BB sr-screener
Protocol-driven study screening for systematic, scoping and rapid reviews. Turns a proposal or PROSPERO protocol into confirmed eligibility rules, then screens titles/abstracts and full texts with two blinded AI reviewers and a third-reviewer adjudicator: ordered exclusion codes, no silent defaults, resumable batch runs, QC (seed studies, near-miss rechecks, kappa and PABAK), PRISMA 2020 counts, EndNote/Zotero RIS groups, an Excel log, a methods draft and a literature_corpus handoff to academic-paper. Reads RIS, PubMed .nbib, Web of Science and CSV exports; removes duplicates. 8 modes: protocol, quick, pilot, ta-screen, ft-screen, adjudicate, audit, report. Use it to screen records against eligibility criteria, pilot screening, resolve screening conflicts, audit exclusions or report the selection process. Triggers on: screen these papers, title/abstract screening, full-text screening, screening conflicts, inclusion criteria, PRISMA flow, غربالگری مقالات, اسکرینینگ عنوان و چکیده, معیارهای ورود و خروج.
Protocol-driven study screening for systematic, scoping and rapid reviews.
As a process B 66/100 · Nearly there — weak spots: inputs and preconditions, execution cost, running it twice
How to improve
- 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: 25. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
body-longSKILL.md body ≈ 8100 tokens (recommended < 5000); move details to references/ - note
edit-residuethe text marks something as outdated (lines 273): check that old rules are not kept next to new ones — the full check reads the text for contradictions
Process rating: all ten parameters 66/100
- 0Inputs and preconditions. Does not say what the process needs to start
- 30Running it twice. 1 mutating operations with no state check
- 40Execution cost. Instruction body is 8100 tokens: crowds the task out of the window
- 60Tools and files. Uses tools (bash, 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
- 100Steps. 41 steps
- 100Failures and branches. 2 branches, has a failure section
- 100Consistency. Name and required fields are in place
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
- low 24 top-level sections: this looks like several domains in one skill
- low The response is described with custom markup (5 tags): a typed call is more reliable
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 1015: 120–800 characters recommended
- +1No license
- +2Single-language instructions
- +4Structure: 29 headings
- +3Step-by-step instructions: 41 items
- +3Output format is stated explicitly
- +4Has examples (5 code blocks)
- +4Reference files are cited in the instructions (7 of 7)
- +3All 6 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 81.