Amazon Keyword Traffic Analysis logo

Amazon Keyword Traffic Analysis

Organization
SerendipityOneInc
amazon-keyword-traffic-analysis

Analyze Amazon keyword value and product traffic health through keyword demand, market structure, weekly trends, observed SERP signals, ASIN traffic structure, traffic terms, changes, and named-term timelines. Use for keyword expansion, keyword deep dives, traffic-term value analysis, and ASIN-centered product traffic analysis or diagnosis. Produces evidence-bounded validation priorities; does not make direct bid, budget, pause, or negative-keyword decisions without seller ABA-SQP and Amazon Ads data. Requires ZOODATA_API_KEY.

Overview

PublisherSerendipityOneInc
RepositoryZooData-Skills
Skill nameamazon-keyword-traffic-analysis
Stars
78
Forks
12
Bundled files
18
LicenseMIT
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.

  • 18 bundled files

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

  • Open source

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

Installation

Install the Amazon Keyword Traffic Analysis 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/SerendipityOneInc/ZooData-Skills/tree/main/amazon-keyword-traffic-analysis
  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/SerendipityOneInc/ZooData-Skills.git /tmp/ZooData-Skills
mkdir -p .claude/skills
cp -r /tmp/ZooData-Skills/amazon-keyword-traffic-analysis .claude/skills/amazon-keyword-traffic-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Amazon Keyword Traffic Analysis 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 Amazon Keyword Traffic Analysis 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 Amazon Keyword Traffic Analysis 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.

ZooData — Amazon Keyword Intelligence

Respond in the user's language.

Start here

  1. Read and apply references/analysis-contract.md before classifying the request, making an evidence call, or sending user-facing text.
  2. Classify the request: seed-keyword expansion, target-keyword analysis, product traffic analysis, or a single lookup.
    • Route keyword-centered questions about demand, market/SERP structure, trend, value, relevance, or targeting fit to target-keyword analysis. An ASIN may be supporting evidence without changing the keyword-centered subject.
    • Route ASIN-centered questions about traffic health, current traffic terms or sources, channel/term structure, changes, trends, anomalies, or causes to product traffic analysis.
    • Route a broad ASIN traffic analysis, overview, or health check directly to the product traffic health overview. Do not ask the user to choose between structure and change first.
    • For an ASIN × keyword request, route value/fit/relevance questions without movement or causal intent to target-keyword analysis; route visibility, placement, exposure, movement, anomaly, or causal questions to product traffic analysis.
    • If product traffic analysis identifies a term and the follow-up asks about its value, start target-keyword analysis and reuse compatible ASIN traffic evidence. If keyword analysis identifies a product-side movement question, start product traffic analysis and reuse compatible keyword evidence.
  3. Read the local references/cli-contract.md, references/reference.md, and the relevant zoodata.py --help before selecting a tool. The shared contract owns CLI invocation and result handling; reference.md is the sole source for production endpoint availability, parameters, response fields, dates, batching, credits, and API capability boundaries.
  4. Load references/output-rules.md for domain rendering and apply execution-guide.md § Final Output Gate on every rendering path. For a single lookup, also load only references/execution-guide.md sections Authority and routing, Execution mode, Structured Field Identity Gate, Interface Failure Stop Gate, Final Output Gate, HTTP Validation Rule, and Credential and Credit Failures; use output-rules.md § Quick Mode Output and do not load a scenario unless the follow-up broadens the request.
  5. For every full-mode request, load the complete references/execution-guide.md, references/evidence-protocols.md, and the applicable scenario guide below. The guide owns the keyword scenario/stage and domain Gate contract; evidence protocols operate only inside its active stage. After every retrieval or tool result, apply its Interface Failure Stop Gate before selecting any next capability or command. Route to one applicable scenario, or multiple non-exclusive scenarios only when the guide permits combination:
    • references/scenarios-expand.md
    • references/scenarios-keyword-analysis.md
    • references/scenarios-product-traffic-analysis.md
  6. For a causal, anomaly, or action question, additionally load references/diagnosis-action-protocols.md. Do not load it for a non-diagnostic stage merely because diagnosis is available.
  7. After API retrieval, load only the field-semantic reference needed for the returned data:
    • references/metrics-market-profile.md for market-profile
    • references/metrics-trend-profile.md for trend-profile
    • references/serp-and-rollover.md for SERP or organicRolloverRate
    • references/traffic-observation-semantics.md for traffic-term lists, traffic-term trends, ASIN traffic structure, or ASIN traffic trends
  8. Before requesting or interpreting a seller artifact, load references/sqp-field-semantics.md. Treat it as the sole acquisition and field-semantics source for user-provided ABA-SQP or Amazon Ads data.

Source-of-truth boundaries

  • This file owns only trigger classification, reference loading, scenario routing, and non-negotiable global acquisition/safety boundaries. It may point to an owner module but must not define endpoint contracts, shared workflow procedures, field semantics, or scenario-specific stage logic.
  • analysis-contract.md owns the repository-wide rule hierarchy, universal analysis principles and Gates, and user-facing implementation boundary. Every keyword module may add or narrow domain rules but must not duplicate, weaken, or override it.
  • cli-contract.md owns only the project-wide invocation form, command-identity validation, execution-environment permission handling, caller/CLI responsibilities, composite-result reuse, result acquisition, transport-status precedence, terminal-interface classification, retry ownership, and partial-result handling. It must not define skill-specific command allowlists, endpoint fields, keyword evidence meaning, scenario selection, conclusion authority, or user-facing rendering.
  • reference.md owns only production API and acquisition-surface facts: availability, request parameters, response schema, endpoint-specific status meaning, batching, dates, and billing. It may name fields and capabilities to describe their contract, but must not redefine the shared CLI contract, Agent workflow, action/output policy, business interpretation, or scenario transitions.
  • execution-guide.md owns only the keyword scenario/stage schema, stage execution and handoff, keyword-specific Gate decisions, consequences after shared CLI classification, evidence-level conclusion ceilings, and follow-up reclassification. It may reference owner modules but must not redefine the shared analysis contract, shared CLI contract, API contracts, field meanings, detailed evidence procedures, detailed diagnosis procedures, output rendering, or scenario-specific capability/stage maps.
  • evidence-protocols.md owns only shared evidence planning, retrieval, interpretation, reconciliation, coverage, continuity, comparison, and batching procedures inside an active stage. It must not select stages, define Gate outcomes, render handoff lists, or raise conclusion authority.
  • diagnosis-action-protocols.md owns only the detailed causal-diagnosis and evidence-to-action procedures inside an active stage. It must not select stages, define the Diagnostic Closure Gate result, create handoff routes, or raise conclusion authority.
  • output-rules.md owns only keyword-specific language, the local interface-failure template, the canonical full-mode report template and headings, Data Notes, and API-usage presentation within the shared user-facing boundary. It must not redefine that boundary, select stages, define Gate outcomes, change conclusion authority, or define the contents of the stage-end selection list.
  • The metric/observation semantic references (metrics-*.md, serp-and-rollover.md, and traffic-observation-semantics.md) own only documented field meaning, direction, scope, and permitted/prohibited inference. They may identify source fields/endpoints, but must not define production availability or request parameters, shared workflow policy, or scenario routing/stages.
  • sqp-field-semantics.md owns seller-artifact acquisition order, schema identity, denominator rules, field meaning, and seller-artifact output labels. It must not define ZooData API contracts or scenario-specific stage triggers and conclusions.
  • Scenario files own only scenario-specific stage entry requirements, capability selection, conclusion authority, and section-content requirements inside the canonical report template. They define evidence levels, not report headings/order, workflow-completion states, automatic progression, or mandatory traversal of every listed stage. They may reference owner-defined capabilities, fields, and gates, but must not restate, relax, replace, or create exceptions to their contracts or semantics.
  • For the documented keyword endpoints and realtime/product used by this skill, {skill_base_dir}/scripts/zoodata.py owns a fixed blind transport retry budget, preservation of the final response body, and request/transport/credit metadata. It must not assign HTTP-status meaning, choose status-specific workflow actions, or emit Agent-control instructions. Within those command paths, it also must not define field meaning, stage selection, evidence interpretation, conclusion authority, or user-facing prose/report templates; cli-contract.md owns result classification and shared invocation handling, execution-guide.md owns keyword-stage Gate consequences, and output-rules.md owns rendered prose including the local interface-failure template. The credential-only check path and opt-in endpoint probes are diagnostic utilities outside this evidence-command contract. Other commands bundled in the shared CLI remain outside this skill's responsibility map.
  • README.md is a human-facing package overview and module index only. It must not define or modify runtime routing, endpoint contracts, workflow policy, field semantics, stage transitions, or conclusion authority.
  • Cross-module references are allowed; cross-module redefinition and duplicated policy are not. When statements span modules, split API fact, shared workflow consequence, field interpretation, and scenario application into their respective owners.
  • Apply each rule from its responsible owner module above. A downstream module may narrow behavior but must not override an owner contract.

Non-negotiable boundaries

  • Require ZOODATA_API_KEY. If it is missing or rejected, follow the credential procedure in execution-guide.md; do not substitute public web data.
  • Use the bundled {skill_base_dir}/scripts/zoodata.py for documented keyword endpoints and realtime/product; the bundled manifest scripts/allowed-commands.json enforces that scope — the CLI refuses out-of-scope subcommands with a structured COMMAND_NOT_ALLOWED error before any API request. Use ZooData WebTools /search, /scrape, and /scrape-interactive only through an exposed, documented ZooData WebTools surface after inspecting its live schema.
  • Use only the acquisition routes whitelisted in reference.md. WebTools /search is permitted URL discovery; it is not products/search. Never use products/search, external browser automation, direct Amazon navigation, or non-ZooData public web search as evidence or fallback.
  • WebTools calls are read-only public-page acquisition. Never use /scrape-interactive actions to log in, submit forms, purchase, or otherwise change page or account state; use click/write/press/scroll/JavaScript actions only to render or reveal the requested public content.
  • Treat keyword inputs as Amazon search queries. Before constructing a request, load reference.md for the endpoint's marketplace, keyword-normalization, and date contract.
  • ZooData keyword data is estimated search, visibility, rank, placement, and impression evidence. It is not the seller's ABA-SQP conversion funnel. Keep product-specific value, profitability, bids, spend, budgets, pauses, negatives, and unconditional go/no-go decisions within the evidence authority defined in execution-guide.md.
  • Preserve returned status, period, subject, field scope, and uncertainty. status=empty is an observation-coverage boundary, not proof of low demand.
  • Do not report an API root cause, strategy recommendation, or undocumented metric as though the API returned it.

Execution entry

Use:

bash
python {skill_base_dir}/scripts/zoodata.py <documented-subcommand> ...

Run bare python {skill_base_dir}/scripts/zoodata.py check for credential diagnostics only. Without --endpoints or --keyword-endpoints, it makes no evidence calls; those opt-in probe flags consume credits and are outside this skill's evidence workflow.

The bundled manifest allows exactly: keyword-detail, keyword-market-profile, keyword-trend-profile, keyword-trend, keyword-extends, keyword-search-results, keyword-product-traffic-terms, product-traffic-structure-profile, product-traffic-terms-trend, product-traffic-trend, product-traffic-trend-profile, product, and the diagnostic check.

WebTools has no bundled subcommand in this skill. Use only an exposed ZooData WebTools session/callable surface as documented in reference.md.

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 Amazon Keyword Traffic Analysis AI skill do?

Analyze Amazon keyword value and product traffic health through keyword demand, market structure, weekly trends, observed SERP signals, ASIN traffic structure, traffic terms, changes, and named-term timelines. Use for keyword expansion, keyword deep dives, traffic-term value analysis, and ASIN-centered product traffic analysis or diagnosis. Produces evidence-bounded validation priorities; does not make direct bid, budget, pause, or negative-keyword decisions without seller ABA-SQP and Amazon Ads data. Requires ZOODATA_API_KEY.

Why use Amazon Keyword Traffic Analysis on TypingMind?

Because you install it once and use it with any model. Amazon Keyword Traffic Analysis 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 Amazon Keyword Traffic Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/SerendipityOneInc/ZooData-Skills/tree/main/amazon-keyword-traffic-analysis. 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 Amazon Keyword Traffic Analysis?

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 Amazon Keyword Traffic Analysis?

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

Is the Amazon Keyword Traffic Analysis AI skill free?

Yes. It is published on GitHub by SerendipityOneInc under the MIT license. 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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