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BD WeChat Contact List Video Extraction & CRM Analyzer

Zero-risk, permission-free WeChat personal contacts extraction and structured CRM pipeline. Uses macOS native screen recording (Cmd+Shift+5), high-FPS FFmpeg frame extraction, Apple Silicon concurrent Vision OCR, and multi-dimensional rule-based NLP extraction (Name, Title, Org, Venue, City, Time) to generate Excel/CSV, JSON, and interactive HTML dashboards.

ClawHub Agent Skills author: emergencescience v0.1.0 MIT-0 8 files body ≈ 587 tokens Open the sourceclawhub.ai↗ analyzed 2 d ago

Zero-risk, permission-free WeChat personal contacts extraction and structured CRM pipeline.

As a process D 41/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

AnalyzerData and analyticsSales and CRMtype and topics are labelled automatically from the skill text
Runs in: OpenClaw Claude / Claude Code ChatGPT / Codex Gemini CLI Cursor GitHub Copilot / VS Code Qwen Code Kimi Code CLI Z.ai (GLM) DeepSeek (Deep Code) Mistral Vibe LangChain (deepagents) Goose OpenCode
JSON
Technical rating
B
88/100
safety, quality, tests
Safety 60%
100
Quality 40%
70
Run on models
none yet
Process rating
D
41/100
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: 7. Evidence is masked. Grey chips explain why severity was lowered.

Against the Agent Skills spec

  • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
  • warning description-no-when description does not say WHEN to use the skill (no "use when")
  • note frontmatter-key unknown frontmatter key "slug"

Process rating: all ten parameters 41/100

  • 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 (WeChat Contact List Video Extraction & CRM Analyzer) differs from the folder (emergence-wechat-contact-crm)
  • 60Tools and files. Uses tools (bash, python) that frontmatter does not declare
  • 100Steps. 10 steps
  • 100Execution cost. Instruction body is 587 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
  • +2Single-language instructions
  • +3Description length 360: enough signal without eating the budget
  • +4Structure: 10 headings
  • +3Step-by-step instructions: 10 items
  • +4Has examples (4 code blocks)
  • +3All 2 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 70.

External checks

ClawHub: suspicious
This skill is user-directed and mostly local, but it is designed to bulk extract private WeChat contact data into durable CRM files while understating privacy risk and user-control requirements.
LLM: suspicious (high) · 17 Aug 2026