SKILLEMALL.ai

AC arquitecto-categorico

Disena y audita arquitecturas de datos y APIs con teoria de categorias. Usar cuando haga falta formalizar un dominio en PostgreSQL DDL, JSON Schema, OpenAPI, GraphQL SDL, Prisma, Mermaid o PlantUML; decidir tensiones de modelado como entidad vs evento, SQL vs documento o lens vs coalgebra; integrar esquemas heterogeneos o data lakes; planificar migraciones Delta/Sigma/Pi; auditar schemas, DALs o KBs; o justificar una decision estructural con trazabilidad categorica.

ClawHub Agent Skills author: felix-antonio-sl v1.0.1 MIT-0 33 files body ≈ 580 tokens Open the sourceclawhub.ai analyzed 3 d ago

As a process C 62/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerPostgreSQLtype and topics are labelled automatically from the skill text
JSON
Technical rating
A
90/100
safety, quality, tests
Safety 60%
100
Quality 40%
75
Run on models
none yet
Process rating
C
62/100
Has gaps
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten
This is a copy of a skill from another catalog; the rating counts the canonical one: arquitecto-categorico (ClawHub)

How to improve

  1. Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
For the model run — optional
  • 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: 33. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning description-no-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 62/100

  • 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
  • 60Result and completion. Output format stated, no completion criterion
  • 100Tools and files. No external tools needed
  • 100Steps. 23 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 580 tokens
  • 100Running it twice. No mutating operations
  • 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)
  • +4No input/output examples
  • +1No license
  • +2Single-language instructions
  • +3Description length 470: enough signal without eating the budget
  • +4Structure: 5 headings
  • +3Step-by-step instructions: 23 items
  • +3Output format is stated explicitly
  • +4Reference files are cited in the instructions (4 of 9)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.

External checks

ClawHub: clean
This is an instruction-only modeling/auditing skill with no code, install steps, credentials, or evidence of hidden behavior.
LLM: benign (medium) · VirusTotal: · 29 May 2026