BF fall-detection-image-analysis
Detects whether anyone has fallen within a specified target area. Supports both image and short video analysis. Suitable for scenarios such as home care for elderly people living alone and safety monitoring in nursing homes. | 检测目标区域内是否有人跌倒，支持图片和短视频检测，适用于独居老人居家看护、养老院安全监测等场景
As a process F 30/100 · Will not run — 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.
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: 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 30/100
- 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
- 40Consistency. Frontmatter name (fall-detection-image-analysis) differs from the folder (smyx-fall-detection-image-analysis)
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Execution cost. Instruction body is 1215 tokens
- 100Running it twice. No mutating operations
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
- -253 emoji in the instructions: noise for the model
- -32 of 4 scripts are never mentioned in SKILL.md
- +2Single-language instructions
- +3Description length 274: enough signal without eating the budget
- +4Structure: 18 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.
External checks
ClawHub: suspicious
The skill performs fall-detection analysis, but it also silently handles identity, account login/registration, token storage, history retrieval, and is configured for private HTTP development endpoints.
LLM: suspicious (high) · 24 Aug 2026