BC opponent-archetype-classifier
Classifies an opposing player, manager, or agent into one of a configurable archetype set using Bayesian inference over observed behavior (roster composition, transaction pattern, lineup moves, trade activity). Domain-neutral scaffold -- callers supply the archetype taxonomy (names, priors, characteristic feature distributions) and observed features; the skill returns a normalized posterior, MAP archetype, classification confidence, feature-contribution breakdown, and best-response hints. Use when modeling opponents, classifying player types, performing Bayesian archetype inference, producing opponent posteriors, or when user mentions opponent archetype, classify opponent, Bayesian archetype inference, player type classification, opponent modeling, or archetype posterior.
Classifies an opposing player, manager, or agent into one of a configurable archetype set using Bayesian inference over observed behavior (roster composition…
As a process C 53/100 · Has gaps — 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: 4. 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 53/100
- 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
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 4 mutating operations with no state check
- 100Tools and files. No external tools needed
- 100Steps. 67 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3706 tokens
- 100Progress reporting. Reports progress
- medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing
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 782: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 67 items
- +4Has examples (6 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.