BF meta-analysis-journal-selector
SCI journal selection assistant for meta-analysis and systematic review papers. Use this skill when the user has completed or is completing a meta-analysis and needs journal recommendations, wants a tiered submission strategy (Reach/Target/Safety), asks which SCI journals accept meta-analyses, or needs to compare journals by impact factor, acceptance rate, review speed, and scope match. Trigger phrases include: "which journal for my meta-analysis", "help me select a journal", "meta-analysis journal recommendation", "where to submit meta-analysis", "SCI journal selection for systematic review", "find journals that accept meta-analyses", "我的meta分析投什么期刊", "帮我选刊", "meta分析SCI投稿", "meta选刊建议".
SCI journal selection assistant for meta-analysis and systematic review papers.
As a process F 51/100 · Will not run — References files that are not bundled: references/jcr_verified_data.csv, references/journal_database.md
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 · 0
✓ No critical or high findings
Files scanned: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 5926 tokens (recommended < 5000); move details to references/ - warning
missing-refreference to a missing file: references/jcr_verified_data.csv - warning
missing-refreference to a missing file: references/journal_database.md - note
frontmatter-keyunknown frontmatter key "agent_created"
Process rating: all ten parameters 51/100
- 0Tools and files. 2 referenced file(s) missing: references/jcr_verified_data.csv, references/journal_database.md
- 0Progress reporting. Says nothing while it works
- 30Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 14 mutating operations with no state check
- 40Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (meta-analysis-journal-selector) differs from the folder (meta-analysis-journa-selector)
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 5926 tokens
- 100Steps. 82 steps
- 100Failures and branches. 9 branches, has a failure section
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +5Description quotes 9 example trigger phrases
- +3Description length 695: enough signal without eating the budget
- +4Structure: 29 headings
- +3Step-by-step instructions: 82 items
- +4Has examples (8 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.