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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.

80

Quality

100%

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SecuritybySnyk

Passed

No findings from the security scan

SKILL.md
Quality
Evals
Security

Quality

Content

100%

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

A well-engineered instruction-only skill: dense but not verbose, with concrete formats and conventions, sequenced workflows carrying explicit validation feedback loops, and clean one-level-deep progressive disclosure backed by real reference files.

DimensionReasoningScore

Conciseness

The body is lean and directive — a routing table, hard rules, and short workflows — with no generic definitional fluff (e.g. no "what a PDF is" padding); every section advances a concrete policy, matching the lean top anchor.

3 / 3

Actionability

Concrete executable guidance throughout: grep patterns ("\section", "\begin{abstract}"), placeholder formats ("[XX.X]", "[CITE: ...]"), the flagging convention "⚠ interpreted as ...", and the claim-evidence map format — instruction-only yet highly actionable, so the code-absence note does not penalize it.

3 / 3

Workflow Clarity

Multi-step processes are sequenced (Step 1 route → Step 2 facts → Workflows A/B/C) with explicit feedback loops: reverse-outline and "fix anything that doesn't map", the claim-evidence status map, and the hostile-review pass/needs-revision/needs-experiment scoring with a prioritized fix list.

3 / 3

Progressive Disclosure

The SKILL.md is an overview that routes to one-level-deep reference files (abstract.md, introduction.md, etc.), all verified to exist, each summarized in the References section, with an examples/ tree clearly cited from the section guides — well-signaled and easy to navigate.

3 / 3

Total

12

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12

Passed

Description

100%

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

A strong description: concrete actions, natural and bilingual trigger terms, explicit use/trigger clauses, and a well-scoped niche. It does not over-claim and uses third-person imperative voice throughout.

DimensionReasoningScore

Specificity

"Write, rewrite, and polish academic papers", "drafts or revises Abstract, Introduction...", "fixes paper figures/tables/LaTeX formatting", and "compiles/converts the paper to PDF" list multiple concrete, distinct actions rather than vague language, matching the top anchor.

3 / 3

Completeness

It states what the skill does (write/rewrite/polish/compile) and gives explicit when-guidance via both a "Use when the user..." clause and a "Trigger on mentions of..." clause, clearly answering both what AND when.

3 / 3

Trigger Term Quality

Natural user phrasing is well covered, including bilingual triggers like "does this flow / 这段通顺吗 / polish this paragraph" and "编译PDF, 转成PDF", plus venue names (CVPR/ICCV/NeurIPS) and ".tex files", matching good coverage of terms users would actually say.

3 / 3

Distinctiveness Conflict Risk

The academic ML/CV/NLP paper-writing niche with venue names, .tex handling, and bilingual triggers is a clear, distinct scope unlikely to fire for unrelated skills.

3 / 3

Total

12

/

12

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
XiaomiMiMo/MiMo-Code
Reviewed

Table of Contents

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