SKILLEMALL.ai

AC enterprise-architecture

Design and evolve enterprise architectures by connecting business capabilities, value streams, applications, information, technology, operating models, and transition choices. Use when mapping an enterprise portfolio, comparing current and target states, sequencing transition architectures, or defining federated architecture decision rights and stakeholder communication. Do not use for system or solution design, API or data-platform design, organizational or talent design, product roadmaps, technology adoption, or corporate strategy; route those to the named specialist skills.

magnus919/agent-skills Agent Skills author: magnus919 MIT 12 files body ≈ 1 386 tokens Open the sourcegithub.com↗ analyzed 25 h ago

Design and evolve enterprise architectures by connecting business capabilities, value streams, applications, information, technology, operating models, and…

As a process C 60/100 · Has gaps — weak spots: inputs and preconditions, failures and branches, running it twice

ProcedureOperations and projectstype and topics are labelled automatically from the skill text
JSON
Technical rating
A
97/100
safety, quality, tests
Safety 60%
100
Quality 40%
92
Run on models
none yet
Process rating
C
60/100
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

How to improve

    For the model run — optional
    • 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: 11. 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 60/100

    • 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
    • 30Running it twice. 3 mutating operations with no state check
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 85Steps. 17 steps, 1 vague phrases
    • 100Tools and files. No external tools needed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1386 tokens
    • medium 9 test cases, all positive: not one "should refuse" or "should ask first"
    • low No test case covers injection arriving through data

    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
    • +4No input/output examples
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 583: enough signal without eating the budget
    • +4Structure: 7 headings
    • +3Step-by-step instructions: 17 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (6 of 6)
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 92.