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 zjunlp/SciAtlas --skill sciatlas-literature-reviewgit clone --depth 1 https://github.com/zjunlp/SciAtlasWrote 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/zjunlp/sciatlas/sciatlas-literature-review)<a href="https://agentmods.dev/skills/zjunlp/sciatlas/sciatlas-literature-review"><img src="https://agentmods.dev/badge/skills/zjunlp/sciatlas/sciatlas-literature-review/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/zjunlp/sciatlas/sciatlas-literature-review"><img src="https://agentmods.dev/badge/skills/zjunlp/sciatlas/sciatlas-literature-review.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 Excessive Agency · line 15 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 | $0.00101 | $0.01510 |
| Opus 5 | $0.00051 | $0.00755 |
| Sonnet 5 | $0.00020 | $0.00302 |
| Haiku 4.5 | $0.00010 | $0.00151 |
Grade A, and why
sciatlas-literature-review 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 12d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SciAtlas Literature Review
Use this skill to run the repository literature-review workflow. The workflow performs topic profiling, SciAtlas backend paper search, evidence organization, method clustering, time slicing, outline planning, evidence-pack construction, and optionally full section drafting/integration.
Operating Contract
- Run only
sciatlas literature-revieworpython run_sciatlas.py literature-reviewfor this skill. - Own the end-to-end novice flow: install or locate the CLI, guide registration, configure
.envor shell variables, run the workflow, inspect artifacts, and synthesize the final review result. - Ask the user only for human-only values: missing topic/domain, email, verification code, SciAtlas token, LLM credentials that are not already configured, or one necessary scope clarification.
- Do not ask the user to run shell commands when tool access is available.
- Use
--workflow flashby default for interactive work. - Use
--workflow fullwhen the user requests a comprehensive formal review or when flash artifacts are too thin. - Never disclose full API keys or tokens.
- Read saved artifacts before answering.
Zero-Start Bootstrap
- Check whether the repository command works:
python run_sciatlas.py literature-review -h
If needed, fall back to sciatlas literature-review -h after installing the full checkout.
- This dedicated workflow requires a full SciAtlas checkout. If it is missing, clone the repository, change into it, then run
python -m pip install -e ./sciatlasandpython -m pip install -r requirements-workflows.txt. Do not use the GitHub#subdirectory=sciatlaspackage-only installation for this workflow. - Check current environment and
.envforSCIATLAS_API_KEYand LLM settings before asking the user. - If no SciAtlas token is configured, guide the user to
http://sciatlas.openkg.cn/register; ask for email, verification code, and returnedsciatlas_xxxtoken only when needed. - If LLM credentials are required and missing, ask only for the missing values. Use the user's provider values without printing them back.
- Configure the current shell or
.envyourself, then run the workflow.
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.
- 12d ago First seen · 143 lines · 101 tokens per session scan A d7d79aca9853
sciatlas-literature-review is a skill published in the GitHub repository zjunlp/SciAtlas (149 stars, last pushed today), licensed MIT. It adds 101 tokens to every session and 1,510 once invoked, about $0.0005 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.
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