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AD collaborative-research

Lucky (internet) + Jinx (analysis) collaborative research workflow. Lucky gathers raw data from web sources, Jinx analyzes and structures findings. Use for market research, competitive analysis, marketplace intelligence, API documentation review, trend analysis, pricing research, or any research requiring both web access and deep analysis. Includes market research templates for competitor/product analysis.

ClawHub Agent Skills author: rmbell09-lang v1.0.1 MIT-0 2 files body ≈ 1 764 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process D 47/100 · Unfinished process — weak spots: when it triggers, inputs and preconditions, failures and branches

AnalyzerInfrastructureWriting and documentsCommercetype 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
94/100
safety, quality, tests
Safety 60%
100
Quality 40%
85
Run on models
none yet
Process rating
D
47/100
Unfinished process
Inputs and preconditions w 11
0
Failures and branches w 10
0
Progress reporting w 2
0
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 47/100

    • 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
    • 30Running it twice. 5 mutating operations with no state check
    • 40Consistency. Frontmatter name (collaborative-research) differs from the folder (lucky-collaborative-research)
    • 60Tools and files. Uses tools (web) that frontmatter does not declare
    • 60Result and completion. Output format stated, no completion criterion
    • 100Steps. 81 steps
    • 100Execution cost. Instruction body is 1764 tokens
    • medium Safety rules and hard prohibitions inside a skill: they belong in the system prompt, here they protect nothing

    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
    • +1No license
    • +2Single-language instructions
    • +3Description length 409: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 81 items
    • +3Output format is stated explicitly
    • +4Has examples (6 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 85.

    External checks

    ClawHub: suspicious
    This research skill is understandable, but it asks agents to broadly collect webpages and move them to a hardcoded machine and analysis agent without enough user control or scoping.
    LLM: suspicious (high) · VirusTotal: · 29 May 2026