detecting-typosquatting-packages-in-npm-pypi
by mukul975detecting-typosquatting-packages-in-npm-pypi helps identify suspicious npm and PyPI packages by comparing name similarity, publish recency, and download anomalies. Use it for security audit workflows, dependency review, and first-pass supply-chain risk screening with a reproducible registry-check process.
This skill scores 78/100 and is a solid listing candidate. Directory users get a real, triggerable workflow for detecting typosquatting in npm and PyPI, with enough operational detail to decide it’s worth installing, though the workflow would be easier to adopt with a cleaner quick-start and more explicit execution guidance.
- Strong triggerability: the frontmatter explicitly targets typosquatting, dependency confusion, malicious package identification, and supply-chain threat hunting.
- Operational workflow is concrete: the skill body and API reference describe Levenshtein-based similarity checks, publish-date heuristics, and download/anomaly scoring against PyPI and npm APIs.
- Good agent leverage: supporting script and CLI examples show scan, scan-file, check, and generate flows, which reduces guesswork for automated use.
- Install command is missing from SKILL.md, so setup and activation may require more manual discovery than ideal.
- Some repository cues are incomplete in the previewed skill body, so users may need to consult the script/reference to fully understand edge-case handling and exact outputs.
Overview of detecting-typosquatting-packages-in-npm-pypi skill
What this skill does
The detecting-typosquatting-packages-in-npm-pypi skill helps you identify suspicious npm and PyPI packages that mimic legitimate dependencies through small name changes, fresh publish dates, and weak download history. It is most useful when you need a practical first-pass screen for supply-chain risk, not a full malware sandbox.
Best fit for security work
Use the detecting-typosquatting-packages-in-npm-pypi skill when you are reviewing dependencies, investigating a possible misspelled install, or doing detecting-typosquatting-packages-in-npm-pypi for Security Audit work on a package list. It fits analysts who want a reproducible registry-check workflow for npm and PyPI rather than a generic prompt that only guesses at suspicious names.
What makes it useful
The main value is its mix of similarity scoring and registry metadata checks: Levenshtein-style name comparison, publish recency, version patterns, and download-count anomalies. That combination is stronger than a name-only check because it helps separate harmless lookalikes from packages that are newly created, lightly used, and plausibly malicious.
How to Use detecting-typosquatting-packages-in-npm-pypi skill
Install and activate it
For the detecting-typosquatting-packages-in-npm-pypi install step, add the skill to your environment and then work from the skill folder so the included scripts and references are available. A typical install path is:
npx skills add mukul975/Anthropic-Cybersecurity-Skills --skill detecting-typosquatting-packages-in-npm-pypi
Then point your agent or workflow at the skill content before asking for package analysis.
Give the skill the right input
For effective detecting-typosquatting-packages-in-npm-pypi usage, provide the exact target package, the registry, and the reason for review. Strong inputs look like: “Check reqeusts against requests on PyPI and tell me whether it looks like a typosquat,” or “Audit these npm dependencies for likely typosquats before release.” Weak inputs like “look for bad packages” force the skill to guess the target.
Read these files first
For the fastest detecting-typosquatting-packages-in-npm-pypi guide, start with SKILL.md, then inspect references/api-reference.md for CLI patterns and scoring logic, and scripts/agent.py for the actual detection flow. Those three files tell you how the skill queries registries, what heuristics it uses, and where the operational limits are.
Practical workflow
Use the skill in three passes: define your target package set, run the registry comparison, then review only the highest-risk candidates manually. If you are scanning a dependency file, feed the project’s package.json or requirements.txt context into the analysis so the skill can compare names against the real dependency list instead of a hand-picked example.
detecting-typosquatting-packages-in-npm-pypi skill FAQ
Is this only for npm and PyPI?
Yes, the skill is centered on npm and PyPI registry metadata. If you need Maven, RubyGems, or crates.io analysis, this skill is not a direct fit and should not be forced into a broader package-security prompt.
Do I need Python skills to use it?
No. The detecting-typosquatting-packages-in-npm-pypi skill is beginner-friendly for analysts because the install and scan workflow is straightforward. You only need enough context to name the target package and decide whether you are checking one dependency or an entire dependency file.
How is this different from a normal prompt?
A normal prompt can ask “is this package suspicious,” but the detecting-typosquatting-packages-in-npm-pypi skill adds a repeatable method: candidate generation, registry lookup, and heuristic scoring. That makes the result easier to audit and compare across multiple packages.
When should I not use it?
Do not rely on it for runtime malware detection, code execution analysis, or confirmed attribution. It is best for screening package-name abuse and dependency-confusion-style risk, then handing off to deeper review when a candidate looks suspicious.
How to Improve detecting-typosquatting-packages-in-npm-pypi skill
Provide the target list, not just one name
Better results come from giving the detecting-typosquatting-packages-in-npm-pypi skill the actual dependency inventory, package manager, and target ecosystem. For example, “scan these 30 npm dependencies from package-lock.json and flag any names within one edit of the top 10 packages” is much stronger than asking for a vague package hunt.
State your review threshold
Tell the skill what should count as suspicious: same-looking name, recent publish date, low downloads, author mismatch, or version irregularity. This matters because detecting-typosquatting-packages-in-npm-pypi usage is most useful when the output matches your security bar, not when every near-match is treated equally.
Look for false positives and false negatives
The most common failure mode is over-trusting name similarity alone. Improve the result by asking the skill to compare additional signals and by reviewing any package that is close in spelling but clearly established, widely downloaded, or older than the target library.
Iterate with tighter prompts
After the first pass, refine by asking for a narrower set: “re-score only the top 5 candidates,” “exclude packages older than 2 years,” or “focus on packages with download counts far below the target.” That kind of iteration turns the detecting-typosquatting-packages-in-npm-pypi skill into a better detecting-typosquatting-packages-in-npm-pypi for Security Audit workflow instead of a one-shot search.