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CD 短视频黄金 3 秒钩子生成器

🎬 自动生成高完播、高停留、高流量的短视频开头钩子

Not recommendedcritical or high security findings
modbender/skill-library-mcp Agent Skills author: modbender MIT 13 files body ≈ 332 tokens Open the sourcegithub.com↗ analyzed 2 d ago

🎬 自动生成高完播、高停留、高流量的短视频开头钩子

As a process D 46/100 · Unfinished process — weak spots: result and completion, when it triggers, inputs and preconditions

Proceduretype 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
C
60/100
safety, quality, tests
Safety 60%
63
Quality 40%
56
Run on models
none yet
Process rating
D
46/100
Unfinished process
Result and completion w 14
0
When it triggers w 12
0
Inputs and preconditions w 11
0
the three weakest of ten parameters · all ten

What is at stake

The skill contains fragments that, in the wrong hands, cost money or data. Below: what the installer risks and what the author should do.

Dangerous commands
If you install

The skill contains commands that delete files, rewrite disks or execute code fetched from the network. The agent may run them without asking if it believes the instructions require it.

For the author

Replace destructive commands with safe equivalents that ask for confirmation, scope them to one folder, and stop piping curl into a shell: pin a version and a checksum.

How to improve

  1. Address the high-severity findings: each costs 18 safety points. If one is a false positive, add the rule id to guard.allow in spec.yaml.
  2. 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 · 3

  • high Dangerous commands cmd-persistence DEPLOY.md:87
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    schtasks /create /tn "ShortVideoHook_Monitor" /tr "python C:\Users\11644\.openclaw\workspace\skills\shortvideo-hook\monitor.py report" /sc daily /st 19:00
  • high Dangerous commands cmd-persistence PUBLISH.md:159
    Persistence mechanism (cron / launchd / scheduled task / autorun registry)
    Register-ScheduledTask -TaskName "ShortVideoHook_Monitor" -Action $action -Trigger $trigger -Description "短视频钩子生成器 - 每日数据监控"
Medium and low: 1
  • low Dangerous commands cmd-cron-mention DEPLOY.md:94
    Mentions editing / listing crontab
    crontab -e

Files scanned: 13. 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-short description under 40 chars: too little signal for triggering
  • warning description-no-when description 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
  • 0When it triggers. No condition that starts the skill
  • 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
  • 40Consistency. Frontmatter name (短视频黄金 3 秒钩子生成器) differs from the folder (shortvideo-hook)
  • 100Tools and files. No external tools needed
  • 100Steps. 14 steps
  • 100Execution cost. Instruction body is 332 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)
  • +3Description length 26: 120–800 characters recommended
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +4Structure: 11 headings
  • +3Step-by-step instructions: 14 items
  • +4Has examples (5 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 56.