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AB douyin-video-search

Searches Douyin (douyin.com) for videos by keyword and returns structured video data including author info, stats, cover, description, hashtags, and download URL. Supports date range filtering and sorting by relevance, likes, or recency. Use when user mentions Douyin search, scrape Douyin videos, collect TikTok China videos, extract douyin video data, grab douyin results, fetch douyin keyword videos, douyin video list, douyin content mining, search douyin by keyword, douyin likes filter, douyin date filter, douyin video download links, douyin creator info, douyin hashtag extraction, douyin video scraper, douyin KOL research, douyin content analysis.

browser-act/skills Agent Skills author: browser-act MIT 2 files · 1 script body ≈ 1 665 tokens Open the sourcegithub.com↗ analyzed 5 d ago

Searches Douyin (douyin.com) for videos by keyword and returns structured video data including author info, stats, cover, description, hashtags, and download…

As a process B 69/100 · Nearly there — weak spots: result and completion, when it triggers, running it twice

AnalyzerMedia and videotype and topics are labelled automatically from the skill text
Runs in: 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) OpenClaw Goose OpenCode
JSON
Technical rating
A
95/100
safety, quality, tests
Safety 60%
100
Quality 40%
87
Run on models
none yet
Process rating
B
69/100
Nearly there
When it triggers w 12
20
Running it twice w 4
30
Result and completion w 14
40
the three weakest of ten parameters · all ten

How to improve

    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

    ✓ No remarks against the Agent Skills spec

    Process rating: all ten parameters 69/100

    • 20When it triggers. No condition that starts the skill
    • 30Running it twice. 6 mutating operations with no state check
    • 40Result and completion. Does not say what the result is
    • 60Tools and files. Uses tools (bash, web, python) that frontmatter does not declare
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Steps. 29 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Consistency. Name and required fields are in place
    • 100Execution cost. Instruction body is 1665 tokens
    • 100Progress reporting. Reports progress
    • low 11 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 657: enough signal without eating the budget
    • +4Structure: 15 headings
    • +3Step-by-step instructions: 29 items
    • +4Has examples (3 code blocks)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 87.