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auto-paper-improvement-loop

Autonomously improve a generated paper via GPT-6-Astra 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.

Quality

81%

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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 highly actionable, well-sequenced workflow with exceptional validation and recovery machinery — format-check stop criteria, regression tests, and bias-guard rules are best-in-class. Its weaknesses are duplication (the reviewer prompt repeated nearly verbatim), the monolithic 634-line body that inlines spec content better suited to reference files, and placeholder-riddled templates that stop short of copy-paste readiness.

Suggestions

Replace the near-verbatim duplicate reviewer prompts in Steps 2 and 5 with a single 'Reviewer prompt template' section (the Round 2 variant's only addition is the fresh-context preamble), saving ~50 lines.

Move the ~105-line edit-whitelist specification (schema, resolution rules, glob semantics, detectors, behaviors) into a references/edit-whitelist.md file and keep only a short summary plus the link in SKILL.md.

Complete the Step 4.5 Python snippet so it actually compares normalized theorem blocks (e.g., extract \begin{theorem}...\end{theorem} spans per file and diff them) instead of ending at a comment, and fix the duplicated 'Step 9' section numbering.

DimensionReasoningScore

Conciseness

The bulk is dense operational specifics, but the ~50-line reviewer prompt is duplicated nearly verbatim in Steps 2 and 5, the edit-whitelist gate is restated in Steps 3, 6, and Key Rules, and long empirical anecdotes pad several sections. Anti-drift: above anchor 2 because most sections carry unique actionable content rather than padded fluff.

3 / 5

Actionability

Concrete bash commands (latexmk, pdfinfo, grep overfull classification), full reviewer prompt templates, regex detector tables, a YAML whitelist schema, and a Python normalization snippet. Minor gaps — [VENUE]/[list figure files] placeholders, the Python block ending in a comment instead of a complete comparison, and the pseudo-YAML spawn_agent invocation — keep it below fully copy-paste-ready anchor 5.

4 / 5

Workflow Clarity

Steps 0–9 are explicitly sequenced with validation checkpoints throughout: verify 0 undefined references, a restatement regression test that blocks completion, location-aware format-check stop criteria, crash-recovery state persistence, and optional human checkpoints with response-parsing rules. Feedback loops (fix → revalidate → block) are exactly what the rubric's top anchor rewards; the duplicated 'Step 9' numbering is a cosmetic flaw only.

5 / 5

Progressive Disclosure

No bundle files exist (references/, scripts/, assets/ are all absent; the only pointers go to ../shared-references/ outside this bundle). The 634-line body inlines large content blocks that clearly belong in separate files — the ~105-line edit-whitelist spec and the reviewer prompt template — despite otherwise good section structure. Anti-drift: above anchor 2 because structure is solid and clearly signaled, below anchor 4 because significant content should be split out.

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 description: concrete action pipeline, explicit round count, and bilingual trigger phrases covering both 'what' and 'when' clearly. Only minor weaknesses in coverage breadth and the slightly generic 'auto improve' trigger.

DimensionReasoningScore

Specificity

Names the domain (generated paper) and several concrete actions — 'GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds' — including the review model and round count. Minor gaps in coverage (e.g., the LaTeX/PDF artifacts and improvement log are not mentioned) keep it below the comprehensive anchor 5.

4 / 5

Completeness

Explicitly answers both 'what' ("Autonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds") and 'when' ("Use when user says...") with concrete trigger phrases, exactly matching the anchor-5 pattern.

5 / 5

Trigger Term Quality

Trigger phrases span both languages and natural synonyms: "改论文", "improve paper", "论文润色循环", "auto improve", and "iteratively polish a generated paper" — comprehensive coverage of the natural terms a target user would say.

5 / 5

Distinctiveness Conflict Risk

'Generated paper', the named reviewer model, and the round-based loop carve a mostly distinct niche, but the generic trigger 'auto improve' creates minor overlap risk with sibling paper/review skills.

4 / 5

Total

18

/

20

Passed

Validation

75%

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

Validation — 12 / 16 Passed

Validation for skill structure

CriteriaDescriptionResult

skill_md_line_count

SKILL.md is long (634 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

relative_links

Relative link issues: 1 suspicious

Warning

Total

12

/

16

Passed

Repository

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

Path

skills/skills-codex/auto-paper-improvement-loop/SKILL.md

Reviewed

Table of Contents

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