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BC comsol-opto-simulation

Automate topic-neutral COMSOL Multiphysics optical, semiconductor, thermal, and coupled optoelectronic simulations through Python/mph. Use when a user needs COMSOL environment discovery, project configuration, parameter sweeps, solver diagnostics, or simulation post-processing.

ClawHub Agent Skills author: leima-max v1.0.0 MIT-0 80 files body ≈ 1 032 tokens Open the sourceclawhub.ai↗ analyzed 24 h ago

Automate topic-neutral COMSOL Multiphysics optical, semiconductor, thermal, and coupled optoelectronic simulations through Python/mph.

As a process C 63/100 · Has gaps — weak spots: result and completion, running it twice, progress reporting

ProcedureSoftware developmenttype 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
B
89/100
safety, quality, tests
Safety 60%
93
Quality 40%
84
Run on models
none yet
Process rating
C
63/100
Has gaps
Result and completion w 14
0
Progress reporting w 2
0
Running it twice w 4
30
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 · 7

    ✓ No critical or high findings

    Medium and low: 7
    • low Secrets in code secret-high-entropy-token scripts/extract_detector_metrics_v2.py:515
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      J_da…_m2 = config.get("dark…_m2", 1.0)
      quoted
    • low Secrets in code secret-high-entropy-token scripts/extract_detector_metrics_v2.py:527
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "dark…cm2": J_da…cm2,
      quoted
    • low Secrets in code secret-high-entropy-token scripts/extract_detector_metrics.py:158
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      J_da…_m2 = config.get("dark…_m2", 1.0)
      quoted
    • low Secrets in code secret-high-entropy-token scripts/extract_detector_metrics.py:176
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "dark…cm2": J_da…cm2,
      quoted
    • low Secrets in code secret-high-entropy-token scripts/extract_detector_metrics.py:267
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      d["dark…cm2"],
      quoted
    • low Secrets in code secret-high-entropy-token scripts/run_parameter_sweep.py:274
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "dark…_m2": case_config.get("dark…_m2", 1.0),
      quoted
    • low Secrets in code secret-high-entropy-token templates/config_extract.json:7
      High-entropy token-like string (may be an id, hash or a credential) (quoted — discussed, not commanded)
      "dark…_m2": "<CONFIGURE_DARK_CURRENT_DENSITY_IF_RELEVANT>",
      quoted

    Files scanned: 80. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 63/100

    • 0Result and completion. Does not say what the result is
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 1 mutating operations with no state check
    • 50Failures and branches. 0 branches, has a failure section
    • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 85Steps. 44 steps, 1 vague phrases
    • 100When it triggers. States when to use and when not to
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1032 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)
    • +3Output format is not stated: the model decides each time
    • -351 of 58 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 278: enough signal without eating the budget
    • +4Structure: 8 headings
    • +3Step-by-step instructions: 44 items
    • +4Has examples (6 code blocks)
    • +4Reference files are cited in the instructions (10 of 11)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 84.

    External checks

    ClawHub: suspicious
    The skill is mostly coherent COMSOL automation, but it includes diagnostic and repair scripts that can overwrite simulation models without clear confirmation or backup.
    LLM: suspicious (high) · 28 May 2026