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 compilergit 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/compiler)<a href="https://agentmods.dev/skills/ara-labs/agent-native-research-artifact/compiler"><img src="https://agentmods.dev/badge/skills/ara-labs/agent-native-research-artifact/compiler.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 1 finding, up to medium
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- medium Excessive Agency · line 269 Skill enables autonomous high-impact decisions without human-in-the-loop verification. Critical operations (destructive commands, financial transactions, data deletion) should require explicit user confirmation.Fix: Add human-in-the-loop confirmation for destructive, irreversible, or high-impact operations. Never auto-execute commands that modify files, send data, or alter system state.
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 | $0.00163 | $0.06019 |
| Opus 5 | $0.00081 | $0.03010 |
| Sonnet 5 | $0.00033 | $0.01204 |
| Haiku 4.5 | $0.00016 | $0.00602 |
Grade A, and why
compiler scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Universal ARA Compiler
You are the ARA Universal Compiler. Your job: take ANY research input and produce a complete, validated ARA artifact. You operate as a first-class Claude Code agent — use your native tools (Read, Write, Edit, Bash, Glob, Grep) directly. No API wrapper needed.
Input Philosophy
The compiler is open-ended. It accepts anything that contains research knowledge — papers, repos, code, notebooks, logs, configs, notes, threads, a verbal description, combinations, or nothing at all (build interactively). Figure out what you've been given and extract maximum structured knowledge from it.
When arguments are provided ($ARGUMENTS), interpret them flexibly: paths → read; URLs →
fetch/clone; --output <dir> → where to write (default ./ara-output/); --rubric <path> →
PaperBench rubric for coverage mapping; anything else → context (ask only if it genuinely blocks).
Input Reading Strategy
- Identify what you have. Glob, read, explore the inputs before committing to a plan.
- Maximize coverage. Cross-reference all sources — a PDF gives narrative + claims; code gives ground-truth implementation; logs give the trajectory; notes give dead ends that never reached the paper.
- Decide, then flag. Resolve ambiguity with your own judgment and proceed. Only pause to ask the user when a choice is both genuinely undecidable from the inputs and material to the result (see Rule 15 for the repo-vs-paper conflict case). Never hallucinate to fill a gap; mark it.
- Handle partial inputs gracefully. Populate what you can with high confidence; mark gaps with "Not available from provided input" and tell the user what's missing.
Workflow
1. READ all inputs
2. REASON through the 4-stage epistemic protocol (see below)
3. GENERATE files (the mandatory core + whatever additional files the paper's content warrants)
4. COVERAGE CHECK loop (max 3 rounds): re-read source → diff against ARA → patch gaps
5. VALIDATE by running Seal Level 1
6. FIX any failures, re-validate
7. REPORT summary to user
What ships with it
4 files 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.
- 8d ago First seen · 321 lines · 163 tokens per session scan A b7555bb021d8
compiler is a skill published in the GitHub repository ARA-Labs/Agent-Native-Research-Artifact (676 stars, last pushed 14d ago), licensed MIT. It adds 163 tokens to every session and 6,019 once invoked, about $0.0008 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
systematic-debugging
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes.
local-ai-agents
Build local-first AI agents that run entirely on a developer workstation with Microsoft Foundry Local and Qwen function-calling models. Covers Small Language Models (SLMs), the OpenAI-compatible local endpoint, sandboxed local tools, local RAG with Chroma, local MCP servers, hybrid cloud/local routing, and the…
next-cache-components-adoption
Turn on Cache Components in a Next.js app and resolve the blocking routes it surfaces. Use when the user wants to enable, adopt, or migrate to Cache Components, flip the cacheComponents flag, work through a flood of blocking-prerender / instant validation errors, run the cache-components-instant-false codemod, or…
next-cache-components-optimizer
Drive a Next.js route to instant navigation by setting up an agentic loop, under Cache Components / PPR, on initial load (hard navigation) and client-side navigation (soft navigation). Encode the goal as a failing @next/playwright instant() e2e and work it to green, one verified route at a time; the shipped test then…
next-partial-prefetching-adoption
Turn on Partial Prefetching in a Next.js app and work through the insights it surfaces. Use when the user wants to enable or adopt Partial Prefetching, flip the partialPrefetching flag, opt routes in with export const prefetch = 'partial', audit Link prefetch={true} behavior, preserve existing prefetched UI with…
chronicle
Analyze Copilot session history for standup reports, usage tips, session search, and session reindexing. Use when the user asks for a standup, daily summary, usage tips, workflow recommendations, wants to search or find past sessions by keyword/file/PR, wants to reindex their session store, or asks about deleting…