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AC java-architect

Use when building, configuring, or debugging enterprise Java applications with Spring Boot 3.x, microservices, or reactive programming. Invoke to implement WebFlux endpoints, optimize JPA queries and database performance, configure Spring Security with OAuth2/JWT, or resolve authentication issues and async processing challenges in cloud-native Spring applications.

Jeffallan/claude-skills Agent Skills author: Jeffallan MIT 6 files body ≈ 1 269 tokens Open the sourcegithub.com↗ analyzed 5 d ago

x, microservices, or reactive programming.

As a process C 52/100 · Has gaps — weak spots: when it triggers, inputs and preconditions, failures and branches

GeneratorSoftware developmenttype and topics are labelled automatically from the skill text
Runs in: Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) OpenClaw Goose OpenCode
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
99
Quality 40%
92
Run on models
none yet
Process rating
C
52/100
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
    • 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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • low Secrets in code secret-high-entropy-token references/testing-patterns.md:335
      High-entropy token-like string (may be an id, hash or a credential) (test fixture / example file)
      void shou…ted() throws Exception {
      fixture

    Files scanned: 6. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note edit-residue the text marks something as outdated (lines 37): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 52/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 22 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1269 tokens

    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)
    • +2Single-language instructions
    • +3Description length 366: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 22 items
    • +3Output format is stated explicitly
    • +4Has examples (3 code blocks)
    • +4Reference files are cited in the instructions (5 of 5)
    • +1License stated

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