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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

79%

Does it follow best practices?

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SecuritybySnyk

Low

Low-risk findings worth noting

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tessl review fix ./skills/auto-paper-improvement-loop/SKILL.md
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 concrete orchestration skill with exceptional validation and feedback-loop coverage (regression tests, blocking criteria, state recovery, audit logging). Its weaknesses are the inlined-then-duplicated review prompt and whitelist spec, which inflate token cost and would benefit from reference-file extraction, plus one incomplete inline script.

Suggestions

Replace the duplicated Round 2 review prompt in Step 5 with a reference to the Step 2 prompt template plus only the Round 2 deltas (fresh-thread instruction, zero-context line, 'Ignore any prior review rounds' paragraph), saving ~50 lines.

Move the edit-whitelist spec (schema, resolution rules, glob semantics, detector table, logging schema, ~100 lines) into a reference file such as references/edit-whitelist.md and keep a short summary, the detector table, and a link in SKILL.md.

Complete the Step 4.5 inline Python beyond the normalize() helper — provide the theorem-block extraction and comparison logic (or explicitly route to /proof-checker --restatement-check as the executable path) so the regression gate is runnable as written.

DimensionReasoningScore

Conciseness

All content is task-specific with no basic-concept padding, but the ~50-line reviewer prompt is duplicated verbatim in Steps 2 and 5, and multiple 'Empirical motivation'/'Rationale' blocks could be tightened or merged. Fits anchor 3 (mostly efficient, could be tightened) rather than 2 because the verbosity is domain-relevant, not generic filler, and not 4 because the prompt duplication is a large avoidable block.

3 / 5

Actionability

Nearly every step is copy-paste executable (latexmk commands, pdfinfo/grep format checks, YAML whitelist schema, regex detectors, MCP call configs), but the Step 4.5 inline Python defines only normalize() and leaves the actual theorem-block comparison as a comment, and [VENUE]/$PAPER_DIR placeholders require substitution. Anchor 4 (mostly executable, minor gaps) rather than 5 because the regression-test script is incomplete.

4 / 5

Workflow Clarity

Steps 0-9 are explicitly sequenced with validation checkpoints throughout: 'Verify: 0 undefined references, 0 undefined citations', the Step 4.5 restatement regression test, location-aware format-check stop criteria, whitelist rejection logging, JSON state recovery for compaction, and HUMAN_CHECKPOINT feedback branches. Matches the anchor-5 example's validate->fix->retry rigor; the duplicate 'Step 9' heading numbering is cosmetic.

5 / 5

Progressive Disclosure

The bundle contains zero reference/script files; the entire 710-line implementation lives in SKILL.md, including an ~100-line edit-whitelist spec and the full review prompt template that clearly belong in separate files. External references (shared-references/*, tools/extract_paper_style.py, skills/kill-argument/) are clearly signaled but outside the bundle; anchor 3 (content that should be separate is inline) rather than 2 because sections are well-organized and references are not buried.

3 / 5

Total

15

/

20

Passed

Description

83%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 what-and-when structure with natural bilingual trigger phrases in third-person voice. Minor improvements possible by adding English trigger synonyms and distinguishing itself from the research-oriented /auto-review-loop sibling.

DimensionReasoningScore

Specificity

'GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds' lists several concrete actions with concrete specifics (model, reasoning effort, round count). Anchor 4 rather than 5 because peripheral actions (logging, format checking, state recovery) are unmentioned, leaving minor coverage gaps; not 3 since multiple specific actions are named.

4 / 5

Completeness

Explicitly answers both: what ('improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds') and when ('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') with concrete trigger phrases. This matches the anchor-5 example structure exactly; neither what nor when is vague.

5 / 5

Trigger Term Quality

'改论文', 'improve paper', '论文润色循环', 'auto improve', 'iteratively polish' are natural phrases with bilingual synonym coverage a user would plausibly say. Anchor 4 rather than 5 because common English variants like 'polish paper', 'revise paper', or 'paper polishing' are missing; not 3 because the included terms are natural and varied, not just generic keywords.

4 / 5

Distinctiveness Conflict Risk

'Generated paper', 'GPT-6-Astra xhigh review', and 'iteratively polish' carve a clear niche distinct from generic editing skills. Anchor 4 rather than 5 because the sibling /auto-review-loop skill shares the 'auto improve' trigger space and the description does not itself distinguish research-iteration from paper-polishing; not 3 because the paper-focused triggers keep overlap minor.

4 / 5

Total

17

/

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 (711 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: 2 suspicious

Warning

Total

12

/

16

Passed

Repository

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

Path

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

Reviewed

Table of Contents

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