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AB matlab-simulation-optimizer

Audit and optimize MATLAB simulation programs against a corresponding academic paper or specification. Use when Codex needs to compare local MATLAB simulation source code with a paper, verify the system model, algorithm fidelity, parameters, iteration and stopping conditions, metrics, and experiments, directly improve the source code for correctness and runtime while preserving paper-mandated algorithm logic and parameters, and generate a Chinese modification summary document.

ClawHub Agent Skills author: orbisz v1.0.0 MIT-0 6 files body ≈ 2 444 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Audit and optimize MATLAB simulation programs against a corresponding academic paper or specification.

As a process B 76/100 · Nearly there — weak spots: consistency, running it twice, progress reporting

AnalyzerSoftware developmentWriting and documentstype and topics are labelled automatically from the skill text
Runs in: OpenClaw 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) Goose OpenCode
JSON
Technical rating
A
98/100
safety, quality, tests
Safety 60%
100
Quality 40%
94
Run on models
none yet
Process rating
B
76/100
Nearly there
Progress reporting w 2
0
Running it twice w 4
30
Consistency w 8
40
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 · 0

    ✓ No critical or high findings

    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    • note edit-residue the text marks something as outdated (lines 26): check that old rules are not kept next to new ones — the full check reads the text for contradictions

    Process rating: all ten parameters 76/100

    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 7 mutating operations with no state check
    • 40Consistency. Frontmatter name (matlab-simulation-optimizer) differs from the folder (matlab-simulation-optimizer-skill)
    • 50When it triggers. No condition that starts the skill
    • 60Result and completion. Output format stated, no completion criterion
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 52 steps
    • 100Failures and branches. 2 branches, has a failure section
    • 100Execution cost. Instruction body is 2444 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)
    • +1No license
    • +2Single-language instructions
    • +3Description length 481: enough signal without eating the budget
    • +4Structure: 9 headings
    • +3Step-by-step instructions: 52 items
    • +3Output format is stated explicitly
    • +4Has examples (4 code blocks)
    • +4Reference files are cited in the instructions (1 of 1)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 94.

    External checks

    ClawHub: suspicious
    The skill mostly does the MATLAB optimization it advertises, but it also tells the agent to keep persistent diary notes and propose changes to the skill itself, which goes beyond a normal user task.
    LLM: suspicious (high) · VirusTotal: · 28 May 2026