SKILLEMALL.ai

AC molecular-docking

Run and diagnose expert-grade protein-ligand molecular docking through SciMiner using Gnina, AutoDock Vina, PackDock, SurfDock, DiffDock, and fpocket. Use for focused docking, pocket-aware engine selection, multi-seed ensemble sampling, pose-pool construction, energy-gap filtering, RMSD clustering, cross-run recurrence analysis, critical-contact checks, physics sanity checks, confidence grading, and multi-engine comparisons. Do not use a single run or Rank 1 score as the final answer.

ClawHub Agent Skills author: SciMiner v1.0.4 MIT-0 2 files body ≈ 3 862 tokens Open the sourceclawhub.ai analyzed 3 d ago

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

AnalyzerInfrastructuretype and topics are labelled automatically from the skill text
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
88
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 · 0

    ✓ No critical or high findings

    Files scanned: 2. 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 63/100

    • 0Result and completion. Does not say what the result is
    • 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
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70Failures and branches. 8 branches
    • 85Steps. 76 steps, 1 vague phrases
    • 100Inputs and preconditions. Inputs and preconditions are listed
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3862 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
    • +3Output format is not stated: the model decides each time
    • +1No license
    • +2Single-language instructions
    • +4Description says when NOT to use the skill
    • +3Description length 489: enough signal without eating the budget
    • +4Structure: 25 headings
    • +3Step-by-step instructions: 76 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.

    External checks

    ClawHub: suspicious
    The skill has a coherent molecular-docking purpose, but it lets mutable remote documentation control where credentials and uploaded scientific files are sent.
    LLM: suspicious (high) · 10 Sept 2026