously similar to popular libraries
- Proactively scanning package registries for newly published packages that may be typosquats of your organization's packages
- Investigating a suspected supply chain compromise where a developer installed a misspelled package name
- Building automated monitoring that alerts when new packages appear with names close to critical dependencies
- Assessing the risk profile of unfamiliar packages before adding them to a project's dependency tree
Do not use as the sole determination of malicious intent; name similarity alone does not prove a package is malicious. Do not use for bulk automated takedown requests without manual review of flagged packages. Do not use against private registries without authorization.
Prerequisites
- Python 3.9+ with
requestsandpython-Levenshtein(orrapidfuzz) packages installed - Network access to
https://pypi.org/pypi/<package>/json(PyPI JSON API) andhttps://registry.npmjs.org/<package>(npm registry API) - A list of popular or critical packages to monitor (e.g., top 1000 PyPI packages, organization's dependency list)
- Understanding of common typosquatting patterns: character omission, transposition, insertion, substitution, and hyphen/underscore manipulation
Workflow
Step 1: Build the Target Package Watchlist
Establish the set of legitimate packages to monitor for typosquats:
- Extract project dependencies: Parse
requirements.txt,Pipfile.lock,package.json, orpackage-lock.jsonto extract all direct and transitive dependency names - Include popular packages: Supplement with high-value targets from the top 1000 PyPI downloads (available from
https://hugovk.github.io/top-pypi-packages/) or top npm packages by download count - Add organization packages: Include any packages published by your organization that attackers might target with typosquats to intercept internal installations
- Normalize names: PyPI treats hyphens, underscores, and periods as equivalent (PEP 503 normalization:
re.sub(r"[-_.]+", "-", name).lower()). npm package names are case-sensitive but scoped packages use@scope/nameformat. Normalize before comparison.
What ships with it
3 files 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.
- 9d ago First seen · 177 lines · 132 tokens per session scan A b3d46c0184c4
detecting-typosquatting-packages-in-npm-pypi is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (57 stars, last pushed 14d ago), licensed MIT. It adds 132 tokens to every session and 2,994 once invoked, about $0.0007 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 2026-09-03.
Other skills, from other repositories
performing-supply-chain-attack-simulation
Simulate and detect software supply chain attacks including typosquatting detection via Levenshtein distance, dependency confusion testing against private registries, package hash verification with pip, and known vulnerability scanning with pip-audit.
Supply Chain Security
Software supply chain security — SBOM generation and analysis, dependency confusion and typosquatting detection, malicious package indicators, CI/CD pipeline hardening, and artifact provenance/signing (SLSA, Sigstore).
bumblebee
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analyzing-supply-chain-malware-artifacts
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todos
This chat has a shared, live TODO plan — your tasks for the conversation, which the user also edits. Read this skill and reach for the todo tools whenever a request takes more than a couple of steps. It covers the plan model (group = task, items = its steps; loose items are the user's lane), how to work it: propose…
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Use when adding a new workflow skill to pi-thinkrail-workflow, changing an existing workflow skill's role, trigger, handoff, or structure, or checking a workflow skill against the workflow system's rules. Not for authoring general-purpose skills outside this package.