一键导入
review
// Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.
// Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing.
Use for natural-science or engineering tasks, scientific software routing, simulation, dataset analysis, model fitting, package checks, HPC-through-shell work, validation, and evidence-backed scientific claims using DeepScientist's `artifact.science(...)` Science Evidence Graph. Includes a progressive-disclosure catalog of FermiLink skilled-scipkg package cards.
Prepare, audit, or revise Nature-ready Data Availability statements, data repository plans, dataset citations, and FAIR metadata checklists for manuscripts. Use when the user asks about Nature data availability, research data sharing, repository selection, accession numbers, restricted or sensitive data, source data, supplementary datasets, DataCite-style dataset references, FAIR metadata for academic publication, or Chinese-to-English data availability wording for Chinese-speaking authors preparing Nature-family submissions.
Submission-grade Nature/high-impact journal figure workflow for Python or R. Use whenever the user asks to create, revise, audit, or polish manuscript figures, multi-panel scientific plots, or journal-ready SVG/PDF/TIFF outputs, especially for Nature-family or other high-impact journals. Before plotting, define the figure's conclusion, evidence logic, export needs, and review risks. If the user has not chosen Python or R, ask "Python or R?" and stop. Use only the selected backend for figure generation, previewing, exporting, and QA. Supports matplotlib/seaborn and ggplot2/patchwork/ComplexHeatmap. Not for dashboards or Illustrator/Figma-first infographics.
Build a complete but efficient Nature-style Chinese PPTX presentation from a scientific paper, preprint, PDF, article text, abstract, figure legends, or reading notes. Use this skill whenever the user asks to make slides/PPT/PPTX for journal club, group meeting, paper sharing, thesis seminar, lab meeting, department report, or academic presentation from a research paper, not only medical papers. It identifies the paper type and argument, selects only the figures needed for the story, writes Chinese slide content and speaker notes, creates the actual .pptx deck, and performs lightweight verification with cross-platform Python tooling by default.
Polish, restructure, or translate academic prose into Nature-leaning English using the paper-architecture and writing-strategy principles from Scientific English Writing & Communication, with phrase-level support from Academic Phrasebank. Use whenever the user asks to polish a manuscript paragraph, abstract, introduction, results, discussion, conclusion, title, methods section, or Chinese academic draft for publication-quality English.
Use when a quest has enough evidence to draft or refine a paper, report, or research summary without inventing missing support.
| name | review |
| description | Use when a draft, paper, or paper-like report is substantial enough for an independent skeptical audit before finalization, rebuttal, or revision routing. |
| skill_role | companion |
Use this skill when the quest already has a substantial draft, paper, or paper-like report and now needs an independent, skeptical, evidence-grounded audit. The goal is to reduce revision ambiguity, not to sound harsh for its own sake.
This is not the same as ordinary write.
It is also not the same as rebuttal.
write turns accepted evidence into a narrative.review audits that narrative like a harsh but constructive expert reviewer.rebuttal responds to concrete external reviewer pressure that already exists.artifact.interact(kind='milestone', reply_mode='threaded', ...) update that says what the main risks are, what should be fixed next, and whether the next route is writing, experiment, or claim downgrade.bash_exec.plan.md as the top-level research map whose next anchor must become explicit after the reviewPLAN.md as the current review-node contract and CHECKLIST.md as the execution frontierpaper/review/experiment_todo.md as a review-facing frontier subset, not as a replacement for the quest-level mapreview is an auxiliary audit skill for paper-like deliverables.
It should convert “the draft feels almost done” into a durable, skeptical, technically grounded review workflow:
write, analysis-campaign, baseline, scout, or decisionDefault review stance: independent audit before celebration.
Do not treat “looks polished” as “is defensible”.
Do not accept structural green lights as paper-quality proof. Coverage, compile success, and file counts must still survive a skeptical claim/evidence/reviewer-risk audit.
Apply the publishability stop-loss rule during review: when novelty, evidence sufficiency, or reader value has collapsed beyond reasonable claim narrowing, the correct recommendation is decision with stop or branch, with any narrowed non-paper objective recorded as the next direction, not another cosmetic revision pass. If recommending stop because paper quality is too low, ask the user to confirm before ending the paper objective. Consider user publication, scope, cost, or non-paper preferences, and ask for a user decision when those preferences are unclear and would change the recommendation.
paper/draft.md, report draft, or paper-like manuscript already existsrebuttalstartup_contract.review_followup_policy is present, honor it:
audit_only
auto_execute_followups
user_gated_followups
startup_contract.manuscript_edit_mode = latex_required, treat the provided LaTeX tree or paper/latex/ as the writing surface when manuscript revision is needed.latex_required is requested, do not pretend the manuscript was edited; produce LaTeX-ready replacement text and an explicit blocker note instead.Before judging a manuscript as strong, final, or submission-ready, perform an evidence audit.
Build an experiment inventory from logs, matrices, result files, summaries, recent memory, and manuscript claims. For each main experiment and analysis experiment, record:
Do not trust ready counts when duplicate rows, stale outline refs, pending rows, or missing metrics are present. Recompute the real paper-facing count manually.
Classify every important result as one of:
Explicitly assess unsupported-claim or fabrication risk using these labels:
A paper cannot be called submission-ready unless compile/PDF, evidence provenance, manuscript coverage, citation sufficiency, language hygiene, and experiment-matrix consistency all pass.
Use, in roughly this order:
artifact.validate_manuscript_coverage(detail='full') for paper bundles, memos, or alleged near-final manuscriptsevaluation_summary blocks from recent main experiments and analysis slicesIf the draft/result state is still unclear, open intake-audit first before continuing the review workflow.
Before proposing extra experiments, read those structured evaluation_summary blocks first so you do not request work that the recorded evidence already resolved.
If the user provided draft files or manuscript bundles directly, first normalize them into durable quest-visible paths before planning experiments or section-level revisions.
The review pass should usually leave behind:
paper/review/review.mdpaper/review/revision_log.mdpaper/review/experiment_todo.mdpaper/paper_experiment_matrix.md when more evidence is still neededpaper/paper_experiment_matrix.json when more evidence is still neededUse the templates in references/ when needed:
review-report-template.mdrevision-log-template.mdexperiment-todo-template.mdAudit at least these dimensions:
Before writing the review itself, make the audit explicit.
Identify:
draft_checkpoint, a reviewable manuscript, or truly submission-readyC1, C2, C3artifact.validate_academic_outline(detail='full') and artifact.validate_manuscript_language(detail='full') pass when a selected outline or draft existsBefore calling a substantial manuscript strong, run a current literature/style benchmark unless the review scope is explicitly local-only.
Search for nearby high-quality papers using the best available current source:
Build a compact comparison set, usually 3-8 papers:
For each comparator, record title, venue/source, why it is relevant, and what it teaches about:
Do not request new experiments just to answer a literature-positioning question. First decide whether the fix is writing, positioning, claim narrowing, or genuinely missing evidence.
Write paper/review/review.md using references/review-report-template.md.
The review should be:
Never write "no weaknesses", "no key issues", or equivalent closure language unless the review has first listed the main rejection routes and shown why each is fixed, downgraded, or out of scope.
At minimum, the review report should cover:
If helpful, include an internal conservative overall judgment or score, but do not pretend numerical precision when evidence is still unstable.
Write paper/review/revision_log.md using references/revision-log-template.md.
For each serious issue, record:
finalizestartup_contract.manuscript_edit_mode = latex_requiredOnly if more evidence is truly needed, write paper/review/experiment_todo.md using references/experiment-todo-template.md.
Before creating TODOs, separate three blocker types:
Do not turn manuscript/submission blockers into fake experiments or mark a draft_checkpoint_ready package final.
Do not accept a draft as reviewable while it still reads like a run log, control memo, or user-request transcript.
When the paper still lacks experimental support, also create or revise:
paper/paper_experiment_matrix.mdpaper/paper_experiment_matrix.jsonTreat the matrix as the paper-facing master plan and paper/review/experiment_todo.md as only the current execution frontier or review-facing subset.
Each TODO item should include:
exp_id in the paper experiment matrixDo not write a vague “run more ablations” list.
Each TODO item should be concrete enough to turn into analysis-campaign slices or a baseline recovery task.
The matrix should be broader than the TODO list and should classify the full paper-facing experiment space, not just analysis work.
When building or revising that matrix, explicitly consider:
Do not assume the paper only needs “analysis experiments”. Do not assume case studies belong in the required set. If efficiency or cost could become a reviewer-facing strength or concern, put that into the matrix explicitly. If local serving, batch size, ports, cache reuse, or command shape are only reproducibility details, route them to appendix/protocol rows rather than main-text claims.
For the matrix, each row should usually record:
exp_idtierexperiment_typestatusfeasibility_nowclaim_idshighlight_idsresearch_questionhypothesiscomparatorsmetricsminimal_success_criterionpaper_placementpromotion_rulenext_actionThe matrix should also keep a short highlight hypotheses block.
Do not rely on prose intuition for the method's best selling point; if a likely highlight matters, it should have a corresponding validation row in the matrix.
Before treating the experiments section as stable, require that every currently feasible matrix row that is not merely optional or dropped is either:
Before treating the manuscript prose as stable, scan titles, abstract, captions, method setup, conclusion, and section openings for process language. Rewrite or block if they mention user requests, agent decisions, worktrees, branches, artifact ids, "paper restart", "paper should", "remaining work", tool recommendations, or shorthand such as 64 + 64.
When extra evidence is truly needed, use the shared supplementary-experiment protocol:
artifact.create_analysis_campaign(...)artifact.record_analysis_slice(...)Do not invent a separate review-only experiment workflow.
After the review artifacts are durable:
writescoutbaselineanalysis-campaigndecisionDo not stop immediately after writing the review if the next route is already clear.
When startup_contract.review_followup_policy = auto_execute_followups:
analysis-campaign
baseline
write
paper/review/revision_log.mdpaper/review/experiment_todo.mdWhen startup_contract.review_followup_policy = user_gated_followups:
When startup_contract.review_followup_policy = audit_only:
If manuscript revision is required, make the delta explicit:
If startup_contract.manuscript_edit_mode = copy_ready_text:
paper/review/revision_log.md or a nearby revision notewriteIf startup_contract.manuscript_edit_mode = latex_required:
Open additional skills only when the review workflow requires them:
intake-audit
scout
baseline
analysis-campaign
write
figure-polish
decision
Use these tools deliberately:
artifact.record(payload={'kind': 'decision', ...})
artifact.create_analysis_campaign(...)
artifact.record_analysis_slice(...)
artifact.submit_paper_outline(mode='revise', ...)
artifact.submit_paper_bundle(...)
artifact.interact(...)
Stage-start requirement:
memory.list_recent(scope='quest', limit=5)memory.search(...) for:
Stage-end requirement:
memory.write(...)Useful tags include:
stage:reviewtype:paper-reviewtype:revision-plantype:experiment-gaptype:claim-downgradereview is successful when:
write, analysis-campaign, baseline, scout, or finalize without ambiguityThe goal is not to sound severe. The goal is to make the next revision step technically clear and evidence-bound.
A good review pass leaves fewer plausible next moves, not more.