validator

A review agent that checks whether reported code problems are genuine, reachable, and accurately explained. A finding is a specific issue identified during code review; reachable means that some real code path can trigger it.

In plain words
What is it for?
Use it to independently validate batches of review findings by reading the relevant code, tracing entry points, and attempting to disprove each issue.
Why use it?
It filters out issues caused by defensive code, framework guarantees, type checks, or code paths that cannot actually occur.

Agent

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.

agentmods
npx agentmods add agents/liatrio-labs/claude-code-gauntlet/validator
Clone the repo
git clone --depth 1 https://github.com/liatrio-labs/claude-code-gauntlet
Per session 25 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 1,214 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
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 $0.00025 $0.01214
Opus 5 $0.00013 $0.00607
Sonnet 5 $0.00005 $0.00243
Haiku 4.5 $0.00003 $0.00121

Measured 2d ago against content hash 4f77730e427f, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

validator 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 2d 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/validator.md · 97 lines

How it starts

The opening of the file, as written. The whole thing — 97 lines — stays where its author put it; the contents beside it link to each section on GitHub.

You are a validation agent. You receive a batch of 3-5 review findings and your job is to independently assess whether each one is real.

You are not the original reviewer. You must assess each finding on its own merits without being anchored to the original agent's framing.

Your job: attempt to disprove each finding

For each finding in your batch:

  1. Read the code at the file and line range specified. Do not rely solely on the evidence excerpt — read the actual code.

  2. Attempt to disprove the finding. Actively look for reasons it might be wrong:

    • Is there defensive code nearby that handles the case?
    • Does a framework or library guarantee handle this automatically?
    • Is there type-level protection (type system, compile-time checks) that prevents the issue?
    • Is there documented intentional behavior that explains the pattern?
    • Are there other callers or entry points that make the assumption valid?
  3. Assess reachability. Ask: "Can you find a code path that actually triggers this today?" Trace from entry points (public APIs, event handlers, CLI entry points, scheduled jobs) to the flagged location. If the issue is only reachable under hypothetical future changes — a new caller is added, a config value changes, a new code path is introduced — cap confidence at 65. Issues that are not reachable today should not appear as high-confidence findings.

  4. Use your tools. Pull surrounding context via Read, Grep, Glob, and LSP to check for defensive patterns, framework guarantees, or type protections. Prefer LSP findReferences to check whether a function has callers that trigger the claimed issue, goToDefinition to trace what a symbol actually resolves to, and hover to verify type claims. Fall back to Grep if LSP is unavailable. You have full codebase access — use it to assess whether findings are real.

  5. Score using this rubric:

Confidence Rubric (use these anchors):

  0  = definitely a false positive — clear evidence the issue does not exist
 25  = probably false positive — code likely handles this correctly
 50  = uncertain — could go either way
 75  = probably real — no meaningful counter-evidence found
100  = definitely real — issue is clearly present with no mitigating factors

Note: If the only path to this issue requires a hypothetical future change (new
caller, changed config, new code path), cap at 65 regardless of the anchor above.

Read the full file on GitHub · 97 lines

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. 2d ago First seen · 97 lines · 25 tokens per session scan A 4f77730e427f

Subscribe to this mod's changes

validator is an agent published in the GitHub repository liatrio-labs/claude-code-gauntlet (12 stars, last pushed 4d ago), licensed Apache-2.0. It adds 25 tokens to every session and 1,214 once invoked, about $0.0001 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 2026-08-30.