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auto-paper-improvement-loop

Autonomously improve a generated paper via GPT-5.6-Sol xhigh review → 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.

67

Quality

82%

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

77%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 strongly actionable, well-sequenced workflow with thorough validation and feedback loops. Its main weakness is verbosity from duplicated whitelist material and anecdotal padding, and a monolithic structure that could offload the whitelist spec and prompt templates into reference files.

Suggestions

Consolidate the edit-whitelist behavior into one section (e.g. the 'Optional: Edit Whitelist' block) and have Steps 3/6 and Key Rules reference it rather than re-describing the gate, logging schema, and end-of-round surfacing each time.

Move the whitelist YAML schema, resolution rules, and forbidden-operation detector table into a references/ file (e.g. EDIT_WHITELIST.md) and the two review prompt templates into references/review-prompts.md, leaving SKILL.md as an overview that links one level deep.

Trim or relocate the recurring 'Empirical motivation' anecdotes into a single 'Design notes' section so the workflow steps stay lean and token-efficient.

DimensionReasoningScore

Conciseness

The core is actionable rather than teaching basics, but the ~575-line body repeats the edit-whitelist behavior across four locations (Constants, the long 'Optional: Edit Whitelist' section, the Step 3/6 gates, and Key Rules) and carries several narrative 'Empirical motivation' anecdotes that are padding; it is mostly efficient yet could be materially tightened, fitting the 3 anchor better than the 4.

3 / 5

Actionability

It provides copy-paste-ready bash, a full Python normalization script, complete spawn_agent prompt templates, and concrete fix-pattern tables covering the common cases; the few bracketed placeholders ([VENUE], [list figure files]) are clearly intended for substitution in a parameterized skill rather than gaps.

5 / 5

Workflow Clarity

Steps 0–9 are clearly sequenced with explicit validation checkpoints (recompile verification, the Step 4.5 Restatement Regression Test feedback loop, and the Step 8 format check with hard stop criteria and auto-fix patterns), satisfying the top anchor including feedback loops for the batch/destructive edit operations.

5 / 5

Progressive Disclosure

Section structure is clear and the one external reference (../shared-references/review-tracing.md) is signaled one level deep, but no bundle files exist and the large whitelist schema/rules plus the repeated review-prompt templates are inlined in a monolithic 575-line SKILL.md rather than split into reference files, matching the 'some structure but content that should be separate is inline' anchor.

3 / 5

Total

16

/

20

Passed

Description

87%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 high-quality description: third-person, concrete, with explicit trigger guidance in both English and Chinese and clear differentiation from a sibling skill. Minor gains possible by adding a few more English synonyms.

DimensionReasoningScore

Specificity

Names the domain and lists several concrete actions — 'review → implement fixes → recompile, for 2 rounds' via a named GPT-5.6-Sol xhigh chain — but stops short of exhaustively enumerating every sub-action, fitting the 'several specific actions; minor gaps' anchor rather than the fully comprehensive 5.

4 / 5

Completeness

It explicitly answers both 'what' (autonomously improve a generated paper via review→fix→recompile for 2 rounds) and 'when' with concrete trigger phrases in an explicit 'Use when...' clause, matching the top anchor.

5 / 5

Trigger Term Quality

Strong natural trigger coverage across two languages ('改论文', 'improve paper', '论文润色循环', 'auto improve', 'iteratively polish a generated paper') with good synonym variety, but a few common English phrasings like 'polish paper' or 'refine paper' are absent, keeping it just below comprehensive.

4 / 5

Distinctiveness Conflict Risk

It carves a clear niche (iterative paper-writing polish via external LLM review) and explicitly contrasts itself with /auto-review-loop (research iteration), giving it distinct triggers and minimal conflict risk.

5 / 5

Total

18

/

20

Passed

Validation

81%

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

Validation13 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (575 lines); consider splitting into references/ and linking

Warning

allowed_tools_field

'allowed-tools' contains unusual tool name(s)

Warning

frontmatter_unknown_keys

Unknown frontmatter key(s) found; consider removing or moving to metadata

Warning

Total

13

/

16

Passed

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

Table of Contents

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