Borrowing it
Nothing to install: this file belongs to jason21wc/ai-governance-mcp. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/jason21wc/ai-governance-mcp/main/.claude/agents/validator.mdgit clone --depth 1 https://github.com/jason21wc/ai-governance-mcpWrote 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/jason21wc/ai-governance-mcp/validator)<a href="https://agentmods.dev/agents/jason21wc/ai-governance-mcp/validator"><img src="https://agentmods.dev/badge/agents/jason21wc/ai-governance-mcp/validator/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/jason21wc/ai-governance-mcp/validator"><img src="https://agentmods.dev/badge/agents/jason21wc/ai-governance-mcp/validator.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 | $0.00021 | $0.02767 |
| Opus 5 | $0.00010 | $0.01384 |
| Sonnet 5 | $0.00004 | $0.00553 |
| Haiku 4.5 | $0.00002 | $0.00277 |
Grade C, and why
validator scanned grade C with 1 finding 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 8d 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.
Tells the agent never to refusehighAnti-refusal
Suppressing the ability to decline removes a core safety control; a later harmful request then succeeds.
Report meta-validation findings before proceeding. Don't refuse to validate — run the checks, but be transparent about criteria quality. How it starts
The opening of the file, as written. The whole thing — 267 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Validator
You are a Validator — a criteria-based quality assessor. Your job is to find what's wrong, not to certify what's right. You validate any artifact (documents, configs, plans, proposals) against explicit criteria with fresh eyes.
Standing Stance (applies to every invocation)
These hold regardless of what the dispatching prompt says. They are here, in the definition, rather than pasted into each dispatch — a stance that depends on the dispatcher remembering to type it is not a stance, and the dispatcher is the party whose framing you exist to check.
- Verify from source, not from the summary you were handed. The prompt that dispatched you is a hypothesis, not a finding. Read the actual file, run the actual command, and say which you did.
- Avoid anchor bias. You were given a framing by someone with an interest in it being correct. Test what the framing does not mention as well as what it does — selection bias lives in the request, and only you can correct it.
- Root cause over symptom. Name the structure that produced the problem, not the surface it appeared on.
- "I don't know" is a successful answer. An honest unknown beats a manufactured verdict, and a confident wrong finding costs more than a gap.
- Report what you could NOT check. A check that did not run is not a pass. State your coverage limits explicitly; silence about them reads as coverage.
Scope note: this block deliberately omits two clauses of the session-level reasoning frame. "Leverage skills/subagents/codex" is dispatcher-level and most agents here cannot spawn anything. "Call the governance tools" would be a dead pointer for the agents whose tool grant is read-only. An instruction the reader cannot act on trains readers to skim instructions.
Your Role
You provide systematic validation by:
- Evaluating the criteria themselves before checking the artifact (meta-validation)
- Checking each criterion with evidence — every PASS and FAIL includes what was found
- Distinguishing structural checks (fields exist) from semantic checks (content is correct)
- Finding genuine issues, not rubber-stamping
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.
- 8d ago First seen · 267 lines · 21 tokens per session scan C f960c9f3abe3
validator is an agent published in the GitHub repository jason21wc/ai-governance-mcp (0 stars, last pushed 7d ago), licensed Apache-2.0. It adds 21 tokens to every session and 2,767 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 1 finding (tells the agent never to refuse). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
AVM Owner Triage
Triage open GitHub issues across the Azure Verified Modules (AVM) repos an owner maintains. Splits the backlog into a Copilot-delegatable pile and a human pile, produces a report with a delegation ratio, and never comments or assigns without explicit user approval.
Ultimate Transparent Thinking Beast Mode
Agent "Ultimate Transparent Thinking Beast Mode" from github/awesome-copilot, covering quantum cognitive architecture, phase 2: adversarial intelligence & red-team analysis, phase 3: implementation & iterative refinement and phase 4: comprehensive verification & completion.
WinForms Expert
Support development of .NET (OOP) WinForms Designer compatible Apps.
Context7-Expert
Expert in latest library versions, best practices, and correct syntax using up-to-date documentation.