BC meta-analysis-topic-selector
Topic selection assessment and topic-report generation for meta-analyses (systematic reviews). Triggered when: you want to do a meta-analysis but don't know what topic to pick; you have a research direction and want to assess whether it is suitable for a meta-analysis; you want to evaluate feasibility / novelty / clinical value of a candidate topic; you want to generate a structured topic report; you need PICO decomposition; you need to choose the meta-analysis type; you want PRISMA 2020 and AMSTAR-2 pre-checks; you need guidance on deduplication searches; you are preparing for PROSPERO registration. Covers intervention / exposure / diagnostic / prognostic meta-analysis topic selection in medicine, epidemiology, pharmacy, nursing, public health, psychology, education, and related fields. Keywords: meta-analysis topic selection, systematic review question, PICO, PRISMA 2020, AMSTAR-2, PROSPERO registration, deduplication search, network meta-analysis topic, IPD meta-analysis topic, dose-response meta-analysis topic.
Topic selection assessment and topic-report generation for meta-analyses (systematic reviews).
As a process C 59/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting
How to improve
- Shorten the description to 1024 characters.
- For Hermes the description must be one sentence under 60 characters; move the conditions to a "When to Use" section.
- The SKILL.md body is over 5,000 tokens: move reference detail into references/ and load it when needed.
- 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: 15. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- error
description-longdescription is 1030 chars, limit 1024 - warning
description-long-hermesdescription is 1030 chars; the Hermes authoring standard requires ≤ 60 (one sentence, ending with a period) - warning
body-longSKILL.md body ≈ 5678 tokens (recommended < 5000); move details to references/ - note
frontmatter-keyunknown frontmatter key "slug" - note
frontmatter-keyunknown frontmatter key "description_zh" - note
frontmatter-keyunknown frontmatter key "triggers" - note
frontmatter-keyunknown frontmatter key "agent_created"
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
- 30Running it twice. 2 mutating operations with no state check
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (web, python) that frontmatter does not declare
- 60Result and completion. Output format stated, no completion criterion
- 70When it triggers. States when to use, but not when not to
- 70Execution cost. Instruction body is 5678 tokens
- 85Steps. 145 steps, 1 vague phrases
- 100Consistency. Name and required fields are in place
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)
- +3Description length 1030: 120–800 characters recommended
- -218 emoji in the instructions: noise for the model
- +2Single-language instructions
- +4Structure: 32 headings
- +3Step-by-step instructions: 145 items
- +3Output format is stated explicitly
- +4Has examples (2 code blocks)
- +4Reference files are cited in the instructions (5 of 5)
- +3All 1 scripts are documented
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 46.