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SKILL verified MIT Self-run

Product Competitor Analysis

skill-atlasomnia-donna-starter-product-competitor-analysis · by AtlasOmnia

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.

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Install

$ agentstack add skill-atlasomnia-donna-starter-product-competitor-analysis

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

View the full security report →

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Reliability & compatibility

Security review passed
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5d ago

Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

Preview Execution monitoring

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About

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

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.