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 acceptancegit 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/acceptance)<a href="https://agentmods.dev/skills/juicesharp/rpiv-mono/acceptance"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/acceptance/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/acceptance"><img src="https://agentmods.dev/badge/skills/juicesharp/rpiv-mono/acceptance.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 | $0.00122 | $0.01937 |
| Opus 5 | $0.00061 | $0.00968 |
| Sonnet 5 | $0.00024 | $0.00387 |
| Haiku 4.5 | $0.00012 | $0.00194 |
Grade A, and why
acceptance 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 11d 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 — 124 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Acceptance
You derive an acceptance inventory from the verbatim goal: the distinct observable outcomes the finished work must exhibit, each with a short stable id (a1, a2, …) and — wherever one can be derived — a runnable evidence command that will exit 0 once the outcome holds. One non-interactive pass, one Write. You do not plan, design, or judge feasibility — the inventory is the measure the later stages answer to, not a plan of how to get there.
The inventory exists to keep the standard of completion independent of the work: it is authored before any plan, from the goal alone, so a plan that silently narrows the brief is caught against enumerated items instead of prose re-reading. Downstream: the planner addresses or explicitly defers each item, the grade panel's completeness dimension receives the inventory as --acceptance, and validate runs the evidence commands against the finished tree — a failed item with a runnable command becomes a structured, remediable blocker.
Input
$ARGUMENTS — flags (order-independent):
--goal <path>(required) — the verbatim brief. Read it FULLY (no limit/offset). Missing/empty ⇒ print an error and stop — a dispatch error (the workflow runsgoalbeforeacceptance).--research <path>(optional) — the grounding doc. Read it FULLY. Research grounds only the evidence procedures (which command, which test path, which grep target); it NEVER adds, drops, or narrows items — the item set derives from the goal alone.
Metadata
node "${SKILL_DIR}/../_shared/now.mjs"
echo
node "${SKILL_DIR}/../_shared/git-context.mjs"
Copy values verbatim. <iso> is the first tab-separated field (use as date:); <slug> is the second.
What an item is
An acceptance item is one observable outcome the goal asks for — behavior a reader could check on the finished tree, not an implementation step. Good items are:
- Goal-traceable — the
statementrestates one explicit ask (or explicit constraint) from the goal in one line; quote or closely paraphrase the goal's own words. Never invent scope the goal doesn't name (the graders' anti-scope-inflation rule applies here first). - Observable — phrased as a checkable end state ("
/wf shiphalts at the grade gate with a route note"), never as activity ("implement the gate"). - Singular — one outcome per item; a goal sentence naming two outcomes yields two items.
- Right-sized set — typically 3–12 items; every explicit ask is covered, and nothing is padded. A one-line goal may legitimately yield a single item. The schema's hard ceiling is 24 — deliberately tighter than the plan/slice family's 32, because an inventory that large is re-litigating scope, not enumerating a brief; a goal genuinely that broad belongs in
build's slice decomposition, with each slice's asks staying items here only at the observable-outcome grain.
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.
- 11d ago First seen · 124 lines · 122 tokens per session scan A 33c354b6ea60
acceptance is a skill published in the GitHub repository juicesharp/rpiv-mono (773 stars, last pushed 2d ago), licensed MIT. It adds 122 tokens to every session and 1,937 once invoked, about $0.0006 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.
Other skills, from other repositories
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
tika-eval-compare
Compare extracts from two Tika builds over a corpus to detect regressions in content, encoding, exceptions, and embedded-document handling. Use for "compare before/after extracts", "eval this change against the corpus".
neuron-evaluation-engineer
Create and run AI evaluations with datasets, assertions, and output drivers in Neuron AI. Use this skill whenever the user mentions evaluation, testing AI systems, creating evaluators, dataset-driven testing, assertion-based validation, or wants to measure AI system performance. Also trigger for tasks involving…
jetson-validate-image
Use after jetson-flash-image to run static BSP checks, on-target smoke/regression tests on a flashed DUT, or both. Not for build or flash steps. Triggers: validate bsp, on-target validation.
atmos-validation
Validate Atmos projects, components, arbitrary JSON Schema inputs, EditorConfig, and GitHub Actions; use affected-file selection and native CI annotations.
skill-benchmark
Benchmark AI skill effectiveness by measuring implementation quality against legacy constraints.