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Product Competitor Analysis

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AtlasOmnia
product-competitor-analysis

product-competitor-analysis — Conduct codebase-grounded competitive assessments for iOS/macOS apps by inventorying implemented behavior, mapping user needs, extracting differentiators, identifying blockers, and producing a positioning summary with line-grounded evidence.

Overview

PublisherAtlasOmnia
Repositorydonna-starter
Skill nameproduct-competitor-analysis
Stars
131
Forks
9
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Product Competitor 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/AtlasOmnia/donna-starter/tree/main/skills/product-competitor-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/AtlasOmnia/donna-starter.git /tmp/donna-starter
mkdir -p .claude/skills
cp -r /tmp/donna-starter/skills/product-competitor-analysis .claude/skills/product-competitor-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Competitor 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 Product Competitor 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 Product Competitor 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.

Product Competitor Analysis

What this skill covers

Use this skill when asked to compare a local app/product to mainstream alternatives from local evidence only (or with explicit permission to use external research). It is intentionally structured for code-first competitive assessments where output should cite files, line ranges, and concrete implementation details.

Core principle

Every claim in the final analysis must be traceable to one of:

  • concrete local code artifacts (files + line ranges)
  • local config/docs
  • command output that is itself grounded in local files

Category separation: native integration versus standalone replacement

When comparing an in-host integration to a standalone AI application, classify the products before scoring features. Do not treat “AI edits Office files” as the category; distinguish:

  • Standalone replacement: owns its document, spreadsheet, or presentation engine; typically wins on convenience, breadth, cross-platform independence, and zero host-app requirements.
  • Native integration: operates against the live host application's object model; can win on active-document context, fidelity to existing business files, in-place workflows, and verified host mutations, but carries more installation and compatibility complexity.

State the customer segment explicitly. A native integration is not universally superior: it is materially stronger for customers already dependent on the host application and its existing templates, while a standalone replacement may be better for users seeking an Office alternative. Avoid declaring a moat from architecture alone; translate the difference into an observable before/after workflow and identify the host-acceptance, onboarding, and reliability gates required to make the advantage real.

For this comparison pattern, apply the comparison framework in this skill.

Preflight

Before analysis:

  1. Confirm target slice and language scope (e.g., iOS, macOS, CLI).
  2. Confirm whether external validation is allowed.
  • If user says “no web research” (or equivalent), perform conceptual only comparisons against mainstream categories.
  1. Capture project shape first (top-level files, app layers, active targets).
  2. Identify authoritative README(s), architecture notes, and build metadata.

Required evidence buckets

Collect evidence in these buckets and keep each bucket grounded:

  • Feature inventory

  • UI controls and state toggles

  • Runtime states/lifecycle

  • Input/output pipelines

  • Persisted settings and storage

  • User/jobs-to-features mapping

  • Map likely jobs-to-be-done to concrete paths and controls

  • Separate “implemented in code today” vs “present in docs only”

  • Differentiators

  • What the app does materially differently from baseline competitors

  • Include provider architecture, route controls, feedback loops, anti-echo handling, etc.

  • Missing table-stakes

  • Compare against mainstream competitor categories to identify must-haves absent here

  • Security / distribution blockers

  • Secrets handling, key material pathways, packaging risks

  • Permissions and entitlement impacts

  • Store/review/readiness gaps

  • UX/reliability risks

  • failure states visible to users

  • onboarding and setup friction

  • session lifecycle interruptions

  • Positioning

  • A concise 1–2 sentence positioning for store/market fit

Evidence format

For every major claim, include:

  • file path + line range
  • short phrase describing what is observed
  • confidence level (implemented, partial, docs-only, inferred)

Standard execution flow

1) Discover layout and project shape

Use local discovery only:

  • app entrypoint(s)
  • domain folders (App/Providers/Audio/Resources)
  • runtime entry config (project.yml, .xcscheme, Info.plist)

2) Inventory implementation

Extract the following types first:

  • status/state enums
  • provider protocol + implementations
  • session manager/view model
  • audio capture and routing layer
  • key settings/state persistence mechanisms

3) Produce analysis sections in fixed order

Use this section order in final response:

  1. Feature inventory
  2. Target users + use cases
  3. Differentiators
  4. Missing table-stakes features
  5. Security/distribution blockers
  6. UX risks
  7. Overall positioning
  8. Confidence/risk callouts

4) Keep comparisons constrained when requested

If user says no web research:

  • do conceptual benchmarking only
  • avoid naming external products as facts unless user supplied
  • do not cite ungrounded feature claims

5) Close with concise verdict

End with a short, non-hedged “where this ships well today” vs “what must ship before broad consumer release”.

Strong constraints

  • Do not assert unsupported metrics (latency, MAU, retention) without evidence from test artifacts.
  • Avoid inventing competitor feature details from memory.
  • Keep line-citation references consistent across the report.
  • If a required evidence bucket is empty, call that explicitly and avoid padding.

Pitfalls

  • Comparing platform-specific behavior to unrelated desktop behavior without separating targets.
  • Mixing docs and runtime evidence as equivalent.
  • Forgetting direct dependency on permissions (microphone, audio routes, background modes).
  • Ignoring production security posture and focusing only on functional parity.

Deliverable template

Feature inventory

  • [feature] – [evidence]

Use cases

  • [persona/use case] – [mapped controls/settings]

Differentiators

  • [claim] – [mechanism / path]

Missing table-stakes

  • [feature gap] – [impact + priority]

Security & distribution

  • [risk] – [where implemented or missing]

UX risks

  • [risk] – [user-facing consequence]

Positioning

  • [1–3 sentence positioning + likely store category]

Reuse assets

  • : compact checklist + session examples for quick copy-paste.

Validation checklist

  • At least 5 evidence citations across at least 2 feature domains
  • At least one distribution/security block identified if credentials are user-provided in-app
  • At least one clear non-negotiable blocker and one “nice-to-have but not blocking” item
  • No uncited claim in final report

Frequently asked questions

What does the Product Competitor Analysis AI skill do?

product-competitor-analysis — Conduct codebase-grounded competitive assessments for iOS/macOS apps by inventorying implemented behavior, mapping user needs, extracting differentiators, identifying blockers, and producing a positioning summary with line-grounded evidence.

Why use Product Competitor Analysis on TypingMind?

Because you install it once and use it with any model. Product Competitor 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 Product Competitor Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/AtlasOmnia/donna-starter/tree/main/skills/product-competitor-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Product Competitor 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 Product Competitor Analysis?

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

Is the Product Competitor Analysis AI skill free?

Yes. It is published on GitHub by AtlasOmnia 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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