Deep competitive intelligence for any market. Analyzes competitors' products, pricing, customer sentiment, GTM strategy, and growth signals using real web data. Produces battle cards, pricing landscape, and feature matrix. Use when the user wants to understand their competitive landscape, analyze co

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 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. C2: Strategic & Growth Signals — Funding trajectory (rounds, investors, timing), hiring patterns (engineering heavy = building, sales heavy = scaling, support heavy = struggling), content/SEO footprint (what keywords they rank for, where the gaps are), product roadmap signals from changelogs and public statements. Identify content pillars each competitor owns and which topics nobody covers well. Post Research Checkpoint After all three waves complete, before synthesis, briefly present what the research found to the user: how many competitors were profiled, the top customer pain themes, the most notable strategic signals (funding, hiring, GTM patterns). Ask: "Does this align with your expectations? Any competitors to add or remove before I synthesize?" Keep it to one message — this is a quick alignment check, not a full report. Phase 3: Synthesis Reference: Read references/research synthesis.md for synthesis protocol and battle card template. After the checkpoint, synthesize raw findings into strategic deliverables. This step creates the real value — it's not reporting, it's pattern matching across data sources. How to Synthesize Synthesis is where raw competitor data becomes strategy — it's reasoning, not formatting. Before writing, think hard about how the findings interlock: a pricing gap means little until you connect it to a recurring customer complaint and a hiring signal. This is the highest leverage thinking in the analysis, so if the model supports extended thinking, spend it here. Then work through these steps deliberately: 1. Read all raw files before writing anything 2. Connect findings across waves: pricing gaps + customer complaints + hiring signals = strategic opportunities 3. Identify contradictions between sources and explain which to trust 4. Rate confidence for each major claim (High / Medium / Low) 5. Surface strategic implications — not just facts, but what they mean 6. Aggregate all data gaps from raw files into a dedicated "Data Gaps & Research Limitations" section in the competitors report — every analysis has blind spots, and being explicit about them prevents false confidence 7. Include adjacent solutions (broader platforms, manual alternatives, tools from neighboring categories) — customers don't just choose between direct competitors, they choose between "good enough" options from adjacent spaces Output Files Every deliverable file must start with a standardized header: {Title}: {product} followed by Skill: startup competitors Generated: {date} . Every deliverable must end with Red Flags, Yellow Flags, and Sources sections. {project name}/competitors report.md — The main deliverable: Executive summary (5 sentence competitive landscape overview) Market concentration assessment (fragmented / consolidating / dominated) Key findings per research dimension Strategic opportunities (where to compete) Strategic risks (where to avoid) Competitive moat assessment (network effects, switching costs, data moat, brand, scale) Data gaps & research limitations (mandatory — aggregate from all raw files) Red flags and yellow flags {project name}/competitive matrix.md — Feature comparison table: Features as rows, competitors as columns Rating: strong / adequate / weak / missing Highlight gaps where no competitor serves well Your product included (or placeholder if pre launch) {project name}/pricing landscape.md — Dedicated pricing analysis: Tier by tier comparison across all competitors Value metric analysis (what each charges for and why) Pricing psychology breakdown (anchoring, decoy, freemium strategies) Price positioning map (axes: price vs. feature depth) Pricing whitespace — where there's room to position Switching cost matrix (per competitor: technical, contractual, emotional) {project name}/battle cards/{competitor name}.md — One per competitor: One page format: who they are, their strengths, their weaknesses How to win against them (specific talking points) When they win over you (be honest) Customer objections and responses Key vulnerability to exploit Churn signals (

startup-competitors