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AD Amazon Product Research & Seller Analytics

Amazon product research and seller analytics for FBA and FBM businesses. Find winning products with 14 selection strategies, track competitors, monitor BSR trends, analyze reviews, estimate monthly sales, optimize listings, and assess market opportunities. Real-time ASIN lookup with 200M+ product database. Amazon seller tools, niche research, keyword analysis, pricing strategy, and category insights powered by APIClaw API. Use when user asks about: Amazon product selection, finding products to sell, ASIN lookup, BSR analysis, competitor tracking, market opportunity, risk assessment, FBA research, review analysis, or listing optimization. Requires APICLAW_API_KEY.

LeoYeAI/openclaw-master-skills Agent Skills author: LeoYeAI MIT 12 files body ≈ 4 987 tokens Open the sourcegithub.com↗ analyzed 2 d ago

Amazon product research and seller analytics for FBA and FBM businesses.

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

IntegrationData and analyticstype 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
93/100
safety, quality, tests
Safety 60%
100
Quality 40%
83
Run on models
none yet
Process rating
D
47/100
Unfinished process
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
This is a copy of a skill from another catalog; the rating counts the canonical one: Amazon Product Research & Seller Analytics (LeoYeAI/openclaw-master-skills)

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: 12. Evidence is masked. Grey chips explain why severity was lowered.

    Against the Agent Skills spec

    • warning name-format name should be kebab-case (lowercase letters, digits, hyphens)
    • note frontmatter-key unknown frontmatter key "homepage"

    Process rating: all ten parameters 47/100

    • 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
    • 30Running it twice. 1 mutating operations with no state check
    • 40Consistency. Frontmatter name (Amazon Product Research & Seller Analytics) differs from the folder (apiclaw-analysis)
    • 60Tools and files. Uses tools (web, python) that frontmatter does not declare
    • 70Execution cost. Instruction body is 4987 tokens
    • 100Steps. 38 steps
    • 100Failures and branches. 2 branches, has a failure section
    • low 14 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
    • -268 emoji in the instructions: noise for the model
    • +1No license
    • +2Single-language instructions
    • +3Description length 671: enough signal without eating the budget
    • +4Structure: 30 headings
    • +3Step-by-step instructions: 38 items
    • +4Has examples (12 code blocks)
    • +4Reference files are cited in the instructions (7 of 7)
    • +3All 1 scripts are documented

    Quality base 70; lint remarks subtract, signals add up to 100. Result: 83.