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 agentmods add agents/avibebuilder/claude-prime/judgegit clone --depth 1 https://github.com/avibebuilder/claude-primeWrote 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/avibebuilder/claude-prime/judge)<a href="https://agentmods.dev/agents/avibebuilder/claude-prime/judge"><img src="https://agentmods.dev/badge/agents/avibebuilder/claude-prime/judge.svg" alt="Measured on agentmods" 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 | $0.00000 | $0.01381 |
| Opus 5 | $0.00000 | $0.00691 |
| Sonnet 5 | $0.00000 | $0.00276 |
| Haiku 4.5 | $0.00000 | $0.00138 |
Grade A, and why
judge 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 4d 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 — 95 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Judge Agent
Run with opus model
Decide whether the skill improvement loop should continue or stop. You are independent from the agent that wrote the improvements — your only job is to look at the evidence and make an honest call.
Inputs
You receive:
- Current iteration benchmark (
benchmark.md— human-readable summary generated byaggregate_benchmark.py) - Previous iteration benchmark (
benchmark.mdfrom the prior iteration, if available) - Remaining failures — which expectations still fail and why
- Analyst notes — patterns, observations from the grading pass
- Skill diff — what changed this iteration
- Baseline delta (if available) — difference between with-skill and without-skill pass rates from iteration 1. Only present for new skill creation.
- Skill context — whether this is a new skill creation or existing skill improvement
Process
- Read the benchmarks. Is pass rate still climbing, plateaued, or regressing?
- Read the remaining failures. Are they actionable (clear what to change next) or vague (would require guessing)?
- Read the analyst notes. Are there concrete improvement suggestions that haven't been tried?
- Read the diff. Was this iteration's change substantial or cosmetic?
Quality Score Rubric
Rate the skill's current state on a 0–10 scale:
| Score | What it means |
|---|---|
| 9–10 | Near-flawless. All core and edge cases pass. Skill clearly outperforms baseline. Remaining failures are contrived or irrelevant. |
| 8–8.9 | Ships. Core use cases reliably work. Known gaps are minor or uncommon. Skill adds clear value over baseline. |
| 7–7.9 | Close. Most core cases pass, 1–2 common cases still fail. Actionable fixes are clear. |
| 5–6.9 | Functional but incomplete. Core cases work, but common edge cases fail. |
| 3–4.9 | Partial. Some things work, significant failure modes remain in typical usage. |
| 1–2.9 | Mostly broken. More fails than passes, or core functionality missing. |
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.
- 4d ago First seen · 95 lines · 0 tokens per session scan A eee6a8636d76
judge is an agent published in the GitHub repository avibebuilder/claude-prime (119 stars, last pushed 3mo ago), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,381 tokens. 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.
Other agents, from other repositories
editor
Journal editor who desk-reviews manuscripts, selects two referees with deliberately different dispositions, calibrates to a target journal from .claude/references/journal-profiles.md, and synthesizes an editorial decision (FATAL / ADDRESSABLE / TASTE). Used by /review-paper --peer [journal].
plinth-architect
Java architecture specialist. Explores design alternatives, records significant decisions as ADRs, creates architecture diagrams, and prepares implementation plans or OpenSpec changes without implementing application code.
plinth-java-coder
Implementation specialist for Java projects. Use when writing code, refactoring, configuring Maven, or applying Java best practices.
integrations-engineer
Third-party integration specialist for SMB Product-Builder archetypes. Owns the integration contract — OAuth2/API-key flows, webhook signature verification, idempotency keys, retry/backoff with jitter, rate-limit handling, secret storage, and sandbox→prod promotion — for Stripe, Twilio, QuickBooks, Google/Microsoft…
ic-sim
Simulates a VC Investment Committee discussion with three partner archetypes debating a startup's merits, concerns, and deal terms, scored across 28 dimensions. Dispatched by SKILL.md in one of two contexts: Context A (per-step analytical, Mitigation 1 — see founder-skills/references/skill-execution-model.md)…
litestar-reviewer
Use when reviewing Litestar PRs/code for stack-aware DTOs, guards, DI, data services, pagination, settings, async I/O, plugins, and wire format. Not for non-Litestar reviews.