AD mushroom-cultivation-basics
Use when you need reliable, low-space, waste-based food production for nutrition security, supplemental income, or embodied self-reliance in post-AI gig-economy instability. Agent personalizes species and method by your exact space, climate, budget, and goals; generates scaled recipes, automated calendars with reminders, supplier research, contamination decision trees, yield forecasts, and filesystem logs. Human performs all physical substrate prep, sterile inoculation, daily environmental adjustments, and harvesting — the hands-on craft only a human can feel and perfect.
As a process D 49/100 · Unfinished process — weak spots: result and completion, inputs and preconditions, failures and branches
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 · 0
✓ No critical or high findings
Files scanned: 2. Evidence is masked. Grey chips explain why severity was lowered.
Against the Agent Skills spec
✓ No remarks against the Agent Skills spec
Process rating: all ten parameters 49/100
- 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
- 30Running it twice. 2 mutating operations with no state check
- 50When it triggers. No condition that starts the skill
- 60Tools and files. Uses tools (web) that frontmatter does not declare
- 100Steps. 88 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 3135 tokens
- 100Progress reporting. Reports progress
- low 10 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
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +4Description says when NOT to use the skill
- +3Description length 578: enough signal without eating the budget
- +4Structure: 17 headings
- +3Step-by-step instructions: 88 items
- +4Has examples (4 code blocks)
Quality base 70; lint remarks subtract, signals add up to 100. Result: 88.