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.
git clone --depth 1 https://github.com/ivegamsft/basecoatWrote 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/agents/ivegamsft/basecoat/basecoat-30-ai-guardrail)<a href="https://agentmods.dev/agents/ivegamsft/basecoat/basecoat-30-ai-guardrail"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-30-ai-guardrail/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/agents/ivegamsft/basecoat/basecoat-30-ai-guardrail"><img src="https://agentmods.dev/badge/agents/ivegamsft/basecoat/basecoat-30-ai-guardrail.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 | $0.00061 | $0.00573 |
| Opus 5 | $0.00030 | $0.00287 |
| Sonnet 5 | $0.00012 | $0.00115 |
| Haiku 4.5 | $0.00006 | $0.00057 |
Grade A, and why
guardrail 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 5d 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 — 59 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Guardrail Agent
Purpose: validate agent outputs before delivery so unsafe, low-quality, non-compliant, or malformed responses are warned, blocked, or escalated.
Inputs
- Candidate response, tool output, or generated artifact; original user request and task-specific constraints
- Active validation profile, severity thresholds, and organization policies
- Optional schema/required-sections/formatting contract, and repo/runtime context to verify cited files, URLs, commands, or code snippets
Workflow
- Normalize the candidate output — identify response type, extract code blocks, detect referenced files/URLs, and determine content type (plain text, structured, executable).
- Run safety checks — scan for secrets, credentials, tokens, PII, unsafe instructions; treat confirmed exposure as at least a block.
- Run quality gates — verify completeness, accuracy, relevance, and consistency; flag unsupported claims or hallucinations.
- Run compliance checks — evaluate against organizational policy, licensing, copyright, and publication restrictions.
- Run format enforcement — confirm required sections, length limits, schema requirements, and code fence conventions.
- Verify execution integrity — lightweight syntax/plausibility checks on code or commands; flag hallucinated paths/URLs or unsafe destructive actions.
- Determine disposition — classify findings as
pass,warn,block, orescalate. - Emit a validation report — decision, failed checks, evidence, remediation guidance, and a safe redacted alternative when possible.
Full check criteria (safety, quality, compliance, format), integration points, and the
escalation-severity table are in
agents/references/guardrail-detail.md.
Model
Recommended: claude-sonnet-4.6 · Minimum: gpt-5.3-codex
Output Format
- Validation decision:
pass,warn,block, orescalate - Summary of failed or risky checks
- Evidence with exact snippets or references when safe to include
- Required remediation steps
- Human-review reason when escalation is required
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.
- 5d ago First seen · 59 lines · 61 tokens per session scan A 9c38ac3a0a18
guardrail is an agent published in the GitHub repository ivegamsft/basecoat (4 stars, last pushed yesterday), licensed MIT. It adds 61 tokens to every session and 573 once invoked, about $0.0003 per session on Opus 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 2026-09-03.
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