SKILLEMALL.ai

AC cp2k-crosscode-input-studio

Generate, refine, explain, and cross-convert CP2K-centered input drafts for computational chemistry and materials workflows. Use when a user wants a CP2K .inp file, asks to turn a natural-language request plus structure file into a runnable draft, wants help choosing conservative CP2K defaults, needs an existing CP2K input reviewed for task mapping, periodicity, SCF mode, basis/potential choices, k-points, dispersion, MD settings, or warning flags, or wants a CP2K draft translated into Gaussian, VASP, ORCA, or Quantum ESPRESSO input files. Helpful for CP2K input generation, input translation, quantum chemistry setup, materials simulation setup, and cross-code draft preparation.

ClawHub Agent Skills author: lemon1044 v1.0.1 MIT-0 19 files body ≈ 915 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process C 64/100 · Has gaps — weak spots: inputs and preconditions, running it twice, progress reporting

GeneratorWriting and documentsSoftware developmentInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
90
Run on models
none yet
Process rating
C
64/100
Has gaps
Inputs and preconditions w 11
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 · 0

    ✓ No critical or high findings

    Files scanned: 19. 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 64/100

    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 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
    • 60Failures and branches. 2 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Steps. 31 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 915 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)
    • +4No input/output examples
    • +1No license
    • +2Single-language instructions
    • +3Description length 686: enough signal without eating the budget
    • +4Structure: 5 headings
    • +3Step-by-step instructions: 31 items
    • +3Output format is stated explicitly
    • +4Reference files are cited in the instructions (8 of 8)
    • +3All 4 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 90.

    External checks

    ClawHub: clean
    This skill is an offline CP2K input draft generator/converter with disclosed local Python helpers and no evidence of hidden data access or harmful behavior.
    LLM: benign (high) · VirusTotal: · 29 May 2026