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 tmolavi/mcp-agent-skills-hub --skill bash-defensive-patternsgit clone --depth 1 https://github.com/tmolavi/mcp-agent-skills-hubWrote 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/tmolavi/mcp-agent-skills-hub/bash-defensive-patterns)<a href="https://agentmods.dev/skills/tmolavi/mcp-agent-skills-hub/bash-defensive-patterns"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/bash-defensive-patterns/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/tmolavi/mcp-agent-skills-hub/bash-defensive-patterns"><img src="https://agentmods.dev/badge/skills/tmolavi/mcp-agent-skills-hub/bash-defensive-patterns.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 | $0.00036 | $0.00311 |
| Opus 5 | $0.00018 | $0.00156 |
| Sonnet 5 | $0.00007 | $0.00062 |
| Haiku 4.5 | $0.00004 | $0.00031 |
Grade A, and why
bash-defensive-patterns 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 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.
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.
This is a copy
100% identical to bash-defensive-patterns — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Bash Defensive Patterns
Comprehensive guidance for writing production-ready Bash scripts using defensive programming techniques, error handling, and safety best practices to prevent common pitfalls and ensure reliability.
Use this skill when
- Writing production automation scripts
- Building CI/CD pipeline scripts
- Creating system administration utilities
- Developing error-resilient deployment automation
- Writing scripts that must handle edge cases safely
- Building maintainable shell script libraries
- Implementing comprehensive logging and monitoring
- Creating scripts that must work across different platforms
Do not use this skill when
- You need a single ad-hoc shell command, not a script
- The target environment requires strict POSIX sh only
- The task is unrelated to shell scripting or automation
Instructions
- Confirm the target shell, OS, and execution environment.
- Enable strict mode and safe defaults from the start.
- Validate inputs, quote variables, and handle files safely.
- Add logging, error traps, and basic tests.
Safety
- Avoid destructive commands without confirmation or dry-run flags.
- Do not run scripts as root unless strictly required.
Refer to resources/implementation-playbook.md for detailed patterns, checklists, and templates.
Resources
resources/implementation-playbook.mdfor detailed patterns, checklists, and templates.
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.
- 6d ago First seen · 44 lines · 36 tokens per session scan A bbe18fd4a2d1
bash-defensive-patterns is a skill published in the GitHub repository tmolavi/mcp-agent-skills-hub (8 stars, last pushed 14d ago), licensed MIT. It adds 36 tokens to every session and 311 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to bash-defensive-patterns, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
data-pro-skill
Market research data analysis meta-prompt. Transforms raw quantitative and qualitative data into dense, Tufte-style analytical documents. Document-driven. Invisible agent loop: Statistician -> Critic -> Tufte Designer. Commands: /dps-setup, /dps-cross, /dps-inject-open, /dps-export. Modes: /dps-mode:quant…
deploy-planner
Deployment and DevOps agent that generates Dockerfiles, CI/CD configs, and step-by-step deployment guides for free hosting platforms. Triggers on: deploy, launch, hosting, Docker, CI/CD, production, go live, ship it.
github-pr-workflow
GitHub PR lifecycle: branch, commit, open, CI, merge.
bash-defensive-patterns
Master defensive Bash programming techniques for production-grade scripts. Use when writing robust shell scripts, CI/CD pipelines, or system utilities requiring fault tolerance and safety.
tdd-configure-ci
Configures CI/CD pipelines to mechanically enforce CONSTRAINTS.md quality bars and Floor-Guard anti-cheat rules. (Optional Utility).
bailian-train-deploy
A workflow for using Alibaba Cloud’s Bailian command-line tool to fine-tune or directly deploy AI models as callable services. It covers text, speech-synthesis, image-generation, and video-generation models.