SKILLEMALL.ai

BC synomega

Retrosynthesis, reaction prediction, and synthesizability for organic molecules, using the synomega Python package (pip install synomega) — runs locally, works out of the box. Six capabilities: single-step retrosynthesis (product → reactants, candidate disconnections), single-step forward reaction prediction / reaction outcome (reactants → product), multi-step route planning down to purchasable building blocks, a continuous synthesizability / makeability score (SynScore), reaction-plausibility screening, and multi-component evolution (growing a forward synthesis network from a set of reactants, e.g. one-pot / multicomponent chemistry). Use this whenever the user gives a molecule (as SMILES or a resolvable name) and asks how to make / synthesize it, whether it can be made or how hard, how to rank molecules by ease of synthesis, what reactants give a target, what product a set of reactants gives, a reaction outcome, or how a reactant mixture evolves — i.e. for retrosynthesis, synthesis planning, cheminformatics, and reaction-prediction tasks. Safety judgments for hazardous, controlled, or otherwise dual-use compounds are deferred to the host's safety policy (see "Safety boundary / dual-use" below).

ClawHub Agent Skills author: zhangbc v1.8.1 MIT-0 5 files body ≈ 3 050 tokens Open the sourceclawhub.ai analyzed 2 d ago

Retrosynthesis, reaction prediction, and synthesizability for organic molecules, using the synomega Python package (pip install synomega) — runs locally…

As a process C 55/100 · Has gaps — weak spots: result and completion, inputs and preconditions, running it twice

ProcedureGitHubSoftware developmenttype and topics are labelled automatically from the skill text
JSON
Technical rating
B
84/100
safety, quality, tests
Safety 60%
100
Quality 40%
60
Run on models
none yet
Process rating
C
55/100
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

How to improve

  1. Shorten the description to 1024 characters.
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: 5. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • error description-long description is 1215 chars, limit 1024

Process rating: all ten parameters 55/100

  • 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. 3 mutating operations with no state check
  • 55Failures and branches. 1 branches
  • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
  • 70When it triggers. States when to use, but not when not to
  • 100Steps. 14 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 3050 tokens
  • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

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)
  • +3Description length 1215: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 15 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (10 code blocks)
  • +3All 1 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 60.

External checks

ClawHub: clean
This skill is a disclosed local chemistry tool with expected model downloads and configuration knobs, but users should treat route planning for hazardous compounds carefully.
LLM: benign (high) · VirusTotal: · 29 Aug 2026