AC review-paper-writing
Literature review paper writing assistant — guides you through the full lifecycle: (1) Systematic search using PICO/Boolean/MeSH frameworks across arXiv, Semantic Scholar, OpenAlex, CrossRef & PubMed; (2) DOI & arXiv ID validation with cross-source conflict detection; (3) Multi-format citation output (APA 7th, MLA 9th, IEEE, BibTeX, RIS, Markdown); (4) Auto-generated structured literature review with timeline, keyword extraction & source-quality tagging. Use when drafting a review article, survey paper, or thesis background chapter — or when given a topic to compile references for.
As a process C 59/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, consistency
How to improve
- 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 · 0
✓ No critical or high findings
Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 59/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 7 mutating operations with no state check
- 40Consistency. Frontmatter name (review-paper-writing) differs from the folder (literature-review-writing-assistant)
- 50Failures and branches. 0 branches, has a failure section
- 60Result and completion. Output format stated, no completion criterion
- 100Tools and files. No external tools needed
- 100Steps. 9 steps
- 100Execution cost. Instruction body is 1557 tokens
- low 10 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
- +5Description has no quoted example phrases that should trigger the skill
- +4Description does not say when NOT to use the skill (false activations)
- -42 reference files, but SKILL.md never points to them: the model will not open them
- +1No license
- +2Single-language instructions
- +3Description length 588: enough signal without eating the budget
- +4Structure: 22 headings
- +3Step-by-step instructions: 9 items
- +3Output format is stated explicitly
- +4Has examples (15 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 86.