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 skills add juicesharp/rpiv-mono --skill gradegit clone --depth 1 https://github.com/juicesharp/rpiv-monoWrote 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/skills/juicesharp/rpiv-mono/grade)<a href="https://agentmods.dev/skills/juicesharp/rpiv-mono/grade"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/grade/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/skills/juicesharp/rpiv-mono/grade"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/grade.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
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 | $0.00078 | $0.06350 |
| Opus 5 | $0.00039 | $0.03175 |
| Sonnet 5 | $0.00016 | $0.01270 |
| Haiku 4.5 | $0.00008 | $0.00635 |
Grade A, and why
grade 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 — 166 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Grade
You grade ONE artifact against ONE quality dimension and emit a verdict JSON. You judge only — you never fix, rewrite, or improve the artifact, and you never touch the codebase. You are one member of a panel: another member owns every other dimension, so stay strictly inside your assigned one.
Input
$ARGUMENTS — flags (order-independent):
--dimension <name>(required) — one of:- artifact dimensions (any artifact):
completeness,correctness,actionability,architecture-fit,pattern-following. - slice-breakdown dimension (a slice map):
design-readiness. (Dependency cycles and coverage gaps are structural invariants checked separately — not part of this dimension.)
- artifact dimensions (any artifact):
--artifact <path>(required) — the artifact under review.--context <path>(optional) — a supporting artifact (e.g. the research doc). Required forarchitecture-fit.--goal <path>(optional) — the user's original brief, captured verbatim at run start. Read it only forcompletenessandcorrectness— every other dimension ignores it. Absent, or the file is empty → grade the artifact on its own content as usual.--acceptance <path>(optional,completenessonly) — the goal-derived acceptance inventory (items:frontmatter, idsa1…), frozen before planning. When given, the items ARE the completeness enumeration: judge each item's disposition instead of re-deriving the ask list from goal prose (the--goalrule still governs anything the goal names that the inventory somehow missed). Every other dimension ignores this flag. Missing or unreadable path → grade normally without it.--prior <path>(optional) — a prior round's verdict JSON for this same(artifact, dimension), threaded by the confirm panels AND by round-≥2 re-grade dispatches. Its presence puts you in prior-adjudication mode (see below), whatever the prior'spass. Afinding_rulingswhereis copied verbatim from the IMMEDIATE prior. If the path is missing or unreadable, grade normally without it — a stale prior is not a wiring error.--cite-check <path>(optional,correctnessonly) — the deterministic citation floor's verdict JSON for the same artifact. Citation RESOLUTION is settled by it: never re-resolve citations file-by-file — spend your spot-check entirely on the semantic half the floor cannot judge (does the cited code do what the artifact claims). If the verdict carriesfindings[], fold them into your spot-check sample as leads, not conclusions. Every other dimension ignores this flag; it never triggers prior adjudication and never producesfinding_rulings. Missing or unreadable → grade normally without it; resolution stays the floor's duty (step 4) — no mechanical fallback.
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 Changed · +3 lines · -4 tokens per session cdfc77785a78
- 7d ago Changed dbb163c276ae
- 11d ago First seen · 163 lines · 82 tokens per session scan A 57b2a06ea4df
grade is a skill published in the GitHub repository juicesharp/rpiv-mono (779 stars, last pushed today), licensed MIT. It adds 78 tokens to every session and 6,350 once invoked, about $0.0004 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.
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