SKILLEMALL.ai
RU Sign in

AB FEP-alternative

Relative binding free-energy and activity-label prediction workflows using PBCNet 2.0 on SciMiner, with Gnina docking and PDB/database retrieval to complete missing inputs.

ClawHub Agent Skills author: SciMiner v1.0.1 MIT-0 4 files body ≈ 3 286 tokens Open the sourceclawhub.ai↗ analyzed 19 h ago

Relative binding free-energy and activity-label prediction workflows using PBCNet 2.0 on SciMiner, with Gnina docking and PDB/database retrieval to complete…

As a process B 65/100 · Nearly there — weak spots: result and completion, running it twice

ProcedureAI and agentstype 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
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
B
65/100
Nearly there
Result and completion w 14
0
Running it twice w 4
30
Failures and branches w 10
55
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

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "credential_files"

    Process rating: all ten parameters 65/100

    • 0Result and completion. Does not say what the result is
    • 30Running it twice. 4 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 60Tools and files. Uses tools (bash) that frontmatter does not declare
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 70 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 3286 tokens
    • 100Progress reporting. Reports progress
    • low 11 top-level sections: this looks like several domains in one skill

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 172: enough signal without eating the budget
    • +4Structure: 12 headings
    • +3Step-by-step instructions: 70 items
    • +4Has examples (1 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

    External checks

    ClawHub: suspicious
    The skill’s main scientific workflow is understandable, but its bundled helper can make broader web requests and write raw responses more freely than the stated PDB lookup purpose requires.
    LLM: suspicious (medium) · 13 Jun 2026