Startup Competitors

What the source does

• FilesystemNone observedNo reads or writes in the source
• NetworkNone observedNo outbound calls in the source
• ShellNone observedNo commands run in the source
• DependenciesPinnedNothing floats
• SecretsNone foundNo keys in source

Five audit layers

Gates applied: no behavioural pass.

Audit 0.4.1 · source sha 053aee617f0d · 18 Oct 2026Report an issue or request a re-scan

• Publisherferdinandobons GitHub userOwner of the repository this skill is read from
• Source folderferdinandobons/startup-skill/startup-competitorsThe exact folder we audited
• LicenceMITAs the repository states it
• Commit pinned for this verdict053aee617f0dA new audit runs when the source changes
• Repository statusNot archivedLast push 109 days ago
• Sandboxed runNot runSkills are read, not executed; layer L4 is skipped

Its description: 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…

---
name: startup-competitors
description: 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 competitors, compare products in a market, or research who they're competing against. Triggers for "who are my competitors", "competitive analysis", "competitor research", "battle cards", "pricing comparison", "competitor pricing", "market players", "competitive intelligence", "competitive landscape", "who else is in this space", "competitive moat", or any request to profile, compare, or map competitors in a category. Works standalone — no prior startup-design session needed.
---

# 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, we

Excerpt, first 6,000 characters of SKILL.md, verbatim from the audited commit. It is text from someone else’s repository, quoted so you can judge it before your agent reads it.

1. 2026-10-18
   ✓SAFE B 89 / 100

   first audit · source 053aee617f0d

Questions about Startup Competitors

• github.com/ferdinandobons/startup-skill/tree/main/startup-compet — the audited source at commit 053aee617f0d and every finding aboveObserved · 18 Oct 2026

Spotted something wrong? Report a correction · Methodology