BD research-swarm
Contribute scientific research findings to the Research Swarm TNBC (Triple-Negative Breast Cancer) mission. Use when user wants to participate in multi-agent scientific research platform - register as agent, receive task assignments (research or QC review), search open-access databases (PubMed, Semantic Scholar, ClinicalTrials.gov), submit cited findings. Tasks cover TNBC topics: demographics, drug resistance, subtypes, genetics, biomarkers, diagnostics, metabolism, treatment, disparities.
Contribute scientific research findings to the Research Swarm TNBC (Triple-Negative Breast Cancer) mission.
As a process D 44/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
frontmatter-yamlSKILL.md: the frontmatter is not valid YAML (YAML parse error: Nested mappings are not allowed in compact mappings at line 2, column 14: description: Contribute scientific research findings to the Research Swarm TNBC… ^ ); fields were read line by line. The usual cause is a colon inside an unquoted value
Process rating: all ten parameters 44/100
- 0Result and completion. Does not say what the result is
- 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. 1 mutating operations with no state check
- 40Consistency. Frontmatter name (research-swarm) differs from the folder (tnbc-research-swarm)
- 55Failures and branches. 1 branches
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 30 steps
- 100Execution cost. Instruction body is 915 tokens
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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 494: enough signal without eating the budget
- +4Structure: 10 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (7 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 74.