# Competitor Research > Research 3–5 competitors for any product or feature. Returns positioning, pricing, key differentiators, gaps, and an unclaimed angle. Use when the user asks about competitors, market landscape, or competitive analysis. Use when this capability is needed. - Skill: `tomevault-io/competitor-research` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add tomevault-io/competitor-research` - Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/competitor-research/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Marketing & Growth - Author: tomevault-io (https://skillmd.com/u/tomevault-io) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/tomevault-io/competitor-research --- # Competitor Research You research competitors yourself for the light, fast tasks (search and homepage fetches), and you delegate the heavy work to specialist subagents: - `pricing-fetcher` — pricing extraction with WebFetch-first, Playwright-fallback - `review-miner` — distilling user sentiment from 10+ review pages > **Workshop note:** the split between "skill does it" and "subagent does it" > follows one rule: **delegate work that is heavy, parallelizable, and returns > clean structured output**. Light fetches stay in the skill. Heavy or > potentially-heavy work (Playwright at ~114K tokens, review-mining across > dozens of pages) gets its own context window. --- ## Step 1 — Scope Ask the user **once**: > "What product or feature are you researching? Who is it for?" Skip if they already gave both. Cap at **2** clarifying questions max (geography, segment, direct vs. adjacent). Don't run an interview. ## Step 2 — Identify competitors Use **`WebSearch`** by default. Look for: - "best [category] tools 2025/2026" - G2 / Capterra / Gartner category pages - Reddit "alternatives to [known leader]" threads **Use `mcp__brave-search__brave_web_search` instead only when** you need strictly the last 12 months of results (e.g., a fast-moving category where 2-year-old listicles would be misleading). Brave's `freshness` parameter is deterministic; `WebSearch`'s recency is a soft preference. For most categories the soft preference is fine. Pick **direct** competitors (same buyer, same job-to-be-done). List them back to the user briefly so they can correct the set before you go deep. ## Step 3 — Per competitor (parallel work) For each competitor, dispatch the following **in a single message with multiple tool calls** so they run concurrently: ### 3a. Homepage — you do this yourself (`WebFetch`) Fetch the homepage and extract: - Hero headline (verbatim) - Subhead (verbatim) - Top 3 features in homepage order - Stated differentiators (translated to plain English, not paraphrased into marketing-speak) This stays inline because homepage HTML is server-rendered for ~95% of competitor sites — fast, light, and pollutes nothing. ### 3b. Pricing — delegate to `pricing-fetcher` subagent Spawn one `pricing-fetcher` per competitor. It tries `WebFetch` first; falls back to Playwright browser automation only if the page is JS-rendered. **Why this is delegated:** Playwright sessions cost ~114K tokens each. Even when only 1 in 3 competitors needs it, isolating that work in subagents keeps the orchestrator's context lean and lets fetches run in parallel. The subagent returns a structured pricing table plus a `Method used` field (WebFetch / Playwright) — audit this field. If 4 of 5 competitors all used Playwright, something's wrong with WebFetch and you should investigate before trusting the data. ### 3c. Reviews — delegate to `review-miner` subagent Spawn one `review-miner` per competitor. It searches across G2/Capterra/Reddit/ HN/ProductHunt/Trustpilot, distills patterns (≥3 confirming voices across ≥2 platforms = signal), and returns recurring strengths + weaknesses. **Why this is delegated:** review pages are dense user-voice text — 10+ pages per competitor. Doing it inline pollutes the orchestrator's context with raw review snippets. If a subagent returns "insufficient data," don't retry with the same prompt — either accept the gap (and surface it in the final report) or hand the next attempt a sharper, narrower question. ## Step 4 — Synthesize per-competitor cards Combine your homepage research with the two subagents' findings into **exactly 6 bullets** per competitor: ``` ### [Competitor Name] - **Positioning**: one sentence (their words, plain English) - **Target customer**: who they're built for - **Pricing**: tiers and price points, or "Not public" - **Differentiators**: 2–3 things they do well - **Weaknesses**: 1–2 recurring complaints from reviews/forums - **Source date**: most recent source pulled (YYYY-MM) ``` Hard rules: - 6 bullets exactly. If a bullet is empty, write "—" but keep the line. - Pricing must cite a source URL inline if public. - No marketing language in your translation. ## Step 5 — Gap Analysis Add this section verbatim: ``` ### Gap Analysis - **What no competitor does well**: [specific capability gap] - **Where pricing is underserved**: [a tier or model nobody offers] - **Unclaimed positioning angle**: [a frame nobody owns] ``` Each gap must be **falsifiable** — grounded in something a reader can verify. "Better UX" is not a gap. "No competitor offers per-seat pricing under $10/mo for teams under 5" is. ## Step 6 — Close End the report with exactly one line: > **Based on this, which gap are you trying to own?** No summary. No "let me know if you want more." Just the question. --- ## Tool selection cheat sheet | Task | Default tool | Reach for MCP when... | | --- | --- | --- | | Identify competitors | `WebSearch` | You need strict 12-month recency → `brave_web_search` | | Fetch a homepage | `WebFetch` | (never — homepage is light) | | Fetch a pricing page | `pricing-fetcher` subagent | (subagent decides internally whether Playwright is needed) | | Mine reviews | `review-miner` subagent | (subagent decides internally — uses Brave's news/summarizer for specific cases) | **Default principle:** built-in tools first, MCPs only when they offer something built-ins can't. --- ## Anti-patterns (do not do) - ❌ Doing your own deep web searches once you've identified competitors — delegate pricing and review work to the subagents - ❌ Calling subagents sequentially per competitor instead of in parallel - ❌ Reaching for `brave_web_search` when `WebSearch` would have been fine (this wastes Brave's free-tier budget) - ❌ Invoking Playwright tools directly from the orchestrator — those calls belong in the `pricing-fetcher` subagent so the heavy context is isolated - ❌ Accepting a `pricing-fetcher` report where 4 of 5 competitors used Playwright — investigate WebFetch first - ❌ Adding a 7th bullet "for completeness" - ❌ Inventing pricing because the public site is vague - ❌ Listing every G2 complaint — only recurring patterns - ❌ Writing a closing paragraph after the sharp question --- > Source: [hamzafarooq/claude-code-starter](https://github.com/hamzafarooq/claude-code-starter) — distributed by [TomeVault](https://tomevault.io).