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BF competitor-monitor

竞品数据定时监控Skill，定时访问指定电商或社交平台页面，抓取点赞、评论、销量等数据，检测数据异常波动（如出现爆款），自动截图并发送提醒通知。适用于竞品分析、市场监控、爆款发现等场景。

ClawHub Agent Skills author: rumu14 v1.0.0 MIT-0 10 files · 1 script body ≈ 2 151 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

As a process F 31/100 · Will not run — References files that are not bundled: assets/screenshots/

ReferenceInfrastructuretype 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%
99
Quality 40%
71
Run on models
none yet
Process rating
F
31/100
Will not run
References files that are not bundled: assets/screenshots/
Tools and files w 18
0
Result and completion w 14
0
Inputs and preconditions w 11
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.
  2. The text references files that are not there: add them or drop the references.
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 · 1

✓ No critical or high findings

Medium and low: 1
  • low Dangerous commands cmd-background-process README.md:53
    Starts a background / autostarted process
    nohup python scripts/monitor_service.py start > monitor.log 2>&1 &

Files scanned: 10. 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")
  • warning missing-ref reference to a missing file: assets/screenshots/

Process rating: all ten parameters 31/100

Will not run. References files that are not bundled: assets/screenshots/
  • 0Tools and files. 1 referenced file(s) missing: assets/screenshots/
  • 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 (competitor-monitor) differs from the folder (competitor-monitor-ai)
  • 100Steps. 53 steps
  • 100Execution cost. Instruction body is 2151 tokens
  • 100Running it twice. No mutating operations
  • low 13 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)
  • +3Description length 93: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +2Single-language instructions
  • +4Structure: 43 headings
  • +3Step-by-step instructions: 53 items
  • +4Has examples (21 code blocks)
  • +4Reference files are cited in the instructions (1 of 1)
  • +3All 4 scripts are documented
  • +1License stated

Quality base 70; lint remarks subtract, signals add up to 100. Result: 71.

External checks

ClawHub: clean
This skill behaves like a disclosed competitor-monitoring tool, though users should treat its screenshots, alerts, webhooks, and installer as privacy-sensitive.
LLM: benign (high) · VirusTotal: · 29 May 2026