Nerissa-xue/nerissa-competitive-research

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# nerissa-competitive-research > **Stop letting AI hallucinate competitor reports.** A disciplined Claude Skill that refuses lazy shortcuts — every funding number must have 2 sources, every "they signed N customers" must be queryable, every "company X is similar to us" must be scored on 6 dimensions before being accepted. [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](LICENSE) [![Claude Skill](https://img.shields.io/badge/Claude-Skill-orange.svg)](https://docs.claude.com/en/docs/agents-and-tools/agent-skills/overview) --- ## The Problem When you ask AI to research a competitor, you usually get one of these bad outputs: 1. **Funding numbers pulled from one secondhand blog** — and the blog was wrong 2. **"They're an AI X startup like us"** — without ever opening their website 3. **A description of "what they do"** — never asking "what is the essence, packaging-layer or real tech?" 4. **A recommendation to "learn from them"** — based on $400M ARR, ignoring that their customer layer doesn't match yours at all 5. **Inherited from an old DD report** — never re-validated, so old errors propagate forever Each of these is a real failure pattern I hit while doing competitive research for an early-stage startup. After 12 companies and many corrections, I extracted the discipline into this skill. ## What This Skill Does Differently It refuses the shortcuts. Specifically: - **Every funding claim requires ≥ 2 independent sources** (Crunchbase + official PR; or LATKA + Rewardful case study) - **Every "X signed Y customers" requires querying your source-of-truth database** — not memory, not last week's report - **Every company gets decomposed**: technology / packaging-layer / sales motion / brand / network effect — not just "what it does" - **Every recommendation requires 6-dimension scoring** — funding size alone never qualifies - **Dual-track separation**: fundraising-reference (Track A · $10M+ similar direction) vs business-learning-target (Track B · path similarity, ignore funding). Mixing them causes errors. - **Inherited historical judgments must be explicitly tagged** — "I didn't re-validate" is safer than "looks fine" ## Quick Start ### Use as a Claude Code Skill 1. Clone this repo into your Claude skills directory: ```bash mkdir -p ~/.claude/skills cd ~/.claude/skills git clone https://github.com/xuenerissa-png/nerissa-competitive-research.git ``` 2. In any Claude Code session, trigger by saying: - `"research [Company Name] using the competitive research skill"` - `"调研 [公司名]"` (Chinese) - `"add [Company Name] to the competitor table — full SOP"` 3. Claude will execute the 7-step SOP, score on 6 dimensions, and produce the 15-field structured output. ### Use without Claude (as a manual SOP) The `SKILL.md` is also a fully readable SOP document. Print it, run a research session manually, fill the 15 fields, and add to your own tracking database (Notion / Airtable / Feishu Base / etc). A ready-to-use Feishu Base schema script is in `references/feishu-base-schema.md`. ## Output Format For each company researched, you get: ``` 🏢 Company: [Name] 🔗 Website: [verified open] 💰 Funding: [≥ 2 sources cited] 🧱 Essence: [technology / packaging-layer / sales / brand / network] 👥 Customer overlap with you: [verified, not inferred] 📊 6-dimension score: X / 12 🎯 One-line "what to copy": ... 📖 3 must-see pages: ... 🚫 Anti-lazy flags caught: ... ``` Plus the 15-field structured record ready to drop into a database. ## The 7 Steps (Summary) 1. **Open the official website** (30s) — verify it exists, not just an aggregator blog mention 2. **Find funding data** (5min) — Crunchbase + official PR + ≥ 2 sources 3. **Decompose essence** (10min) — 5 questions about what it really is 4. **Verify customer overlap** (10min) — case studies + Twitter / G2 reviews 5. **Score on 6 dimensions** (5min) — only 8+ qualifies for Top 6. **Produce 3 sentences** (15min) — what it is / what to copy / 3 must-see pages 7. **Enter structured database** (5min) — 15 fields, verifiable sources Total: 30-60 minutes per company ## Examples 5 worked examples in `examples/` (sanitized from real research): - **01 · Bootstrapped SaaS via LATKA** — A B2B attribution SaaS was listed in an old DD as "$500k seed." LATKA + an affiliate platform case + official "About" page cross-verified: $0 funding, fully bootstrapped, $770k ARR with 7 people. The wrong number propagated for weeks before correction. - **02 · Funding wave packaging layer** — A hot AI app-builder ($400M ARR, $6.6B valuation). Original take: "AI full-stack builder, must learn from." Essence decomposition revealed: it's a packaging layer over commodity infrastructure (UI-gen tool + BaaS + edge cloud) with a conversational interface. No new technology. Conclusion changed from "must learn" to "wide moat (scale/capital/brand) — technically anyone can copy, but they out-spent everyone on onboarding UX." - **03 · Direct local-market competitor** — Found via a tech-media report mentioning an ex-unicorn COO founding a vertical AI agent for outbound sales. Multi-source verified: founder background (international payments → unicorn distribution), tier-1 VC Pre-A funding, humanized "named AI employees" product framing. A much closer mirror to the original team's customer profile than the US enterprise giants they had been benchmarking against. - **04 · Incumbent warning** — A major platform launches a free AI agent for its ecosystem users. Not a direct competitor. But entry of a giant with free + ecosystem monetization is a structural threat to paid service models in adjacent space. Flagged for monthly monitoring, not deep research. - **05 · Dual-track example** — An enterprise AI agent giant ($1.5B funding, $15B valuation, Fortune 50 customers) vs a bootstrapped peer ($0 funding, 7 people, $770k ARR). Both relevant — but for different uses. The giant is Track A (fundraising-reference: "we're a vertical version of their outcome model"). The bootstrapped peer is Track B (business-learning: "their path is what we can actually replicate"). Confusing the two initially killed the bootstrapped peer from the recommendation list. ## Why This Exists I built this skill while doing real competitive research for an early-stage growth startup. Over a week of research, I caught myself (and Claude) making the same lazy mistakes repeatedly: - Re-quoting funding numbers from old DD reports without re-checking - Calling companies "competitors" without opening their website - Recommending a hot AI app-builder as a learning target without realizing it's a packaging-layer with a moat we can't access - Saying "we signed 3 customers" in 5 reports when source-of-truth actually showed 0 signed, 2 in quote stage, 9 in conversation - Defaulting to a specific industry vertical based on 1 sample company, when actually only 40% of the pipeline matched that industry Each correction pointed to a rule. This skill captures all of them. ## Comparison to Other Approaches | Approach | What it gives you | What it misses | |---|---|---| | **One-shot AI query** ("research X company") | Quick narrative | Often inherits wrong data from one source; no verification | | **Generic competitor template** (Notion / Airtable) | Structure | No discipline against lazy filling; no essence decomposition | | **VC pitch deck competitor slide** | Visual at-a-glance | Tends to compare on dimensions that flatter you; not honest | | **nerissa-competitive-research** | All of the above + forced verification + dual-track + essence decomposition + anti-lazy checklist | More work upfront (30-60 min per company) | If you do < 3 competitor researches a year, you probably don't need this. If you do 10+, the discipline pays back fast. ## Roadmap - [x] v1.0 · Core SOP + 6-dimension scoring + 15-field schema + 5 examples - [ ] v1.1 · Add Notion / Airtable schema templates (currently only Feishu Base) - [ ] v1.2 · Add automated Crunchbase + LATKA + G2 multi-source fetcher (when reliable scrapers available) - [ ] v1.3 · Add competitor monitoring agent (daily diff watcher for funding / pricing / customer wall changes) ## Related Skills - [decision-lens](https://github.com/xuenerissa-png/decision-lens) — Multi-role decision review (when "should we treat X as competitor?" itself is a complex decision) ## License MIT · Free to use, fork, adapt. ## Author Built by [@xuenerissa-png](https://github.com/xuenerissa-png). If you find this useful or want to contribute a new failure pattern → rule, file an issue. --- 中文版 README: [README_zh-CN.md](README_zh-CN.md)

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A disciplined 7-step competitive research SOP that prevents lazy AI shortcuts (sourcing from secondhand blogs, inheriting unverified judgments, confusing funding size with relevance). Forces verifiable two-source funding data, official-site visits, essence…

원문 언어: 영어

2026-06-06

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