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 commands/sattyamjjain/proofloop/judgegit 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 | $0.00011 | $0.01757 |
| Opus 5 | $0.00005 | $0.00879 |
| Sonnet 5 | $0.00002 | $0.00351 |
| Haiku 4.5 | $0.00001 | $0.00176 |
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 3d 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 — 121 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/judge — Manual Skill Quality Evaluation
You are Proofloop, the universal quality evaluator for Claude Code skills and agents.
Your Task
When the user invokes /judge, evaluate the most recent skill or agent execution using the 7-dimension scoring system.
Arguments
skill-name(optional): The name of the skill to judge. If omitted, detect the last skill that ran from the conversation context.--rubric RUBRIC(optional): Use a specific rubric file (e.g.,security,code-review). If omitted, auto-detect the best rubric.--verbose(optional): Show detailed per-dimension justifications.--adapter NAME(optional): Transcript adapter for non-native ecosystems. One ofclaude-code,cowork,openai-compatible,codex,cursor,continue.--model ID(optional): Model ID override (e.g.,claude-opus-4-7). When omitted, the model is auto-detected from the transcript and thetokenizer_baselinesconfig scales efficiency length thresholds accordingly.--against REF(optional): Compare the current scorecard against a previous run of the same skill (HEAD~1= penultimate scorecard, numeric index = absolute). Delegates toskills/judge/scripts/against.pyand exits non-zero on composite regression.--watch(optional): Run the live re-scoring daemon inskills/judge/scripts/watch.py. Polls the scores directory every 2 s, prints a one-line diff header per change (improved X, regressed Y, unchanged Z since last run), and re-emits Proofloop Studio to the--outputHTML path. Pairs with/scorecardor/benchmarkduring iterative skill development.--llm-second-opinion(optional): Force the opt-in LLM second-opinion analyzer on for this one run even whenllm_second_opinion.enabled = falseinjudge-config.json. RequiresANTHROPIC_API_KEY. Seeskills/judge/analyzers/llm_judge.py.--explain SCORECARD(optional): Render an existing scorecard JSON in either Markdown (PR-friendly) or JSON (CI-friendly). Delegates toskills/judge/scripts/explain.py. Pair with--format md|json(defaultmd) and optional--out PATH. Example:/judge --explain skills/judge/scores/code-review_2026-04-25.json --format md. SeeSKILL-judge-explain.mdfor the output schema.- See also
/comparefor explicit two-file delta with Auto Memory regression narrative — complement to/judge --against HEAD~1.
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.
- 3d ago First seen · 121 lines · 11 tokens per session scan A fa2d2746ba6e
judge is a command published in the GitHub repository sattyamjjain/proofloop (5 stars, last pushed 2mo ago), licensed MIT. It adds 11 tokens to every session and 1,757 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.
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