CC energy-systems
Applies energy-systems modelling conventions so that capacity, energy, cost and emissions numbers are consistent and comparable, covering kW versus kWh and MW versus MWh, capacity versus energy, capacity factors, load duration curves, the LCOE formula with discounting and capital recovery factors, dispatch and capacity-expansion modelling with PyPSA and pyomo, time-series alignment across time zones and interval conventions, emissions factors by scope, and the assumptions table every result needs. Use for power-system, generation cost, storage, grid-mix or decarbonization analyses; use statistical-conventions for the statistics and geoscience-data for weather and resource rasters.
Applies energy-systems modelling conventions so that capacity, energy, cost and emissions numbers are consistent and comparable, covering kW versus kWh and MW…
As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions
What is at stake
Medium-severity findings: the skill is probably honest, but read what alarmed the scanner.
Below is the worst case for this category. The finding here is medium: the guard saw a sign, not a proof.
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.
Narrow allowed-tools and the variable list to the minimum; replace binaries with readable sources or scripts.
How to improve
- 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-toolsSKILL.md:1Broad tool permissions pre-approved: Bashallowed-tools: Read Bash python
Files scanned: 1. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
- note
frontmatter-keyunknown frontmatter key "summary"
Process rating: all ten parameters 53/100
- 0Result and completion. Does not say what the result is
- 0Inputs and preconditions. Does not say what the process needs to start
- 0Failures and branches. Linear process with no failure handling
- 20When it triggers. No condition that starts the skill
- 30Running it twice. 3 mutating operations with no state check
- 100Tools and files. Tools declared in frontmatter
- 100Steps. 20 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1466 tokens
- 100Progress reporting. Reports progress
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 689: enough signal without eating the budget
- +4Structure: 7 headings
- +3Step-by-step instructions: 20 items
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 80.