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 CHENyiru3/AI-Skills-Collections --skill security-auditgit clone --depth 1 https://github.com/CHENyiru3/AI-Skills-CollectionsWrote 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/chenyiru3/ai-skills-collections/security-audit)<a href="https://agentmods.dev/skills/chenyiru3/ai-skills-collections/security-audit"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/security-audit/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/chenyiru3/ai-skills-collections/security-audit"><img src="https://agentmods.dev/badge/skills/chenyiru3/ai-skills-collections/security-audit.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 | $0.00072 | $0.00936 |
| Opus 5.5 | $0.00029 | $0.00374 |
| Sonnet 5.5 | $0.00014 | $0.00187 |
| Haiku 4.5 | $0.00007 | $0.00094 |
Grade A, and why
auditing-python-security 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 6d 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.
Runs shell commandslowCapability
Expected in a hook, worth knowing in a rule or an instructions file.
subprocess.run(["cat", filename], check=True) How it starts
The opening of the file, as written. The whole thing — 107 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Python Security Auditing
Quick Start
# Static analysis
bandit -r src/ -ll # High severity only
pip-audit # Dependency vulnerabilities
detect-secrets scan > .secrets.baseline # Secrets detection
Tool Configuration
Bandit (.bandit):
exclude_dirs: [tests/, docs/, .venv/]
skips: [B101] # assert_used - OK in tests
pip-audit:
pip-audit -r requirements.txt # Scan requirements
pip-audit --fix # Auto-fix vulnerabilities
Common Vulnerabilities
| Issue | Bandit ID | Fix |
|---|---|---|
| SQL injection | B608 | Use parameterized queries |
| Command injection | B602 | subprocess without shell=True |
| Hardcoded secrets | B105, B106 | Environment variables |
| Weak crypto | B303 | Use SHA-256+, bcrypt for passwords |
| Pickle untrusted data | B301 | Use JSON instead |
| Path traversal | B108 | Validate with Path.resolve() |
Secure Patterns
# SQL - Parameterized query
conn.execute("SELECT * FROM users WHERE id = ?", (user_id,))
# Commands - No shell
subprocess.run(["cat", filename], check=True)
# Secrets - Environment
API_KEY = os.environ.get("API_KEY")
# Paths - Validate
base = Path("/data").resolve()
file_path = (base / filename).resolve()
if not file_path.is_relative_to(base):
raise ValueError("Invalid path")
CI Integration
# .github/workflows/security.yml
- run: bandit -r src/ -ll
- run: pip-audit
- run: detect-secrets scan --all-files
For detailed patterns, see:
- VULNERABILITIES.md - Full vulnerability examples
- CI_SECURITY.md - Complete CI workflow
Audit Checklist
Code:
- [ ] No SQL injection (parameterized queries)
- [ ] No command injection (no shell=True)
- [ ] No hardcoded secrets
- [ ] No weak crypto (MD5/SHA1)
- [ ] Input validation on external data
- [ ] Path traversal prevention
Dependencies:
- [ ] pip-audit clean
- [ ] Minimal dependencies
- [ ] From trusted sources
CI:
- [ ] Security scan on every PR
- [ ] Weekly dependency scan
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.
- 6d ago First seen · 107 lines · 72 tokens per session scan A 91e1161a40d5
auditing-python-security is a skill published in the GitHub repository CHENyiru3/AI-Skills-Collections (1 stars, last pushed 7d ago), licensed MIT. It adds 72 tokens to every session and 936 once invoked, about $0.0003 per session on Opus 5.5. A static security scan graded it A with 1 finding (runs shell commands). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-10-02.
Other skills, from other repositories
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
holoscan-install-wheel
Install Holoscan SDK Python wheel via pip into a venv. Use for Python installs; not for native C++/apt or Conda installs.
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.
python
Python development with ruff, mypy, pytest - TDD and type safety.