AD tencentcloud-faceid-detectface
腾讯云人脸检测(DetectFace)接口调用技能。当用户需要对图片进行人脸检测时，应使用此技能。可检测图片中的人脸位置（坐标、宽高），并可选返回人脸属性（性别、年龄、表情、魅力、眼镜、口罩、头发、姿态）和人脸质量信息（质量分、模糊分、光照分、遮挡分）。支持图片Base64和图片URL两种输入方式，支持同时检测多张人脸。
As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions
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: 3. 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 46/100
- 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
- 60Tools and files. Uses tools (python) that frontmatter does not declare
- 100Steps. 18 steps
- 100Consistency. Name and required fields are in place
- 100Execution cost. Instruction body is 760 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)
- +3Output format is not stated: the model decides each time
- +1No license
- +2Single-language instructions
- +3Description length 161: enough signal without eating the budget
- +4Structure: 11 headings
- +3Step-by-step instructions: 18 items
- +4Has examples (2 code blocks)
- +3All 1 scripts are documented
Quality base 70; lint remarks subtract, signals add up to 100. Result: 75.
External checks
ClawHub: clean
This skill does what it claims: it manually sends selected face images or image URLs to Tencent Cloud for face detection, which is sensitive but disclosed and purpose-aligned.
LLM: benign (high) · VirusTotal: · 29 May 2026