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Sif Amazon Research

Community
liangdabiao
sif-amazon-research

Use this skill for Amazon e-commerce research, market validation, competitor analysis, traffic diagnosis, keyword strategy, advertising review, launch evaluation, and growth optimization using the Sif MCP server and Sif's real marketplace, traffic, keyword, sales, and ad data. Trigger when users mention Sif, Sif MCP, ASIN research, Amazon product opportunity research, keyword opportunity, competitor teardown, traffic drop/root-cause diagnosis, advertising structure review, or whether a product/market is worth entering, launching, scaling, optimizing, or abandoning.

Overview

Publisherliangdabiao
Repositoryamazon-sorftime-research-MCP-skill
Skill namesif-amazon-research
Stars
964
Forks
138
Bundled files
3
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 3 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by liangdabiao on GitHub. Read the source before you install it.

Installation

Install the Sif Amazon Research AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/liangdabiao/amazon-sorftime-research-MCP-skill/tree/main/SKILLS/skills/sif-amazon-research
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/liangdabiao/amazon-sorftime-research-MCP-skill.git /tmp/amazon-sorftime-research-MCP-skill
mkdir -p .claude/skills
cp -r /tmp/amazon-sorftime-research-MCP-skill/SKILLS/skills/sif-amazon-research .claude/skills/sif-amazon-research
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Sif Amazon Research in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Sif Amazon Research on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Sif Amazon Research is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Sif Amazon Research

Operating rules

Use Sif MCP as the evidence source whenever available. Do not infer final business conclusions from one metric alone.

Before calling tools, identify the task type:

  • Market/opportunity research: keyword or category direction.
  • Market validation: keyword + candidate ASIN viability.
  • Competitor analysis: competitor ASIN growth path and traffic source.
  • Keyword layout: main terms, long tails, root structure, missing terms.
  • Launch evaluation: whether to push a product.
  • Post-launch feedback: continue, optimize, or stop.
  • Growth optimization: expand proven traffic and keywords.
  • Traffic/ad diagnosis: root cause of traffic or ad performance changes.

If the request does not include enough identifiers, ask for the missing ASIN, keywords, country, and time window. Default country to US only when the user does not specify a marketplace.

Core workflow

  1. Define the decision being made: enter / not enter, launch / not launch, scale / optimize / stop, or diagnose root cause.
  2. Collect Sif evidence across the minimum necessary domains:
    • Market demand: keyword demand, history, root trend.
    • Competition: keyword competition, top ASIN concentration, competitor keyword signals.
    • Traffic: ASIN/listing traffic trend, structure, keyword distribution.
    • Sales: sales list/trend, variant performance.
    • Ads: ASIN ad structure, ad trend, campaign contribution, campaign/ad group drill-down.
  3. Cross-check evidence. Separate confirmed findings from hypotheses.
  4. Produce a business decision with confidence, evidence chain, risks, missing data, and next validation actions.

For scenario-specific tool sequences, read references/research-playbooks.md. For tool selection and parameter conventions, read references/sif-tool-map.md. For real-world MCP response quirks, large-output handling, and field interpretation pitfalls, read references/field-notes.md before any full analysis or debugging.

Required output format

For every Sif-based research answer, include:

  1. Decision / conclusion: the direct answer and confidence level.
  2. Problem layer: market demand, competition, traffic, ads, product/listing, or mixed.
  3. Evidence chain: concise bullets linking each claim to Sif data returned by tools.
  4. Business impact: what the evidence means for sales, traffic, ranking, or launch risk.
  5. Priority actions: the top 3 actions, ordered by expected impact and speed.
  6. Do not do yet: actions blocked by missing or weak evidence.
  7. Missing data: 3 data gaps and which conclusion each gap affects.
  8. 7-day validation plan: measurable checks the user can run quickly.

If data is incomplete, say what current data supports, what is missing, and whether the recommendation is only a hypothesis or execution-ready.

Guardrails

  • Do not conclude from ACOS alone, CTR alone, or sales change alone.
  • Do not output a final plan when key Sif data is unavailable; label it as a hypothesis.
  • Do not treat analyze_traffic_anomaly as a generic raw-data tool. Use it only for explicit traffic anomaly/root-cause diagnosis or when the user explicitly asks to diagnose traffic.
  • Preserve any render_footer returned by Sif tools verbatim at the end of the reply.
  • Respect Sif time conventions: week values must be Sunday dates; current week data may be delayed, so use latelyDay=7 for current-week-style checks.
  • Do not expose or repeat MCP secret keys.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Sif Amazon Research AI skill do?

Use this skill for Amazon e-commerce research, market validation, competitor analysis, traffic diagnosis, keyword strategy, advertising review, launch evaluation, and growth optimization using the Sif MCP server and Sif's real marketplace, traffic, keyword, sales, and ad data. Trigger when users mention Sif, Sif MCP, ASIN research, Amazon product opportunity research, keyword opportunity, competitor teardown, traffic drop/root-cause diagnosis, advertising structure review, or whether a product/market is worth entering, launching, scaling, optimizing, or abandoning.

Why use Sif Amazon Research on TypingMind?

Because you install it once and use it with any model. Sif Amazon Research is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Sif Amazon Research in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/liangdabiao/amazon-sorftime-research-MCP-skill/tree/main/SKILLS/skills/sif-amazon-research. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Sif Amazon Research?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Sif Amazon Research?

As many as you like. As long as a model supports skills, you can use Sif Amazon Research with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Sif Amazon Research AI skill free?

It is published on GitHub by liangdabiao. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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