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抖音直播复盘六部曲（2026新规适配版）。从7天长效考核→场景匹配→收藏/复访/铁粉权重→全域计划投放入口→话术AI实时转写监听到A-D五级费率。每步有合格线、问题定位、责任归属、优化方向。触发词：直播复盘、抖音复盘、播后复盘、数据分析、直播间诊断。

ClawHub Agent Skills author: 1027399464-tech v2.0.1 MIT-0 2 files body ≈ 899 tokens Open the sourceclawhub.ai↗ analyzed 4 d ago

抖音直播复盘六部曲（2026新规适配版）。从7天长效考核→场景匹配→收藏/复访/铁粉权重→全域计划投放入口→话术AI实时转写监听到A-D五级费率。每步有合格线、问题定位、责任归属、优化方向。触发词：直播复盘、抖音复盘、播后复盘、数据分析、直播间诊断。

As a process C 53/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

Analyzertype 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%
69
Run on models
none yet
Process rating
C
53/100
Has gaps
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: 2. 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")
  • note frontmatter-key unknown frontmatter key "slug"
  • note frontmatter-key unknown frontmatter key "displayName"
  • note frontmatter-key unknown frontmatter key "feedback"
  • note frontmatter-key unknown frontmatter key "source"

Process rating: all ten parameters 53/100

  • 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
  • 100Tools and files. No external tools needed
  • 100Steps. 74 steps
  • 100Consistency. Name and required fields are in place
  • 100Execution cost. Instruction body is 899 tokens
  • 100Running it twice. No mutating operations
  • low 10 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)
  • +3Output format is not stated: the model decides each time
  • +1No license
  • +2Single-language instructions
  • +3Description length 125: enough signal without eating the budget
  • +4Structure: 18 headings
  • +3Step-by-step instructions: 74 items
  • +4Has examples (2 code blocks)

Quality base 70; lint remarks subtract, signals add up to 100. Result: 69.

External checks

ClawHub: clean
This is a Chinese-language Douyin livestream review checklist with no code execution, persistence, credential access, or hidden data movement.
LLM: benign (high) · VirusTotal: · 24 Jul 2026