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auto-paper-improvement-loop

Autonomously improve a generated paper via Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds. Use when user says "改论文", "improve paper", "论文润色循环", "auto improve", or wants to iteratively polish a generated paper.

66

Quality

80%

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SKILL.md
Quality
Evals
Security

Quality

Content

70%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.

A thorough, highly actionable paper-improvement loop with exemplary validation checkpoints and feedback loops. Its weaknesses are token efficiency (duplicated prompts and inline reference-grade material) and progressive disclosure (a monolithic file that should split out the edit-whitelist documentation).

Suggestions

De-duplicate the Round 1 and Round 2 review prompts (Steps 2 and 5) into a single shared template, parameterizing only the 'fresh zero-context review' instruction — this removes ~30 lines of near-verbatim repetition.

Move the ~100-line Edit Whitelist section (schema, resolution rules, glob semantics, forbidden-operation detectors) into a references/EDIT_WHITELIST.md and keep a brief summary plus link in SKILL.md to improve progressive disclosure.

Fix the duplicate 'Step 9' headers (both 'Document Results' and 'Summary' are numbered Step 9) and condense the repeated 'Empirical motivation' anecdotes into one short rationale per feature.

DimensionReasoningScore

Conciseness

The body is operationally dense and mostly useful, but the ~30-line review prompt is duplicated verbatim between Step 2 and Step 5, the ~100-line Edit Whitelist section is inline rather than referenced, and repeated 'Empirical motivation' anecdotes pad the file — fitting 'mostly efficient but includes some unnecessary explanation or could be tightened'.

3 / 5

Actionability

Concrete executable bash (latexmk, pdfinfo, grep), a runnable Python normalization heredoc, YAML schemas, and a regex-detector table give mostly copy-paste-ready guidance; the residual [VENUE] / [list all section .tex files] placeholders in the MCP prompts are minor gaps that keep it just below fully-executable anchor 5.

4 / 5

Workflow Clarity

Steps 0–9 are explicitly sequenced with hard validation checkpoints — recompile verification ('0 undefined references, 0 undefined citations'), the Step 4.5 Restatement Regression Test, and the Step 8 location-aware format check with stop criteria and auto-fix patterns — plus feedback loops (validate → fix → re-validate), matching the anchor-5 example.

5 / 5

Progressive Disclosure

Section structure is clear (## Context, ## Constants, ## Workflow), but the 581-line file is monolithic with no bundle files: the ~100-line Edit Whitelist docs and the duplicated review-prompt templates are content that clearly belongs in separate reference files, and the only external pointer (../shared-references/review-tracing.md) is not part of the bundle — fitting the 'content that should be separate is inline' anchor 3.

3 / 5

Total

15

/

20

Passed

Description

91%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.

A strong, third-person description that clearly states both capability and trigger conditions with bilingual synonyms. It is specific and well-scoped, with only minor overlap risk against the broader paper-skill family.

DimensionReasoningScore

Specificity

Names the domain and several concrete actions — 'Claude review through claude-review MCP → implement fixes → recompile, for 2 rounds' — but coverage is a tight loop rather than a comprehensive enumeration of capabilities, fitting the 'several specific actions; minor gaps' anchor rather than the exhaustive anchor 5.

4 / 5

Completeness

Explicitly answers both 'what' (autonomously improve a generated paper via review→fix→recompile for 2 rounds) and 'when' ('Use when user says ...') with concrete trigger phrases, matching the anchor-5 example.

5 / 5

Trigger Term Quality

Comprehensive natural trigger coverage with synonyms across two languages — '改论文', 'improve paper', '论文润色循环', 'auto improve', plus the descriptive phrase 'iteratively polish a generated paper' — matching the 'comprehensive coverage including synonyms' anchor.

5 / 5

Distinctiveness Conflict Risk

The 'iteratively polish a generated paper via external Claude review' niche is mostly distinct and the body even contrasts it with /auto-review-loop, but it sits in a dense paper-skill ecosystem so minor overlap risk with sibling paper skills keeps it just below the minimal-conflict anchor 5.

4 / 5

Total

18

/

20

Passed

Validation

93%

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

Validation15 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (582 lines); consider splitting into references/ and linking

Warning

Total

15

/

16

Passed

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

Table of Contents

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