BC topic-modeling
Specifies and diagnoses structural topic models for survey and experimental text — choosing among STM, LDA, and BERTopic, preprocessing decisions and their consequences, prevalence and content formulas with spectral initialization and a recorded seed, selecting the topic count across semantic coherence, exclusivity and FREX, held-out likelihood, and residuals rather than one metric, interpretation and validation against representative documents, robustness checks, and DA-RT-compliant reporting. Use when the user has open-ended responses or another corpus and asks what topics are in it, asks how many topics to use, asks about STM, searchK, coherence, or FREX, or wants topic prevalence compared across treatment arms or countries. Coding against a fixed codebook goes to text-classification.
Specifies and diagnoses structural topic models for survey and experimental text — choosing among STM, LDA, and BERTopic, preprocessing decisions and their…
As a process C 59/100 · Has gaps — weak spots: result and completion, inputs and preconditions, failures and branches
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
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 59/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 0Progress reporting. Says nothing while it works
- 70When it triggers. States when to use, but not when not to
- 100Tools and files. No external tools needed
- 100Steps. 48 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3645 tokens
- 100Running it twice. Mutating operations check current state
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 798: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 48 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.