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research-paper-writing

Write, rewrite, and polish academic papers (ML/CV/NLP style). Use when the user drafts or revises Abstract, Introduction, Related Work, Method, Experiments, or Conclusion; asks "does this flow / 这段通顺吗 / polish this paragraph"; turns bullet points or a Chinese draft into publication-quality English; runs a pre-submission self-review or reviewer-style critique; fixes paper figures/tables/LaTeX formatting; or compiles/converts the paper to PDF (LaTeX build, 编译PDF, 转成PDF). Trigger on mentions of paper, draft, camera-ready, rebuttal-facing revision, CVPR/ICCV/NeurIPS/ICLR/ACL-style venues, or .tex files being edited for a paper.

77

Quality

96%

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SKILL.md
Quality
Evals
Security

Quality

Content

96%Weight 40%Scale 1-5

Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.

An exceptionally well-crafted instruction-only skill body: lean, assumption-respecting prose with concrete formats, limits, and commands, clearly sequenced workflows with real validation checkpoints, and a routing-table-driven progressive disclosure design. The only structural nit is that detailed examples live two levels deep under the section guides, slightly beyond the ideal one-level reference depth.

DimensionReasoningScore

Conciseness

The body contains no concept explanations Claude already knows (no 'what a paper is', no library tutorials) — every line is a directive, a rule, or routing, e.g. "Match the user's situation and load ONLY the needed reference (do not preload all)" and "Never fill gaps by inventing." This is the score-5 anchor (lean, assumes competence, every token earns its place); score 4 would require identifiable over-explanation to trim, and there is none.

5 / 5

Actionability

Concrete, executable guidance throughout: exact placeholder formats ("[XX.X]", "[CITE: sparse-view NeRF methods]"-style), concrete commands ("locate sections with grep (\section, \begin{abstract})"), exact limits ("at most 3 focused questions in one message", "3–7 bullet mini-outline"), and a concrete output format ("Claim: ... | Evidence: ... | Status: supported / needs evidence / weakened"). Per the code-vs-instruction scoring note, an instruction-only skill with guidance this specific is fully actionable — the score-5 anchor equivalent; score 4 would require missing key details, and the routing table plus workflows cover the common cases.

5 / 5

Workflow Clarity

Three clearly sequenced workflows by request size (A quick polish, B section draft, C pre-submission review) with explicit validation checkpoints: "Reverse-outline the result: thesis → topic sentences → evidence; fix anything that doesn't map", the claim-evidence map with status tracking, "If the paragraph's real problem is structural... say so instead of cosmetically polishing", and "Pick engine, build, verify output". This matches the score-5 anchor (clear sequence, explicit validation, feedback loops, checklist for the review workflow); it is not score 4 because validation is explicit rather than implied at each stage.

5 / 5

Progressive Disclosure

Good structure: SKILL.md is a pure overview with a situation→reference routing table, an explicit "load ONLY the needed reference" instruction, and a References section with one-line descriptions; all 25 referenced paths verify to real files. However, detail sits two levels deep — SKILL.md → references/<section>.md → references/examples/<topic>/*.md (e.g. method.md cites 10 example files) — which is beyond the one-level-deep ideal of the score-5 anchor. Fits score 4 (good structure, most content appropriately placed, minor organization gap); not score 3 because navigation is easy and nothing is buried or inlined that belongs in a separate file.

4 / 5

Total

19

/

20

Passed

Description

96%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An exemplary description: comprehensive concrete capabilities, a fully explicit 'Use when' clause, and rich bilingual natural trigger terms including venue names and file extensions. The only weakness is minor overlap risk from the generic trigger word 'draft' and the LaTeX/PDF compile coverage bordering on general LaTeX skills.

DimensionReasoningScore

Specificity

Quotes: "Write, rewrite, and polish academic papers", "drafts or revises Abstract, Introduction, Related Work, Method, Experiments, or Conclusion", "runs a pre-submission self-review or reviewer-style critique", "fixes paper figures/tables/LaTeX formatting", "compiles/converts the paper to PDF" — multiple specific concrete actions covering drafting, polishing, translation, review, formatting, and compilation. This matches the score-5 anchor (comprehensive coverage); it is not score 4 because no meaningful capability of the domain is left out.

5 / 5

Completeness

Quotes: "Write, rewrite, and polish academic papers (ML/CV/NLP style)" (clear what) and "Use when the user drafts or revises... asks... turns... runs... fixes... or compiles..." (explicit when with concrete trigger phrases). This is the score-5 anchor verbatim in structure; it is not score 4 because the 'when' is already explicit and specific, not merely present.

5 / 5

Trigger Term Quality

Quotes: "does this flow / 这段通顺吗 / polish this paragraph", "publication-quality English", "LaTeX build, 编译PDF, 转成PDF", "camera-ready", "CVPR/ICCV/NeurIPS/ICLR/ACL-style venues", ".tex files" — natural phrases users would actually say, with synonyms in two languages, venue names, and file extensions. Matches the score-5 anchor; score 4 would require some common natural term to be missing, and none is.

5 / 5

Distinctiveness Conflict Risk

Quotes: "academic papers (ML/CV/NLP style)", ".tex files being edited for a paper", "CVPR/ICCV/NeurIPS/ICLR/ACL-style venues" establish a clear niche, but the bare trigger term "draft" in "Trigger on mentions of paper, draft" and the LaTeX/PDF-compilation coverage overlap with generic writing and LaTeX/PDF skills. Fits the score-4 anchor (mostly distinct; minor overlap risk with closely related skills); not score 5 because that overlap risk is non-minimal, and not score 3 because the paper-specific scoping dominates.

4 / 5

Total

19

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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