BC agente-fisica
Agente especialista em Física para FUVEST — Ciência da Computação (IME-USP). Física é a segunda disciplina específica da 2ª fase, junto com Matemática. Domínio total é obrigatório. Cobre: Cinemática, Dinâmica, Energia, Termodinâmica, Eletrostática, Eletrodinâmica, Eletromagnetismo, Óptica e Ondulatória. Opera em integração com vestibular-tutor, vestibular-srs, vestibular-planner e agente-matematica (Física usa Matemática constantemente). Prioridade MÁXIMA — entra no ciclo logo após Matemática e Português consolidarem base. Acionar quando Antonio mencionar qualquer tópico de física, perguntar sobre forças/movimento/energia/eletricidade/ondas, ou o orchestrator abrir sessão de Física.
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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
description-no-whendescription does not say WHEN to use the skill (no "use when")
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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 100Tools and files. No external tools needed
- 100Steps. 30 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 983 tokens
- 100Running it twice. No mutating operations
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
- -5TODO / placeholder text left in the skill
- +1No license
- +2Single-language instructions
- +3Description length 691: enough signal without eating the budget
- +4Structure: 14 headings
- +3Step-by-step instructions: 30 items
- +4Has examples (1 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 67.