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AC launchfast-full-research-loop

Complete Amazon FBA product research pipeline using the LaunchFast MCP. Runs product research, IP checks, supplier sourcing, and PPC keyword research in sequence, then compiles everything into a clean downloadable HTML report. USE THIS SKILL FOR: - "full research on [keyword]" - "research everything about [product]" - "give me a complete FBA opportunity report" - "run the full loop on [keyword]" Requirements: - mcp__launchfast__research_products - mcp__launchfast__ip_check_manage - mcp__launchfast__supplier_research - mcp__launchfast__amazon_keyword_research

ClawHub Agent Skills author: BlockchainHB v1.0.0 2 files body ≈ 4 350 tokens Open the sourceclawhub.ai↗ analyzed 3 d ago

As a process C 57/100 · Has gaps — weak spots: result and completion, when it triggers, inputs and preconditions

ProcedureInfrastructureData and analyticsAI and agentstype 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
96/100
safety, quality, tests
Safety 60%
100
Quality 40%
89
Run on models
none yet
Process rating
C
57/100
Has gaps
Result and completion w 14
0
Inputs and preconditions w 11
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 57/100

    • 0Result and completion. Does not say what the result is
    • 0Inputs and preconditions. Does not say what the process needs to start
    • 0Progress reporting. Says nothing while it works
    • 20When it triggers. No condition that starts the skill
    • 55Failures and branches. 1 branches
    • 70Execution cost. Instruction body is 4350 tokens
    • 100Tools and files. No external tools needed
    • 100Steps. 36 steps
    • 100Consistency. Name and required fields are in place
    • 100Running it twice. No mutating operations
    • low 17 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

    • +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
    • +5Description quotes 4 example trigger phrases
    • +3Description length 566: enough signal without eating the budget
    • +4Structure: 20 headings
    • +3Step-by-step instructions: 36 items
    • +4Has examples (8 code blocks)

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 89.

    External checks

    ClawHub: clean
    This skill appears to be a normal product-research workflow, but users should be aware it sends business research inputs to external services and saves an HTML report locally.
    LLM: benign (medium) · VirusTotal: suspicious · 28 May 2026