BF plant-species-recognition-analysis
Accurately identifies plant species from images based on deep learning and computer vision, outputs structured information including species name, family, growth habits and maintenance tips. | 植物物种识别技能，基于深度学习与计算机视觉技术，通过图像快速识别植物物种，输出物种名称、科属分类、生长习性及养护要点等结构化信息，为园艺、生态调研、自然教育提供专业识别服务
Accurately identifies plant species from images based on deep learning and computer vision, outputs structured information including species name, family…
As a process F 19/100 · Will not run — References files that are not bundled: references/api_doc.md
The same skill appears in 1 more place: ClawHub
How to improve
- Say in the description WHEN to use the skill ("use when…", example requests): that is the agent's main cue.
- The text references files that are not there: add them or drop the references.
- 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: 29. 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") - warning
missing-refreference to a missing file: references/api_doc.md
Process rating: all ten parameters 19/100
- 0Tools and files. 1 referenced file(s) missing: references/api_doc.md
- 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
- 40Consistency. Frontmatter name (plant-species-recognition-analysis) differs from the folder (smyx-plant-species-recognition-analysis)
- 100Execution cost. Instruction body is 1405 tokens
- 100Running it twice. No mutating operations
- 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
- +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
- -255 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 279: enough signal without eating the budget
- +4Structure: 20 headings
- +4Has examples (3 code blocks)
- +1License stated
Quality base 70; lint remarks subtract, signals add up to 100. Result: 59.