SKILLEMALL.ai

CC framework-topology

Turns a crystal structure (CIF) of a hydrogen-bonded, coordination or covalent framework into its underlying net and the quantities a topology question asks for: node definition, the periodic quotient graph, connectivity as distinct neighbours rather than bond counts, degree of interpenetration, coordination sequences and RCSR three-letter symbols, and internodal distances. Use whenever a task asks for the topology, connectivity, interpenetration or net symbol of a HOF, MOF, COF or molecular crystal from CIF or coordinate data; use atomistic-workflows for simulation setup and cheminformatics-definitions for molecular descriptors.

synthetic-sciences/OpenScience Agent Skills author: synthetic-sciences Apache-2.0 1 file body ≈ 1 553 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Turns a crystal structure (CIF) of a hydrogen-bonded, coordination or covalent framework into its underlying net and the quantities a topology question asks…

As a process C 62/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureData and analyticstype and topics are labelled automatically from the skill text
JSON
Technical rating
C
89/100
safety, quality, tests
Safety 60%
95
Quality 40%
80
Run on models
none yet
Process rating
C
62/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
0
Progress reporting w 2
0
the three weakest of ten parameters · all ten

What is at stake

Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.

Broad scope medium severity

Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.

If you install

The skill asks for more than the task needs: broad tool access, credential environment variables, binaries. Every extra permission widens the damage from a mistake or a compromise.

For the author

Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.

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 · 1

    ✓ No critical or high findings

    Medium and low: 1
    • medium Broad scope meta-broad-allowed-tools SKILL.md:1
      Broad tool permissions pre-approved: Bash
      allowed-tools: Read Write Bash python

    Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • note frontmatter-key unknown frontmatter key "summary"

    Process rating: all ten parameters 62/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 30Running it twice. 4 mutating operations with no state check
    • 55Failures and branches. 1 branches
    • 70When it triggers. States when to use, but not when not to
    • 100Tools and files. Tools declared in frontmatter
    • 100Steps. 14 steps
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1553 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
    • +4No input/output examples
    • +2Single-language instructions
    • +3Description length 637: enough signal without eating the budget
    • +4Structure: 4 headings
    • +3Step-by-step instructions: 14 items
    • +1License stated

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.