--- name: ferdinandobons/startup-competitors source: https://app.decimal.ai/s/ferdinandobons-startup-competitors@1/SKILL.md source_sha256: cb2c6781b592 --- # 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. **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 (why their customers leave) ### Raw Data Keep raw research files in `{project-name}/raw/` for reference: - `competitor-profiles.md` - `pricing-intelligence.md` - `review-mining.md` - `forum-mining.md` - `gtm-analysis.md` - `strategic-signals.md` --- ## Phase 3.5: Research Verification After synthesis completes and all deliverable files are written, run a verification pass. > **Reference:** Read `references/verification-agent.md` for the full verification protocol, universal checks, and skill-specific checks. ### Process 1. 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 2. V1 also runs startup-competitors-specific checks: battle card vs. report consistency, matrix vs. profiles alignment, pricing landscape vs. profiles consistency, cross-deliverable coherence 3. V1 produces `{project-name}/verification-report.md` 4. **If Critical issues found:** Pause and present issues to the user. Ask: fix first, or proceed as-is? 5. **If only Warnings/Info:** Show one-line summary In Claude.ai or when Agent tool is unavailable, run the verification checks yourself in the main conversation following the same protocol. --- ## Honesty Protocol > **Reference:** Read `references/honesty-protocol.md` for full protocol and anti-pattern details. Competitive intelligence is only useful if it's honest. Core rules apply (label claims, quantify, declare gaps), plus competitive-intelligence-specific additions: 1. **No cheerleading.** If a competitor is objectively better at something, say so. Battle cards that ignore competitor strengths are useless in real sales conversations. 2. **Label claims.** Use **[Data]**, **[Estimate]**, **[Assumption]**, **[Opinion]** tags. Never present guesses as facts. 3. **Quantify.** "$12M ARR growing 40% YoY" not "they're growing fast." 4. **Date everything.** Flag data older than 12 months. 5. **Declare gaps.** "DATA GAP: Could not find reliable data on [X]" is always better than fabrication. 6. **Surface red flags.** If the competitive landscape looks brutal, say so directly. 7. **Challenge confirmation bias.** When research confirms what the founder already believes, probe deeper. Look for disconfirming evidence. See `references/honesty-protocol.md` for the full anti-pattern table (6 entries) and detailed protocol. --- ## Reference Files Read only what you need for the current phase. | File | When to Read | ~Lines | Purpose | |------|-------------|--------|---------| | `honesty-protocol.md` | Start of session | ~72 | Full honesty protocol with anti-patterns | | `research-principles.md` | Before starting Phase 2 | ~54 | Source quality, cross-referencing, data gaps | | `research-wave-1-profiles-pricing.md` | When running Wave 1 | ~186 | Agent templates for profiles + pricing | | `research-wave-2-sentiment-mining.md` | When running Wave 2 | ~189 | Agent templates for review + forum mining | | `research-wave-3-gtm-signals.md` | When running Wave 3 | ~192 | Agent templates for GTM + strategic signals | | `research-synthesis.md` | After all waves complete | ~231 | How to synthesize + battle card template | | `research-scaling.md` | After intake, before Phase 2 | ~106 | Complexity scoring, tier definitions, wave configurations | | `verification-agent.md` | After synthesis | ~126 | Verification protocol, universal + skill-specific checks |