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AA deep-research

Deep web research across 11 Chinese and international sources (Baidu, Bing, Sogou, Quark, ChinaSo, WeChat, Yandex, Zhihu, Bilibili, V2EX, GitHub). Use when the user asks for deep research, comprehensive search, multi-source search, or needs results from Chinese platforms (Zhihu, Bilibili, WeChat). Falls back to built-in web_search if the API is unavailable.

ClawHub Agent Skills author: nb-clh v1.0.0 MIT-0 4 files body ≈ 365 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process A 81/100 · Runs to the end — weak spots: consistency, progress reporting

IntegrationGitHubInfrastructuretype 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
A
91/100
safety, quality, tests
Safety 60%
100
Quality 40%
78
Run on models
none yet
Process rating
A
81/100
Runs to the end
Progress reporting w 2
0
Consistency w 8
40
Result and completion w 14
60
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: 4. 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 81/100

    • 0Progress reporting. Says nothing while it works
    • 40Consistency. Frontmatter name (deep-research) differs from the folder (agentic-rag-cn)
    • 60Result and completion. Output format stated, no completion criterion
    • 70When it triggers. States when to use, but not when not to
    • 70Inputs and preconditions. Inputs and preconditions are listed
    • 100Tools and files. No external tools needed
    • 100Steps. 9 steps
    • 100Failures and branches. 1 branches, has a failure section
    • 100Execution cost. Instruction body is 365 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)
    • -2localhost URLs: will not work for another user
    • -41 reference files, but SKILL.md never points to them: the model will not open them
    • -31 of 1 scripts are never mentioned in SKILL.md
    • +1No license
    • +2Single-language instructions
    • +3Description length 359: enough signal without eating the budget
    • +4Structure: 6 headings
    • +3Step-by-step instructions: 9 items
    • +3Output format is stated explicitly
    • +4Has examples (2 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 78.

    External checks

    ClawHub: clean
    This skill openly sends research questions to a local research API and shows no hidden persistence, credential access, destructive behavior, or unrelated data collection.
    LLM: benign (high) · VirusTotal: · 28 May 2026