Startup Competitors

Startup Competitors

Deep competitive intelligence that goes beyond surface-level profiles. Produces actionable battle cards, pricing landscape analysis, and strategic vulnerability mapping using real web data.

How It Works

INTAKE → RESEARCH (3 sequential waves) → SYNTHESIS → BATTLE CARDS

The process is focused: understand the product, research competitors deeply across 3 dimensions, synthesize findings, and produce actionable output. Typical runtime: 15-25 minutes in Claude Code (parallel agents), 30-45 minutes in Claude.ai (sequential).

Language

Default output language is English. If the user writes in another language or explicitly requests one, use that language for all outputs instead.

Phase 0: Resume Check

Before anything else, check if a PROGRESS.md created by this skill exists in the working directory or a project subdirectory (the skill name field says startup-competitors). If it does, read it and resume from the last incomplete phase. Tell the user: "I found progress from a previous session. You completed [phases]. Picking up from [next phase]."

If no progress file exists — or the one found belongs to a different skill — start from Phase 1.

Phase 1: Intake

Short and focused — 1-2 rounds of questions, not an extended interview. The goal is just enough context to run targeted research.

Check for Prior startup-design Work

Before asking questions, check if a startup-design session has already been completed for this project. Look for these files in the working directory or subdirectories:

• 01-discovery/competitor-landscape.md — competitor profiles and analysis
• 01-discovery/market-analysis.md — market size, trends, regulatory
• 01-discovery/target-audience.md — customer personas, pain points
• 00-intake/brief.md — product description and context

If these files exist, read them and use the data as a head start:

• Extract the product description, target market, and known competitors from the brief
• Use the competitor list from competitor-landscape.md as the starting point for deeper analysis (startup-design profiles 5-8 competitors at surface level — this skill goes much deeper on each)
• Pull market size and trends from market-analysis.md to contextualize the competitive landscape
• Use customer pain points from target-audience.md to focus the sentiment mining on what matters most

Tell the user: "I found data from a previous startup-design session. I'll use it as a starting point and go deeper on the competitive analysis."

Skip the intake interview entirely if the startup-design files provide enough context. Go straight to research.

What to Ask (if no prior data exists)

Round 1 — The basics:

• What's your product/idea? (one sentence is fine)
• What problem does it solve and for whom?
• What market/category are you in?
• Do you know any competitors already? (names, URLs)

Round 2 — Sharpening (only if needed):

• What geography/market are you targeting?
• What's your pricing model or range?
• What do you consider your key differentiator?

Don't over-interview. If the user gives a clear description upfront, skip straight to research. The competitive analysis itself will surface what matters.

Output

Save to {project-name}/intake.md — a brief summary of the product, market, and known competitors. If built on startup-design data, note the source files used. The project name should be derived from the product/market (kebab-case, e.g., ai-email-assistant).

Create {project-name}/PROGRESS.md with: project name, skill name (startup-competitors), start date, language, research mode (Live / Knowledge-Based), and a phase checklist. Update it after each phase completes. If PROGRESS.md already exists from a previous session, resume from the last incomplete phase.

Phase 1.5: Research Depth Assessment

After intake, assess market complexity and present the Research Depth recommendation to the user.

Reference: Read references/research-scaling.md for the complexity scoring matrix, tier definitions, wave configurations, and the user communication template.

Process

1. Score three factors from the intake: market breadth (1-3), known competitors (1-3), geographic scope (1-3)
2. Sum the scores (range 3-9) and map to a tier: Light (3-4), Standard (5-7), Deep (8-9)
3. Present the Research Depth table to the user (see research-scaling.md for the exact template)
4. Wait for user response: light, deep, or ok to accept the recommendation
5. Record the selected tier in PROGRESS.md

The selected tier determines the number of agents per wave and search rounds per agent in Phase 2. See research-scaling.md for exact wave configurations per tier.

Phase 2: Research

Three sequential research waves, each attacking the competitive landscape from a different angle — agents within a wave run in parallel. Together they produce a 360-degree view.

Environment Detection

Check if the Agent tool is available:

• Agent tool available (Claude Code): Spawn all agents within each wave in parallel. This is faster.
• Agent tool NOT available (Claude.ai, web): Execute research sequentially, following the same templates. Same depth, just slower.

Web Search

This skill requires WebSearch for real data. If WebSearch is unavailable or denied, fall back to Knowledge-Based Mode: use training data, mark all findings with [Knowledge-Based — verify independently], and reduce confidence ratings by one level.

Reference: Read references/research-principles.md before starting any wave. It defines source quality tiers, cross-referencing rules, and how to handle data gaps.

Wave 1: Competitor Profiles + Pricing Intelligence

Reference: Read references/research-wave-1-profiles-pricing.md for agent templates.

Two agents (or two sequential blocks):

A1: Competitor Deep-Dives — Identify and profile 5-8 direct competitors plus 2-3 adjacent solutions (broader platforms, manual alternatives, tools from neighboring categories that compete for the same budget). For each: product, features, team size, funding, traction signals, strengths, weaknesses. Go beyond their marketing page — check reviews, job postings, and funding data.

A2: Pricing Intelligence — For each competitor: reverse-engineer the pricing model. Not just "it costs $49/mo" but: what's the value metric (per seat? per usage? flat?), how do tiers differentiate, what pricing psychology do they use (anchoring, decoy, charm pricing), what's the switching cost (technical, contractual, emotional). Build a tier-by-tier comparison.

Wave 2: Customer Sentiment Mining

Reference: Read references/research-wave-2-sentiment-mining.md for agent templates.

Two agents (or two sequential blocks):

B1: Review Mining — Mine G2, Capterra, TrustRadius, Product Hunt, and App Store reviews for each competitor. Extract patterns: what do people praise? What do they complain about? What features do they request? Organize by competitor and by pain theme. Include verbatim quotes.

B2: Forum & Community Mining — Mine Reddit, Indie Hackers, Hacker News, Quora, and niche communities. Find: complaints about existing tools, "what do you use for X?" threads, migration stories, workaround discussions. Build a language map — the exact words customers use to describe their problems and desires. Identify churn signals — why people leave each competitor.

Wave 3: GTM & Strategic Signals

Reference: Read references/research-wave-3-gtm-signals.md for agent templates.

Two agents (or two sequential blocks):

C1: Go-to-Market Analysis — For each competitor: primary acquisition channel, sales motion (self-serve vs. sales-led), content strategy (blog frequency, topics, quality), social presence, paid advertising signals, partnership plays. Build a channel opportunity map showing competitor saturation vs. opportunity per channel.