{"id":"startup-competitors","name":"startup-competitors","summary":"あらゆる市場に適した深い競争情報。競合他社の製品、価格設定、顧客感情、GTM戦略、成長シグナルを実際のウェブデータを用いて分析します。","body":"# Startup Competitors\n\nDeep competitive intelligence that goes beyond surface-level profiles. Produces actionable battle cards, pricing landscape analysis, and strategic vulnerability mapping using real web data.\n\n## How It Works\n\n```\nINTAKE → RESEARCH (3 sequential waves) → SYNTHESIS → BATTLE CARDS\n```\n\nThe 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).\n\n### Language\n\nDefault output language is **English**. If the user writes in another language or explicitly requests one, use that language for all outputs instead.\n\n---\n\n## Phase 0: Resume Check\n\nBefore 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].\"\n\nIf no progress file exists — or the one found belongs to a different skill — start from Phase 1.\n\n---\n\n## Phase 1: Intake\n\nShort and focused — 1-2 rounds of questions, not an extended interview. The goal is just enough context to run targeted research.\n\n### Check for Prior startup-design Work\n\nBefore 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:\n\n- `01-discovery/competitor-landscape.md` — competitor profiles and analysis\n- `01-discovery/market-analysis.md` — market size, trends, regulatory\n- `01-discovery/target-audience.md` — customer personas, pain points\n- `00-intake/brief.md` — product description and context\n\nIf these files exist, read them and use the data as a head start:\n- Extract the product description, target market, and known competitors from the brief\n- 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)\n- Pull market size and trends from `market-analysis.md` to contextualize the competitive landscape\n- Use customer pain points from `target-audience.md` to focus the sentiment mining on what matters most\n\nTell 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.\"\n\nSkip the intake interview entirely if the startup-design files provide enough context. Go straight to research.\n\n### What to Ask (if no prior data exists)\n\n**Round 1 — The basics:**\n- What's your product/idea? (one sentence is fine)\n- What problem does it solve and for whom?\n- What market/category are you in?\n- Do you know any competitors already? (names, URLs)\n\n**Round 2 — Sharpening (only if needed):**\n- What geography/market are you targeting?\n- What's your pricing model or range?\n- What do you consider your key differentiator?\n\nDon't over-interview. If the user gives a clear description upfront, skip straight to research. The competitive analysis itself will surface what matters.\n\n### Output\n\nSave 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`).\n\nCreate `{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.\n\n---\n\n## Phase 1.5: Research Depth Assessment\n\nAfter intake, assess market complexity and present the Research Depth recommendation to the user.\n\n> **Reference:** Read `references/research-scaling.md` for the complexity scoring matrix, tier definitions, wave configurations, and the user communication template.\n\n### Process\n\n1. Score three factors from the intake: market breadth (1-3), known competitors (1-3), geographic scope (1-3)\n2. Sum the scores (range 3-9) and map to a tier: Light (3-4), Standard (5-7), Deep (8-9)\n3. Present the Research Depth table to the user (see `research-scaling.md` for the exact template)\n4. Wait for user response: **light**, **deep**, or **ok** to accept the recommendation\n5. Record the selected tier in PROGRESS.md\n\nThe 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.\n\n---\n\n## Phase 2: Research\n\nThree 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.\n\n### Environment Detection\n\nCheck if the `Agent` tool is available:\n\n- **Agent tool available (Claude Code):** Spawn all agents within each wave in parallel. This is faster.\n- **Agent tool NOT available (Claude.ai, web):** Execute research sequentially, following the same templates. Same depth, just slower.\n\n### Web Search\n\nThis 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.\n\n> **Reference:** Read `references/research-principles.md` before starting any wave. It defines source quality tiers, cross-referencing rules, and how to handle data gaps.\n\n### Wave 1: Competitor Profiles + Pricing Intelligence\n\n> **Reference:** Read `references/research-wave-1-profiles-pricing.md` for agent templates.\n\nTwo agents (or two sequential blocks):\n\n**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.\n\n**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.\n\n### Wave 2: Customer Sentiment Mining\n\n> **Reference:** Read `references/research-wave-2-sentiment-mining.md` for agent templates.\n\nTwo agents (or two sequential blocks):\n\n**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.\n\n**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.\n\n### Wave 3: GTM & Strategic Signals\n\n> **Reference:** Read `references/research-wave-3-gtm-signals.md` for agent templates.\n\nTwo agents (or two sequential blocks):\n\n**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.\n\n**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.\n\n---\n\n### Post-Research Checkpoint\n\nAfter 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?\"\n\nKeep it to one message — this is a quick alignment check, not a full report.\n\n---\n\n## Phase 3: Synthesis\n\n> **Reference:** Read `references/research-synthesis.md` for synthesis protocol and battle card template.\n\nAfter 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.\n\n### How to Synthesize\n\nSynthesis 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:\n\n1. Read all raw files before writing anything\n2. Connect findings across waves: pricing gaps + customer complaints + hiring signals = strategic opportunities\n3. Identify contradictions between sources and explain which to trust\n4. Rate confidence for each major claim (High / Medium / Low)\n5. Surface strategic implications — not just facts, but what they mean\n6. 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\n7. 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\n\n### Output Files\n\nEvery 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.\n\n**`{project-name}/competitors-report.md`** — The main deliverable:\n- Executive summary (5-sentence competitive landscape overview)\n- Market concentration assessment (fragmented / consolidating / dominated)\n- Key findings per research dimension\n- Strategic opportunities (where to compete)\n- Strategic risks (where to avoid)\n- Competitive moat assessment (network effects, switching costs, data moat, brand, scale)\n- Data gaps & research limitations (mandatory — aggregate from all raw files)\n- Red flags and yellow flags\n\n**`{project-name}/competitive-matrix.md`** — Feature comparison table:\n- Features as rows, competitors as columns\n- Rating: strong / adequate / weak / missing\n- Highlight gaps where no competitor serves well\n- Your product included (or placeholder if pre-launch)\n\n**`{project-name}/pricing-landscape.md`** — Dedicated pricing analysis:\n- Tier-by-tier comparison across all competitors\n- Value metric analysis (what each charges for and why)\n- Pricing psychology breakdown (anchoring, decoy, freemium strategies)\n- Price positioning map (axes: price vs. feature depth)\n- Pricing whitespace — where there's room to position\n- Switching cost matrix (per competitor: technical, contractual, emotional)\n\n**`{project-name}/battle-cards/{competitor-name}.md`** — One per competitor:\n- One-page format: who they are, their strengths, their weaknesses\n- How to win against them (specific talking points)\n- When they win over you (be honest)\n- Customer objections and responses\n- Key vulnerability to exploit\n- Churn signals (why their customers leave)\n\n### Raw Data\n\nKeep raw research files in `{project-name}/raw/` for reference:\n- `competitor-profiles.md`\n- `pricing-intelligence.md`\n- `review-mining.md`\n- `forum-mining.md`\n- `gtm-analysis.md`\n- `strategic-signals.md`\n\n---\n\n## Phase 3.5: Research Verification\n\nAfter synthesis completes and all deliverable files are written, run a verification pass.\n\n> **Reference:** Read `references/verification-agent.md` for the full verification protocol, universal checks, and skill-specific checks.\n\n### Process\n\n1. Spawn agent **V1: Verification** — it reads all deliverable files and checks for: unlabeled claims, internal contradictions, confidence rating consistency, missing data gaps, missing flags, stale data, and duplicate-source false corroboration\n2. V1 also runs startup-competitors-specific checks: battle card vs. report consistency, matrix vs. profiles alignment, pricing landscape vs. profiles consistency, cross-deliverable coherence\n3. V1 produces `{project-name}/verification-report.md`\n4. **If Critical issues found:** Pause and present issues to the user. Ask: fix first, or proceed as-is?\n5. **If only Warnings/Info:** Show one-line summary\n\nIn Claude.ai or when Agent tool is unavailable, run the verification checks yourself in the main conversation following the same protocol.\n\n---\n\n## Honesty Protocol\n\n> **Reference:** Read `references/honesty-protocol.md` for full protocol and anti-pattern details.\n\nCompetitive intelligence is only useful if it's honest. Core rules apply (label claims, quantify, declare gaps), plus competitive-intelligence-specific additions:\n\n1. **No cheerleading.** If a competitor is objectively better at something, say so. Battle cards that ignore competitor strengths are useless in real sales conversations.\n2. **Label claims.** Use **[Data]**, **[Estimate]**, **[Assumption]**, **[Opinion]** tags. Never present guesses as facts.\n3. **Quantify.** \"$12M ARR growing 40% YoY\" not \"they're growing fast.\"\n4. **Date everything.** Flag data older than 12 months.\n5. **Declare gaps.** \"DATA GAP: Could not find reliable data on [X]\" is always better than fabrication.\n6. **Surface red flags.** If the competitive landscape looks brutal, say so directly.\n7. **Challenge confirmation bias.** When research confirms what the founder already believes, probe deeper. Look for disconfirming evidence.\n\nSee `references/honesty-protocol.md` for the full anti-pattern table (6 entries) and detailed protocol.\n\n---\n\n## Reference Files\n\nRead only what you need for the current phase.\n\n| File | When to Read | ~Lines | Purpose |\n|------|-------------|--------|---------|\n| `honesty-protocol.md` | Start of session | ~72 | Full honesty protocol with anti-patterns |\n| `research-principles.md` | Before starting Phase 2 | ~54 | Source quality, cross-referencing, data gaps |\n| `research-wave-1-profiles-pricing.md` | When running Wave 1 | ~186 | Agent templates for profiles + pricing |\n| `research-wave-2-sentiment-mining.md` | When running Wave 2 | ~189 | Agent templates for review + forum mining |\n| `research-wave-3-gtm-signals.md` | When running Wave 3 | ~192 | Agent templates for GTM + strategic signals |\n| `research-synthesis.md` | After all waves complete | ~231 | How to synthesize + battle card template |\n| `research-scaling.md` | After intake, before Phase 2 | ~106 | Complexity scoring, tier definitions, wave configurations |\n| `verification-agent.md` | After synthesis | ~126 | Verification protocol, universal + skill-specific checks |","author":"@ferdinandobons","ownerProfile":null,"authorContacts":null,"sourceUrl":"https://github.com/ferdinandobons/startup-skill/tree/main/startup-competitors","license":"MIT","category":"document","lang":"en","tokens":3421,"stars":0,"calls30d":2,"claimed":false,"visibility":"public","origin":"crawler","version":"0.1.0","createdAt":"2026-08-22","updatedAt":"2026-08-22","files":[{"path":"references/honesty-protocol.md","size":4949,"sha256":"fe47fa66d838c3f5cd04f765460c43a73561dd57ade84e21f05d833aa2d46ea7"},{"path":"references/research-principles.md","size":2860,"sha256":"0d34a299967ff8049700a88456e09fc40671e1d7d73e315c4ba7555848e2524d"},{"path":"references/research-scaling.md","size":4426,"sha256":"83b5d5577a059c95f1c04dda139f11a051b8b5c552b2f63d7b489472928d3094"},{"path":"references/research-synthesis.md","size":8745,"sha256":"599944d0ed3da51bf77fd581ba5c67ea8eb4348a9adfcdf0bc73059a084281dc"},{"path":"references/research-wave-1-profiles-pricing.md","size":6976,"sha256":"fee1268fc08090c1c91ca71a2eb82d5fe96960de48c03b11572147e5851ff8ae"},{"path":"references/research-wave-2-sentiment-mining.md","size":6976,"sha256":"d577408e76e8918abf97571b0330f2ac12295d9690ed11c984149d5d0b8c67db"},{"path":"references/research-wave-3-gtm-signals.md","size":7327,"sha256":"91c71f37daf27c66a439b144562be9d91c300905c49c46cc9ef60ab839af2f63"},{"path":"references/verification-agent.md","size":5249,"sha256":"0718bbd720c3602adff9ef9740d57dd6db6570186fb444813cd9028b01727011"}],"requires":{"mcp":[],"tools":[]},"safety":{"flags":[],"scannedAt":"2026-08-22","hasScripts":false,"networkEndpoints":[]}}