AB lca
AI-guided Life Cycle Assessment using openLCA. Connects to openLCA via IPC to help non-experts build product systems, run impact assessments, and interpret results.
As a process B 67/100 · Nearly there — weak spots: result and completion, consistency
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
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: 4. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- warning
description-no-whendescription does not say WHEN to use the skill (no "use when")
Process rating: all ten parameters 67/100
- 0Result and completion. Does not say what the result is
- 40Consistency. Frontmatter name (lca) differs from the folder (lca-skill)
- 60Tools and files. Uses tools (python) 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. 41 steps
- 100Failures and branches. 6 branches, has a failure section
- 100Execution cost. Instruction body is 1261 tokens
- 100Running it twice. Mutating operations check current state
- 100Progress reporting. Reports progress
- low The response is described with custom markup (6 tags): a typed call is more reliable
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 164: enough signal without eating the budget
- +4Structure: 15 headings
- +3Step-by-step instructions: 41 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.
External checks
ClawHub: clean
This skill is a coherent openLCA assistant that uses a local Python bridge for LCA workflows, with ordinary cautions around database changes and dependency installation.
LLM: benign (high) · VirusTotal: · 29 May 2026