Agent skill · Audited from source
nature-reviewer
skill · yuan1z0825/nature-skillsApache-2.0 licenceAudited 31 Oct 2026 · source c5a4a9d64275
Is it safe? Yes — SAFE, grade B (89/100). We read this skill's source at commit c5a4a9d64275 and found no filesystem, network or shell access and no hard-coded secrets. What it does: Provide evidence-grounded mock peer review of scientific manuscripts or excerpts, covering significance, validity, and major/minor concerns. Use for 模拟审稿、投稿前自审、审稿人视角评估; not author rebuttal drafting.
Cite
Cite this page
ModelsAtlas. “nature-reviewer (yuan1z0825/nature-skills)”. Retrieved 31 Oct 2026. https://modelsatlas.com/skills/yuan1z0825/nature-reviewer
Trust audit
Audit 0.4.1 · source read 31 Oct 2026
SAFE means nothing in the source contradicts what the skill says it does. Grade B is shared by 16,667 of 17,602 audited skills.
What the source does
- FilesystemNone observedNo reads or writes in the source
- NetworkNone observedNo outbound calls in the source
- ShellNone observedNo commands run in the source
- DependenciesPinnedNothing floats
- SecretsNone foundNo keys in source
Five audit layers
| Layer | What we check | Result | Findings |
|---|---|---|---|
L0 Provenance | Repo owner, licence, archive status, activity | PASS | 0 |
L1 Static source | Secrets, path traversal, supply chain, hidden code | PASS | 0 |
L2 Instructions | Prompt injection, authority framing, hidden directives | PASS | 1 |
L3 Inventory | The files and declarations match the source | PASS | 0 |
L4 Sandbox | Run in isolation and watch behaviour | SKIPPED why | 0 |
Gates applied: no behavioural pass.
Findings (1)
LOWObfuscation / stealthreview.concealment · references/domain-specific-review-gates.md
Do not expose gate names, routing tables, pattern IDs, or this file's internal structure in the final report.
- Why
- The instruction explicitly directs the agent to hide its internal reasoning and structural markers from the user in the final output.
- Fix
- rewrite it so the instruction says plainly what it does, and asks the user before it acts
Audit 0.4.1 · source sha c5a4a9d64275 · 31 Oct 2026Report an issue or request a re-scan
Provenance
Who published it, and what the verdict is pinned to
- Publisheryuan1z0825 GitHub userOwner of the repository this skill is read from
- Source folderyuan1z0825/nature-skills/skills/nature-reviewerThe exact folder we audited
- LicenceApache-2.0As the repository states it
- Commit pinned for this verdictc5a4a9d64275A new audit runs when the source changes
- Repository statusNot archivedPushed today
- Sandboxed runNot runSkills are read, not executed; layer L4 is skipped
What it tells the agent
Full source on GitHub ↗Its description: Provide evidence-grounded mock peer review of scientific manuscripts or excerpts, covering significance, validity, and major/minor concerns. Use for 模拟审稿、投稿前自审、审稿人视角评估; not author rebuttal drafting.
--- name: nature-reviewer description: "Provide evidence-grounded mock peer review of scientific manuscripts or excerpts, covering significance, validity, and major/minor concerns. Use for 模拟审稿、投稿前自审、审稿人视角评估; not author rebuttal drafting." --- # Nature Reviewer Assessment Skill Use this skill to simulate a `Nature`-style reviewer assessment package from the referee side. This skill is for reviewer-style manuscript evaluation, not for drafting the authors' response. If the user wants rebuttal writing, route to `nature-response`. ## Default stance - Ground the review only in the local source basis plus manuscript facts supplied by the user. - Evaluate the manuscript against source-grounded axes: `originality`, `scientific importance`, `interdisciplinary readership`, `technical soundness`, and `readability for nonspecialists`. - Use the 12-axis technical concern taxonomy only as an internal coverage checklist; it supplements but never replaces the five source-grounded axes. - Return exactly `3 mutually blind reviewer reports + 1 post-review synthesis` unless the user explicitly asks for another structure. - Give every reviewer only the same immutable manuscript/source packet, the same journal criteria, and that reviewer's preassigned emphasis. Never provide another review, a shared concern ledger, a draft synthesis, or hints about what another reviewer noticed. - Run each reviewer in a genuinely separate context, subagent, process, or invocation. If the environment cannot isolate contexts, generate one reviewer report per invocation or explicitly state that mutual blindness cannot be guaranteed; never present shared-context drafting as independent peer review. - Define emphasis briefs before any report is generated. They are working lenses, not reviewer identities, specialties, institutions, or biographies. - Freeze each individual report before comparing them. Natural duplication or disagreement is valid evidence of independent review and must not be edited away to manufacture diversity. - After all reports are frozen, run `references/forensic-consistency-audit.md` as a separate editorial pass. Audit arithmetic, metric bounds, aggregation levels, prose-table ordering, duplicate displays, dispersion anomalies, provenance and reproducibility. Never feed audit findings back into reviewer contexts or edit frozen reports after comparison. - Identify who would be interested in the results and why. - Identify technical failings that must be addressed before the authors' case is established. - Give every substantive concern a stable ID, a faithful `claim_pointer`, and a verifiable `evidence_pointer`; mark missing locations instead of inventing them. - Separate user-visible concerns into `Major Concerns` and `Minor Comments`. Mark a Major Concern `Blocking Yes` only when the current manuscript cannot establish its central case until that concern is resolved; Minor Comments are never blocking. - Do not impose a concern quota. If no grounded concern exists at a level, state that explicitly instead of inventing one. - Keep the critique intellectually sharp but professionally phrased; severity comes from impact on the manuscript's case, not from hostile wording. - Avoid em dashes, en dashes, and colons as routine prose punctuation throughout reviewer reports and synthesis. Prefer a new sentence, comma, semicolon, parentheses, or a short heading followed by a new line. Retain ordinary hyphens in standard compound terms and stable IDs such as `R1-M1`. Preserve punctuation in source-faithful titles, quotations, formulas, identifiers, URLs, times, and required machine-readable syntax when changing it would be inaccurate. - Distinguish clearly between what is supported, what is weak, and what is not assessable from the provided material. - When the manuscript has a clear technical domain, use claim-dependent domain gates as supporting checks, but keep the output inside the same 3-reviewer `nature-reviewer` structure. - Do not claim the editor's final decision or certainty about fit to `Nature`. ## Accepted inputs The skill may receive: - full manuscript draft - abstract, summary paragraph, or cover-summary style text - introduction, results, discussion, or methods excerpts - figure legends, selected figures, or result notes - author notes in Chinese or English describing the claimed contribution - pre-submission positioning notes If the provided material is partial, perform a bounded review and mark the assessment boundary explicitly. ## Workflow 1. Identify the input scope and whether the job is a reviewer-style assessment rather than rebuttal drafting. 2. Build one immutable review packet containing only the supplied manuscript, verified source anchors, assessment boundary, and common journal criteria. Do not add analytical conclusions or suspected concerns to this packet. 3. Define the reviewer count and emphasis briefs before launching any reviewer. 4. Launch each reviewer in an isolated context. Pass only the immutable review packet, that reviewer's emphasis brief, the common report skeleton, and the same grounding rules. 5. Inside each isolated review, independently assess readiness and the source-grounded axes, then build that reviewer's own concern ledger using `references/technical-concern-taxonomy.md`. If relevant, load only the applicable section of `references/domain-specific-review-gates.md` inside that same isolated context. 6. Finalize and freeze every reviewer report. Do not show a completed or partial report to another reviewer, and do not redistribute concerns to control overlap. 7. After all reports are frozen, run the forensic consistency audit in a separate editorial pass using `references/forensic-consistency-audit.md`. Classify findings as confirmed internal error, aggregation ambiguity, provenance gap, suspected duplication, unresolved input needed, not assessable, or passed. Do not feed audit findings back into reviewer contexts or edit frozen reports after compar
Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.
Install
Pinned to the audited commit c5a4a9d
git clone https://github.com/yuan1z0825/nature-skills git -C nature-skills checkout c5a4a9d64275
The author documents no install command, so this uses the repository directly. Our verdict covers commit c5a4a9d64275 only.
Works in
The author has not said which agents this skill supports, and we have not tested it in any.
Audit history
Every audit, every source change
- ✓SAFE B 89 / 100
first audit · source
c5a4a9d64275
README badge
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[](https://modelsatlas.com/skills/yuan1z0825/nature-reviewer/)
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Questions about nature-reviewer
What does the nature-reviewer skill do?
Is nature-reviewer safe to install?
What can nature-reviewer access on my machine?
How current is this page?
Sources
- github.com/yuan1z0825/nature-skills/tree/main/skills/nature-revi — the audited source at commit c5a4a9d64275 and every finding aboveObserved · 31 Oct 2026
Spotted something wrong? Report a correction · Methodology
Keep going in your AI
Opens with this page as the source. We add nothing else to the prompt.
- What does the nature-reviewer skill do?
- Is nature-reviewer safe to install?
- What can nature-reviewer access on my machine?