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paper-compile

Compile LaTeX paper to PDF, fix errors, and verify output. Use when user says \"编译论文\", \"compile paper\", \"build PDF\", \"生成PDF\", or wants to compile LaTeX into a submission-ready PDF.

71

Quality

86%

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SKILL.md
Quality
Evals
Security

Quality

Content

73%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.

The body is a thorough, actionable compilation workflow with strong validation and feedback loops. Its main weakness is redundancy in the page-count rules and a monolithic structure that could split reference material into bundle files.

Suggestions

Consolidate the ML-vs-IEEE page-count rule into one location (e.g., the Venue Requirements table) and reference it from Constants and Key Rules to remove the triple restatement.

Move the detailed error-diagnosis table (Step 3) and the venue requirements table into a references/ file (e.g., ERROR_FIXES.md, VENUES.md), keeping SKILL.md an overview with one-level-deep links.

Replace the Step 4 pseudocode loop with concrete shell commands or a real retry script so the guidance is fully executable.

DimensionReasoningScore

Conciseness

Mostly efficient with executable commands and no conceptual padding, but the ML-vs-IEEE page-count rule is restated three times (Constants, Step 6, Key Rules), which is noticeable redundancy that could be tightened.

3 / 5

Actionability

Provides concrete executable commands throughout (latexmk, pdfinfo, pdftotext|python3, grep), but Step 4's fix loop is pseudocode with undefined compile()/auto_fix() helpers, leaving minor gaps.

4 / 5

Workflow Clarity

An explicit 8-step sequence includes validation checkpoints (Step 5 automated checks, page-count verification), a recovery feedback loop (Step 4 retry plus RESCUE_ON_REPEAT_FAILURE), and checklists (Steps 5 and 7).

5 / 5

Progressive Disclosure

Well-organized with clear section headers and no nested references, but the ~250-line body with large venue and error-fix tables is monolithic inline content rather than an overview pointing to one-level-deep reference files.

4 / 5

Total

16

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20

Passed

Description

100%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.

The description is exemplary: it states concrete capabilities and gives bilingual trigger phrases covering both what and when with minimal conflict risk. No improvements needed.

DimensionReasoningScore

Specificity

Lists three concrete actions on a named domain — "Compile LaTeX paper to PDF", "fix errors", "and verify output" — which is comprehensive coverage comparable to the anchor-5 example.

5 / 5

Completeness

Explicitly states both what (compile to PDF, fix errors, verify) and when ("Use when user says... or wants to compile LaTeX into a submission-ready PDF") with concrete trigger phrases.

5 / 5

Trigger Term Quality

Five natural trigger phrases spanning English and Chinese synonyms ("编译论文", "compile paper", "build PDF", "生成PDF") plus the PDF file format, matching the comprehensive-synonyms anchor.

5 / 5

Distinctiveness Conflict Risk

Targets a clear LaTeX-to-PDF niche with compilation-specific triggers, minimizing overlap with adjacent paper-writing skills.

5 / 5

Total

20

/

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.

Validation16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository
wanshuiyin/Auto-claude-code-research-in-sleep
Reviewed

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