BD smyx-succulent-special-state-detection-analysis
AI-powered succulent special-state detection from HD images via plant cameras or smartphones. Identifies three critical conditions—black rot (stem base or leaves turning black and mushy), etiolation/melting (leaves becoming translucent and water-soaked), and stretching (elongated internodes, widened leaf spacing, loose rosette)—and outputs the anomaly type with severity grading, enabling early intervention such as beheading, water restriction, or increased light. Scenarios: home succulent care, succulent greenhouses, flower shops. | 通过多肉种植摄像头或手机拍摄的高清图像，利用AI视觉分析技术识别多肉植物的三种常见异常状态：黑腐病（茎基部或叶片变黑、腐烂）、化水（叶片透明化、水渍状）、徒长（茎节拉长、叶片间距增大、形态松散）。输出对应的异常状态类型及严重程度，帮助种植者及时采取处理措施（如砍头、控水、增加光照）。应用场景：多肉植物家庭养护、多肉大棚、花店。
As a process D 35/100 · Unfinished process — weak spots: steps, result and completion, when it triggers
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- 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: 30. 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 35/100
- 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
- 0Progress reporting. Says nothing while it works
- 20When it triggers. No condition that starts the skill
- 25Steps. 1 steps
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 1857 tokens
- 100Running it twice. No mutating operations
- low 15 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
- +4Description does not say when NOT to use the skill (false activations)
- +3No numbered steps or checklist
- +3Output format is not stated: the model decides each time
- -285 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 703: enough signal without eating the budget
- +4Structure: 28 headings
- +4Has examples (3 code blocks)
- +4Reference files are cited in the instructions (1 of 1)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.