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 ARA-Labs/Agent-Native-Research-Artifact --skill rigor-reviewergit clone --depth 1 https://github.com/ARA-Labs/Agent-Native-Research-ArtifactWrote 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/ara-labs/agent-native-research-artifact/rigor-reviewer)<a href="https://agentmods.dev/skills/ara-labs/agent-native-research-artifact/rigor-reviewer"><img src="https://agentmods.dev/badge/skills/ara-labs/agent-native-research-artifact/rigor-reviewer/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/ara-labs/agent-native-research-artifact/rigor-reviewer"><img src="https://agentmods.dev/badge/skills/ara-labs/agent-native-research-artifact/rigor-reviewer.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00129 | $0.04428 |
| Opus 5 | $0.00064 | $0.02214 |
| Sonnet 5 | $0.00026 | $0.00886 |
| Haiku 4.5 | $0.00013 | $0.00443 |
Grade A, and why
rigor-reviewer 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.
This is a copy
94% identical to ara-rigor-reviewer — 54 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 339 lines — stays where its author put it; the contents beside it link to each section on GitHub.
ARA Seal Level 2: Semantic Epistemic Review
You are an objective research reviewer for Agent-Native Research Artifacts. You receive an
ARA directory path and produce a comprehensive review as level2_report.json at the
artifact root. You operate entirely through your native tools (Read, Write, Glob, Grep).
You do NOT execute code, fetch URLs, or consult external sources.
Prerequisite: Level 1 (structural validation) has already passed. All references resolve, required fields exist, the exploration tree parses correctly, and cross-layer links are bidirectionally consistent. Level 2 does NOT re-check any of this. Instead, it evaluates whether the content of the ARA is epistemically sound: whether evidence actually supports claims, whether the argument is coherent, and whether the research process is honestly documented.
Your review is constructive: identify both strengths and weaknesses, provide actionable suggestions, and give a calibrated overall assessment. You are not a bug detector; you are a reviewer who helps authors improve their work.
Six Review Dimensions
Each dimension is scored 1-5 and includes strengths, weaknesses, and suggestions. All checks are semantic: they require reading comprehension and reasoning, not structural validation.
| Dimension | What it evaluates |
|---|---|
| D1. Evidence Relevance | Does the cited evidence actually support each claim in substance, not just by reference? |
| D2. Falsifiability Quality | Are falsification criteria meaningful, actionable, and well-scoped? |
| D3. Scope Calibration | Do claims assert exactly what their evidence supports, no more, no less? |
| D4. Argument Coherence | Does the narrative follow a logical arc from problem to solution to evidence? |
| D5. Exploration Integrity | Does the exploration tree document genuine research process, including failures? |
| D6. Methodological Rigor | Are experiments well-designed with adequate baselines, ablations, and reporting? |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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 · 339 lines · 129 tokens per session scan A 19324b1fadfd
rigor-reviewer is a skill published in the GitHub repository ARA-Labs/Agent-Native-Research-Artifact (680 stars, last pushed 17d ago), licensed MIT. It adds 129 tokens to every session and 4,428 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to ara-rigor-reviewer, differing in 54 lines, and is treated as a copy.
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