SKILLEMALL.ai

BD familiar-person-recognition-analysis

Identifies acquaintances in videos or images through face photo comparison. Supports database enrollment, and the recognition results tell you who is at which location. Suitable for identity verification in homes and office areas. | 熟人识别分析技能,通过人脸图片比对识别视频/图片中的熟人,支持底库录入,识别结果告诉你哪个位置是谁,适用于家庭、办公区域身份核验

ClawHub Agent Skills author: smyx-skills v1.0.14 MIT-0 30 files body ≈ 1 259 tokens Open the sourceclawhub.ai analyzed 4 d ago

As a process D 38/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerInfrastructureMedia and videotype and topics are labelled automatically from the skill text
JSON
Technical rating
B
89/100
safety, quality, tests
Safety 60%
100
Quality 40%
72
Run on models
none yet
Process rating
D
38/100
Unfinished process
Result and completion w 14
0
Inputs and preconditions w 11
0
Failures and branches w 10
0
the three weakest of ten parameters · all ten

How to improve

  1. 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-when description does not say WHEN to use the skill (no "use when")

Process rating: all ten parameters 38/100

  • 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
  • 40Consistency. Frontmatter name (familiar-person-recognition-analysis) differs from the folder (smyx-familiar-person-recognition-analysis)
  • 60Tools and files. Uses tools (python) that frontmatter does not declare
  • 75Steps. 3 steps
  • 100Execution cost. Instruction body is 1259 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
  • -254 emoji in the instructions: noise for the model
  • -32 of 4 scripts are never mentioned in SKILL.md
  • +2Single-language instructions
  • +3Description length 297: enough signal without eating the budget
  • +4Structure: 19 headings
  • +3Step-by-step instructions: 3 items
  • +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: 72.

External checks

ClawHub: suspicious
This face-recognition skill should be reviewed carefully because it silently creates or reuses an account identity, stores tokens locally, and sends biometric media and history requests to external services.
LLM: suspicious (high) · 27 Aug 2026