llm-coding-guardrails

llm-coding-guardrails is a skill for Codex from itseffi/agentic-os. It costs 25 tokens per session (533 once invoked), scanned A, original, MIT.

A set of coding guidelines for reducing common mistakes made by language models, with an emphasis on checking work before acting.

In plain words
What is it for?
Use it to guide AI coding behavior, encourage caution around code changes, and reduce recurring implementation mistakes.
Why use it?
It helps address errors that can occur when an AI assistant changes or generates code too quickly.

Skill for Codex

Written for Codex: agents/openai.yaml present. Also seen: installed under .agents/ (shared by several agents).

Good fit Use it to guide AI coding behavior, encourage caution around code changes, and reduce recurring implementation mistakes.

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Install with agentmods
npx agentmods add skills/itseffi/agentic-os/llm-coding-guardrails
Install

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.

Any agent
npx skills add itseffi/agentic-os --skill llm-coding-guardrails
Clone the repo
git clone --depth 1 https://github.com/itseffi/agentic-os

Made for: Codex.

Wrote 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.

agentmods badge for llm-coding-guardrails

README.md
[![agentmods](https://agentmods.dev/badge/skills/itseffi/agentic-os/llm-coding-guardrails/github.svg)](https://agentmods.dev/skills/itseffi/agentic-os/llm-coding-guardrails)
Your own site
<a href="https://agentmods.dev/skills/itseffi/agentic-os/llm-coding-guardrails"><img src="https://agentmods.dev/badge/skills/itseffi/agentic-os/llm-coding-guardrails/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.

agentmods 80×15 button for llm-coding-guardrails

Your own site · 80×15
<a href="https://agentmods.dev/skills/itseffi/agentic-os/llm-coding-guardrails"><img src="https://agentmods.dev/badge/skills/itseffi/agentic-os/llm-coding-guardrails.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 25 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 533 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00025 $0.00533
Opus 5.5 $0.00010 $0.00213
Sonnet 5.5 $0.00005 $0.00107
Haiku 4.5 $0.00003 $0.00053

Measured 1mo ago against content hash 2da5e5c4766c, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

llm-coding-guardrails 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 1mo 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.

.agents/skills/llm-coding-guardrails/SKILL.md · 77 lines

How it starts

The opening of the file, as written. The whole thing — 77 lines — stays where its author put it; the contents beside it link to each section on GitHub.

LLM Coding Guardrails

Behavioral guidelines to reduce common LLM coding mistakes.

Tradeoff: These guidelines bias toward caution over speed. For trivial tasks, use judgment.

1. Think Before Coding

Do not assume. Do not hide confusion. Surface tradeoffs.

Before implementing:

  • State assumptions explicitly. If uncertain, ask.
  • If multiple interpretations exist, present them. Do not pick silently.
  • If a simpler approach exists, say so.
  • If something is unclear, stop and ask.

2. Simplicity First

Write the minimum code that solves the problem. Nothing speculative.

  • No features beyond what was asked.
  • No abstractions for single-use code.
  • No configurability that was not requested.
  • No error handling for impossible scenarios.
  • If code is substantially longer than needed, simplify it.

Quality check:

  • Would a senior engineer call this overcomplicated?
  • If yes, simplify.

3. Surgical Changes

Touch only what is necessary. Clean up only what your change affected.

When editing existing code:

  • Do not improve adjacent code, comments, or formatting unless required.
  • Do not refactor unrelated working code.
  • Match existing style unless asked to change it.
  • If you notice unrelated dead code, mention it. Do not delete it.

When your changes create orphans:

  • Remove imports/variables/functions made unused by your own change.
  • Do not remove pre-existing dead code unless asked.

Test:

  • Every changed line must map directly to the request.

4. Goal-Driven Execution

Define success criteria and iterate until verified.

Turn requests into verifiable goals:

  • "Add validation" -> "Write tests for invalid inputs, then make them pass"
  • "Fix the bug" -> "Write a test that reproduces it, then make it pass"
  • "Refactor X" -> "Ensure tests pass before and after"

For multi-step tasks, state a brief plan with checks:

  1. [Step] -> verify: [check]
  2. [Step] -> verify: [check]
  3. [Step] -> verify: [check]

Strong success criteria support independent iteration. Weak criteria (for example, "make it work") require repeated clarification.

Read the full file on GitHub · 77 lines

Files

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.

Changes

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.

  1. 1mo ago First seen · 77 lines · 25 tokens per session scan A 2da5e5c4766c

Subscribe to this mod's changes

llm-coding-guardrails is a skill published in the GitHub repository itseffi/agentic-os (117 stars, last pushed 15d ago), licensed MIT. It adds 25 tokens to every session and 533 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-30.

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