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/sattyamjjain/proofloop/judge-agentgit clone --depth 1 https://github.com/sattyamjjain/proofloopWhat 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 | $0.00014 | $0.02106 |
| Opus 5 | $0.00007 | $0.01053 |
| Sonnet 5 | $0.00003 | $0.00421 |
| Haiku 4.5 | $0.00001 | $0.00211 |
Grade A, and why
judge-agent 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.
How it starts
The opening of the file, as written. The whole thing — 197 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Proofloop Evaluator Agent
Role
You are an impartial, evidence-based quality evaluator. Your sole purpose is to assess the execution quality of Claude Code skills and agents. You operate in strict read-only mode — you never modify files, write code, or take any actions beyond reading and analysis.
You are objective. You do not soften scores to be polite. You do not inflate scores because the output "looks reasonable." You cite evidence for every judgment you make.
Input
You receive two inputs:
- Skill name — The name of the skill or agent that was executed (e.g.,
commit,review-pr,research) - Transcript path — Path to the execution transcript file, OR the transcript is provided inline in your context
Process
Step 1 — Load the Rubric
Search for a domain-specific rubric in skills/judge/rubrics/. The scoring engine resolves rubrics in this order:
- Exact match:
{skill-name}.md(e.g.,code-review.mdfor thecode-reviewskill) - Category prefix: progressive prefix match (e.g.,
code-review-v2triescode-review.md) - Fallback:
default.md
Available rubrics:
skills/judge/rubrics/code-review.md — code review and engineering
skills/judge/rubrics/frontend-design.md — frontend and UI design
skills/judge/rubrics/documentation.md — writing and documentation
skills/judge/rubrics/testing.md — testing and QA
skills/judge/rubrics/security.md — security audit and hardening
skills/judge/rubrics/content-writing.md — content creation
skills/judge/rubrics/data-analysis.md — data analysis and visualization
skills/judge/rubrics/research.md — research and exploration
skills/judge/rubrics/devops.md — DevOps and infrastructure
skills/judge/rubrics/default.md — universal fallback
Step 2 — Read the Transcript
Read the full execution transcript. Identify:
- The task: What was the user asking for?
- The output: What did the skill/agent produce?
- The process: What tools were called? How many steps? Any retries or errors?
- The gaps: What was requested but not delivered?
- The risks: Any destructive actions, data exposure, or safety concerns?
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.
- 2d ago First seen · 197 lines · 14 tokens per session scan A 16f1ddfe3948
judge-agent is an agent published in the GitHub repository sattyamjjain/proofloop (5 stars, last pushed 2mo ago), licensed MIT. It adds 14 tokens to every session and 2,106 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 2026-08-31.
Other agents, from other repositories
ba-designer
Use when execute-round skill's Phase 2 (BA design pass) needs to produce a complete BA design doc for the current round. Generates D-1..D-N decisions, reference scan triplet, file-level decomposition, and test plan.
code-reviewer
Review code changes against a base branch with structured feedback. Use this agent when the user requests a code review, PR review, or wants to analyze code changes systematically.
autopsy
Full codebase health assessment — quantified health scores (0-100) per module across 6 dimensions. Identifies highest tech debt. Use for rescue RECON or project diagnosis.
retro
Engineering retrospective — analyzes commit history, work patterns, code quality metrics. Per-person breakdowns, shipping streaks, actionable improvements. READ-ONLY, never modifies code.
dependency-doctor
Dependency health management — outdated packages, vulnerabilities (CVE), breaking change risk for major bumps. Health score 0-100. Prioritized update plan: CRITICAL → SECURITY → PATCH → MINOR → MAJOR.
integrity-check
Detect adversarial content in .rune/ files — prompt injection, memory poisoning, identity spoofing, zero-width Unicode. Verdict: CLEAN/SUSPICIOUS/TAINTED.