Luxas is an open-source multi-agent research system that takes a research topic through literature searches, experiments, reviews, and LaTeX manuscript compilation. It is intended for autonomous scientific research, using separate agents to read papers, run tests, create figures, and write reports. The catalogue lists skills associated with this workflow.
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 Muuuun/luxas --skill compute-methodsgit clone --depth 1 https://github.com/Muuuun/luxasWrote 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/muuuun/luxas/compute-methods)<a href="https://agentmods.dev/skills/muuuun/luxas/compute-methods"><img src="https://agentmods.dev/badge/skills/muuuun/luxas/compute-methods/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/muuuun/luxas/compute-methods"><img src="https://agentmods.dev/badge/skills/muuuun/luxas/compute-methods.svg" alt="Reviewed on agentmods" width="80" 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 analysis-evasion · line 1 Suspicious Unicode normalization or mixed-script contentFix: Review the flagged content for security risks. Ensure no credentials, secrets, or sensitive data are exposed.
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 | $0.00035 | $0.01569 |
| Opus 5.5 | $0.00014 | $0.00628 |
| Sonnet 5.5 | $0.00007 | $0.00314 |
| Haiku 4.5 | $0.00003 | $0.00157 |
Grade A, and why
compute-methods 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 5d 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 — 94 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Compute-methods registry
Why this exists (2026-07-13, debate-adjudicated): experiment agents abandon
the field-standard tool over first-use friction and fall back to
approximations that change the science. Canonical: pairinteraction rejected as
"requires manual database download" when the real fix was the species string
'Yb174_mqdt' — the ν¹¹-scaling fallback missed series splitting and a pair
resonance that refuted a headline claim. A sheet row is cheaper than a
debugging spiral: a friction listed here is a usage bug, not a tool failure.
Who reads the sheets: nobody by hand. buildMethodsRegistry
(src/agents/context-builders.ts) injects the matching ones into experiment,
tool_impl and tool_review contexts at spawn time. A sheet without match:
keywords is never injected. Sheets cover Rydberg pair interactions, QEC
circuits, qLDPC decoding, code distance, atom dynamics, optics, quantum
chemistry, and the simulators Blender, COMSOL, Zemax OpticStudio and SOLIDWORKS.
Sheet contract:
- frontmatter
match:comma-separated keywords (substring, case-insensitive) — ranked by hit count against RESEARCH.md + literature head + task text. A software sheet is matched against RESEARCH.md and the task text only: a paper in the literature notes that used a program does not select its sheet. Its keywords are matched as whole words, not as substrings (src/software.tsnamesKeyword): an ASCII letter or digit at either end of the keyword must not touch another one, soblenderdoes not hit in "deblender" or "BlenderBot" and does hit in 用Blender建模. One exception: digits may follow a keyword that ends in a letter, because a version is often written straight after the name (Blender4.2, COMSOL6.2, SolidWorks2022). A keyword that starts with a dot is a file extension and must not be followed by a letter, a digit,_or(. The same keywords, matched against RESEARCH.md, also decide the reviewer's "no saved model" notice (src/review-images.ts) and whether a model file far belowrun_N/counts, and one hit is enough for each. So a software sheet's keywords name the program, its API or its file format, and nothing more general: 三维建模,rigidbodyandgeometry nodessaid what a task is about and put the Blender sheet in front of a COMSOL brief's own. A whole word can still be another field's word:bpyis the ligand in[Ru(bpy)3]2+, so the sheet saysimport bpy. - frontmatter
verified:YYYY-MM-DD. Sheets withoutverified:are injected with an UNVERIFIED banner; older than 90 days get a STALE banner (frictions demote to hints).verified-on:vm|mac is a note for people; no code reads it. - Three optional keys for a sheet about one program (read by src/software.ts).
A line that breaks its rule is dropped silently at run time. For a sheet in
this directory smoke_sim_wiring (section 5) fails on a
software:line or anartifacts:entry that did not parse; a droppedbin:name is reported by nothing.software: <id>: lowercase letters, digits,-and_, alone on its line (no comment after it; the same id as in software.json). With any other spelling the sheet is treated as a plain method sheet: no line, no ride-along, no reviewer block. It ties the sheet to the operator's~/.sisyphus/software.jsonand selects the line printed above the sheet: RUNS ON a host that passedsimrun doctor, DECLARED on a host that has not (or whose entry says"desktop": true, which this build of the runner cannot start), FOUND on PATH, or NOT AVAILABLE. "Passed" is the runner's rule: a pass made against the host entry as it is now and, for software that declares acheck, one that ran it. Method sheets take the N slots among themselves; a program's sheet that ranks in the top N is added after them, and of the program sheets left the best one is appended. So a domain sheet cannot crowd the program's sheet out, and a program that is only mentioned cannot crowd a domain sheet out.bin: <name>[, <name>]executable names looked up on PATH of the machine Luxas runs on, when no registered host can run the software now.artifacts: .ext[, .ext]native model formats only. Each entry is a dot followed by letters or digits, matched against a file's last extension (x_t,.x_tand.tar.gzare ignored). The experiment reviewer is handed files with these extensions from the newest run that holds one, for re-opening (src/review-images.ts): any such file within three directories ofrun_N/; a deeper one when the brief names the program or results.json names the file; one below a hidden or installed-package directory only when results.json names it. A directory that is a symbolic link is not looked into (the reviewer is told how many were not). The sheet's Verification section is the only how-to: list a format only if a row there opens it, and write that section so that it stands alone (the reviewer is not given the smoke test or any other part of the sheet). An extension belongs to one sheet.
- A sheet with no
verified:date whose software is not reachable contributes only its header (file name and UNVERIFIED banner) and that line; its rows are withheld. Reachable, it is given whole, under a sentence that no row has been run against the program. - Body: per-tool table (frictions, smoke test), then
## Known false rejections— verbatim wrong claims from past ledgers, so a reviewer can recognize a recurrence. In a software sheet the section headed## Verification…is what the reviewer receives with the model file once the sheet is verified and the program reachable, without the rest of the sheet: it must stand alone (units and sign conventions inside). - After you fix a new friction in anger, add the row in the same session — the next agent inherits it.
- A software sheet earns its
verified:date from a simbench task with planted faults for that program, not from a smoke test: notes/sim-host-day-one.md.
What ships with it
12 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.
- 5d ago Changed · +58 lines · -78 tokens per session 083227d219ef
- 1mo ago First seen · 36 lines · 113 tokens per session scan A 072969d004fc
compute-methods is a skill published in the GitHub repository Muuuun/luxas (1,169 stars, last pushed 5d ago), licensed MIT. It adds 35 tokens to every session and 1,569 once invoked, about $0.0001 per session on Opus 5.5. 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-rigor
A structured method for doing research and analysis with explicit assumptions, evidence checks, scoring criteria, independent review, and later accuracy checks. The description is mainly in Chinese and does not define a specific software integration.
nature-academic-search
Multi-source literature search, citation verification, MeSH search strategy, citation file management (.nbib/.ris/.bib conversion), and reference management (BibTeX, related articles, ID conversion) via MCP tools (PubMed, CrossRef, arXiv). Use when the user needs coordinated multi-step literature workflows beyond a…
ml-paper-writing
Write publication-ready ML/AI papers for NeurIPS, ICML, ICLR, ACL, AAAI, COLM. Use when drafting papers from research repos, structuring arguments, verifying citations, or preparing camera-ready submissions. Includes LaTeX templates, reviewer guidelines, and citation verification workflows.
neuroskill-bci
Connect to a running NeuroSkill instance and incorporate the user's real-time cognitive and emotional state (focus, relaxation, mood, cognitive load, drowsiness, heart rate, HRV, sleep staging, and 40+ derived EXG scores) into responses. Requires a BCI wearable (Muse 2/S or OpenBCI) and the NeuroSkill desktop app…
sparse-autoencoder-training
Provides guidance for training and analyzing Sparse Autoencoders (SAEs) using SAELens to decompose neural network activations into interpretable features. Use when discovering interpretable features, analyzing superposition, or studying monosemantic representations in language models.
distributed-llm-pretraining-torchtitan
Provides PyTorch-native distributed LLM pretraining using torchtitan with 4D parallelism (FSDP2, TP, PP, CP). Use when pretraining Llama 3.1, DeepSeek V3, or custom models at scale from 8 to 512+ GPUs with Float8, torch.compile, and distributed checkpointing.