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 itseffi/agentic-os --skill llm-coding-guardrailsgit clone --depth 1 https://github.com/itseffi/agentic-osWrote 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/itseffi/agentic-os/llm-coding-guardrails)<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.
<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>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 | $0.00025 | $0.00533 |
| Opus 5.5 | $0.00010 | $0.00213 |
| Sonnet 5.5 | $0.00005 | $0.00107 |
| Haiku 4.5 | $0.00003 | $0.00053 |
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.
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:
- [Step] -> verify: [check]
- [Step] -> verify: [check]
- [Step] -> verify: [check]
Strong success criteria support independent iteration. Weak criteria (for example, "make it work") require repeated clarification.
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.
- 1mo ago First seen · 77 lines · 25 tokens per session scan A 2da5e5c4766c
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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