Borrowing it
Nothing to install: this file belongs to Sibyl-Research-Team/AutoResearch-SibylSystem. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/Sibyl-Research-Team/AutoResearch-SibylSystem/main/.claude/skills/sibyl-simulated-reviewer/SKILL.mdgit clone --depth 1 https://github.com/Sibyl-Research-Team/AutoResearch-SibylSystemWrote 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/sibyl-research-team/autoresearch-sibylsystem/sibyl-simulated-reviewer)<a href="https://agentmods.dev/skills/sibyl-research-team/autoresearch-sibylsystem/sibyl-simulated-reviewer"><img src="https://agentmods.dev/badge/skills/sibyl-research-team/autoresearch-sibylsystem/sibyl-simulated-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/sibyl-research-team/autoresearch-sibylsystem/sibyl-simulated-reviewer"><img src="https://agentmods.dev/badge/skills/sibyl-research-team/autoresearch-sibylsystem/sibyl-simulated-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 | $0.00027 | $0.00237 |
| Opus 5.5 | $0.00011 | $0.00095 |
| Sonnet 5.5 | $0.00005 | $0.00047 |
| Haiku 4.5 | $0.00003 | $0.00024 |
Grade A, and why
sibyl-simulated-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 1mo 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.
The source is not reproduced here
No licence file
A repository with no LICENSE is all rights reserved by default, so the body is not copied here. The metadata, the measurements and the link are.
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.
- 1mo ago First seen · 19 lines · 27 tokens per session scan A 4824994bd2ac
sibyl-simulated-reviewer is a skill published in the GitHub repository Sibyl-Research-Team/AutoResearch-SibylSystem (288 stars, last pushed 7mo ago), with no licence file. It adds 27 tokens to every session and 237 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
remote-compute
Use when the user asks to run, submit, monitor, or cancel a job on a remote machine over SSH — their own GPU/CPU server, a workstation, or a Slurm cluster ("the cluster", a login node, "my 3090 box", "the compute server"). Picks a saved machine, runs the work directly over SSH (or via Slurm when present), tracks it…
domain-check
Use ONLY when the user asks to check, validate, review or audit analysis code for scientific correctness, or when you are the reviewer agent running its checks. Never run it unprompted as part of ordinary analysis work. Covers physics, earth/geo, biology, chemistry, social science and bioprocess/fermentation. Runs a…
computer-use
Use when a task needs an app on the user's own screen — a native desktop application (instrument control software, Origin, GraphPad, ImageJ, a reference manager, a spreadsheet), or a desktop browser window that needs window-level control. Read before the first computer call. Covers the observe-act-observe loop, why…
publication-figures
Use whenever you generate or review a chart, plot, table, or paper figure in this workspace, including work delegated by paper-writing, literature-survey, and experiment skills. Applies the Open Science publication style, enforces readable final-size layout for figures and tables, and rejects generic diagram-tool…
large-file
Use BEFORE reading any data file that could be large (CSV/TSV, Parquet, HDF5, FITS, NetCDF, NDJSON, genomics FASTQ/FASTA/VCF/BAM, GRIB, ROOT, or big text/simulation logs like VASP OUTCAR). Returns a compact memory pointer — header/schema/shape/sample/key numbers — by introspection and sampling in bounded memory, so…
modal-run
Use when the user asks to run heavy or GPU work on Modal (the cloud compute platform) — writing a Modal function in the workspace, running it with the user's own modal CLI + token, and bringing results back. Data-to-compute for jobs too big for the laptop, without a Slurm cluster.