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-dependency-confusiongit 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-dependency-confusion)<a href="https://agentmods.dev/skills/adriannoes/awesome-agentic-ai/detecting-dependency-confusion"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-dependency-confusion/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-dependency-confusion"><img src="https://agentmods.dev/badge/skills/adriannoes/awesome-agentic-ai/detecting-dependency-confusion.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 5 findings, up to high
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 →
- high Privilege Escalation · line 31 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 53 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 135 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- high Privilege Escalation · line 220 Code accesses credential files (SSH keys, AWS credentials, etc.). This could indicate credential theft attempts.Fix: Remove references to credential paths. Use environment variables or secrets managers. For docs, use placeholder paths (e.g., /path/to/config). Never load .env or token files in production code paths.
- medium MCP Rug Pull · line 51 Docker image references without a specific tag (:latest is implicit) or digest (@sha256:...) can be silently replaced by a malicious image.Fix: Pin the image: image:tag or image@sha256:abc123
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 | $0.00024 | $0.02734 |
| Opus 5.5 | $0.00010 | $0.01094 |
| Sonnet 5.5 | $0.00005 | $0.00547 |
| Haiku 4.5 | $0.00002 | $0.00273 |
Grade A, and why
detecting-dependency-confusion scanned grade A with 1 finding 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.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -o /dev/null -w "%{http_code}\n" https://registry.npmjs.org/@acme%2finternal-utils How it starts
The opening of the file, as written. The whole thing — 225 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Detecting Dependency Confusion
Legal Notice: This skill is for authorized security testing, defensive engineering, and educational purposes only. Registering or claiming package namespaces you do not own, or testing build pipelines without written authorization, may be illegal and may violate the terms of service of public registries. Only run namespace-claiming or resolution-testing activities against names and infrastructure you control or are explicitly authorized to assess.
Overview
Dependency confusion (also called a substitution or namespace-shadowing attack) was popularized by Alex Birsan in 2021 when he forced malicious code into the internal build systems of Apple, Microsoft, PayPal, and dozens of others. The root cause is that many package managers, when configured to resolve from both an internal/private registry and a public one, will prefer whichever copy has the higher version number rather than honoring the source. An attacker who learns the name of a private package (@acme/internal-utils, acme-billing-sdk) can publish a malicious package of the same name to the public registry (npmjs.com, PyPI, Maven Central) with a very high version (e.g. 99.0.0). When the victim's CI/CD runner or a developer machine resolves dependencies, it pulls the attacker's public package, executes its install scripts, and the supply chain is compromised.
This skill covers both halves of the problem: detection — enumerating internal package names that are not registered (squatted defensively) on public registries and are therefore claimable, using confused and OWASP dep-scan — and prevention — pinning scopes/namespaces to private registries, registering placeholder packages, and enforcing source restrictions in .npmrc, pip.conf/pyproject.toml, and Maven settings.xml. Internal package names leak constantly: in committed lockfiles, sourcemaps, public JS bundles, Docker layers, and error stack traces, so this is treated as an attack-surface management problem, not a one-time check.
What ships with it
4 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.
- 2mo ago First seen · 225 lines · 24 tokens per session scan A ff2ca0885efe
detecting-dependency-confusion is a skill published in the GitHub repository adriannoes/awesome-agentic-ai (95 stars, last pushed 2d ago), licensed MIT. It adds 24 tokens to every session and 2,734 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
Other skills, from other repositories
bumblebee
Run Bumblebee supply-chain inventory and exposure scans on macOS/Linux to detect compromised packages, extensions, and MCP host configs.
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.
ci-mockup-figure
Create paper and proposal figures guided by confirmed visual preferences and efficient use of page space. HTML mockups for systems, dashboards, and timelines; TikZ or skia-canvas for abstract diagrams with arrow routing. Covers tool selection, design, capture, and LaTeX insertion.
AI/ML Model Testing
Testing machine learning models including accuracy validation, bias detection, drift monitoring, A/B testing, and model regression testing.
readme-polish
Audit a GitHub README and rewrite it using modern 2025-2026 patterns — centered header, badges, hero image, GitHub alert callouts, emoji-prefixed features, expandable details, Mermaid diagrams, tables over dense prose. Produces a scannable README that works for a 10-second skim and a deep dive.
my-router
Context-aware router that detects work type and dispatches to the right skill. Ships with a minimal default routing table; extend it in a fork, or register project-local skills in a consuming repo via routing-table.local.md or an AGENTS.local.md Routing section.