CC 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.
Autonomously improve a generated paper via GPT-6-Astra xhigh review → implement fixes → recompile, for 2 rounds.
As a process C 67/100 · Has gaps — References files that are not bundled: ../shared-references/reviewer-backends.md
The same skill appears in 1 more place: Auto-claude-code-research-in-sleep
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- The text references files that are not there: add them or drop the references.
- Your own cases (evals/evals.json, 4–6 real requests with expected answers): the full check would then run those instead of a model-drafted suite.
- A spec.yaml with trigger phrases and assertions — a behaviour contract for CI; `skilltest init` writes a template.
Guard findings · 1
✓ No critical or high findings
Medium and low: 1
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medium Broad scope
meta-broad-allowed-toolsSKILL.md:1Broad tool permissions pre-approved: Bash(*)allowed-tools: Bash(*) Read Write Edit Grep Glob mcp__antigravity__antigravity mcp__antigravity__antigravity-reply mcp__grok__grok mcp__grok__grok-reply mcp__claude-review__review mcp__claude-review__
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 8912 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: ../shared-references/reviewer-backends.md - note
edit-residuethe text marks something as outdated (lines 27, 175, 412): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 67/100
- 0Tools and files. 1 referenced file(s) missing: ../shared-references/reviewer-backends.md
- 40Execution cost. Instruction body is 8912 tokens: crowds the task out of the window
- 50When it triggers. No condition that starts the skill
- 70Inputs and preconditions. Inputs and preconditions are listed
- 85Steps. 74 steps, 1 vague phrases
- 100Result and completion. Output format and completion criterion are stated
- 100Failures and branches. 9 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 11 top-level sections: this looks like several domains in one skill
Everything here is measured from the skill text rather than judged by a model, so the numbers are checkable. A parameter weighs more when it is a more common reason for the process to stall.
Quality signals
- +4Description does not say when NOT to use the skill (false activations)
- +1No license
- +2Single-language instructions
- +5Description quotes 3 example trigger phrases
- +3Description length 231: enough signal without eating the budget
- +4Structure: 37 headings
- +3Step-by-step instructions: 74 items
- +3Output format is stated explicitly
- +4Has examples (16 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 76.