Install
$ agentstack add skill-varunk130-ai-customer-discovery-skills-competitive-analyzer ✓ scanned · ✓ verified — works with Claude Code, Cursor, and more.
Security review
✓ PassedNo issues found. Passed automated security review. · v0.1.0 How review works →
- ✓ Prompt-injection patterns
- ✓ Secret / credential exfiltration
- ✓ Dangerous shell & filesystem operations
- ✓ Untrusted network calls
- ✓ Known-malicious package signatures
What it can access
- ✓ Network access No
- ✓ Filesystem access No
- ✓ Shell / process execution No
- ✓ Environment & secrets No
- ✓ Dynamic code execution No
From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.
About
Competitive Analyzer
Run a disciplined competitive teardown that goes beyond a feature checklist. The output is the smallest set of decision-relevant dimensions, scored objectively across competitors, with explicit gaps you can attack and risks you should defend.
Core Principle
A competitive analysis is only useful if it changes a decision. Most teardowns produce 40-row feature grids that no one reads. This skill forces brutal selection: pick the 4-6 dimensions buyers actually weigh, score them with a defensible rubric, and surface the 2-3 moves the analysis implies.
Output
Save to outputs/competitive-analysis-[market]-[YYYY-MM-DD].md
| Artifact | Description | |----------|-------------| | Dimension Map | The 4-6 buyer-weighted dimensions with rationale for inclusion | | Scoring Matrix | Each competitor scored 1-5 on each dimension, with one-line evidence per cell | | Gap Map | Dimensions where every competitor underperforms - the white space | | Risk Map | Dimensions where one competitor strongly outperforms us | | Implied Moves | 2-3 concrete strategic moves the analysis suggests, ranked by leverage |
Process
Step 1: Frame the Market
I'll ask: > "What market are we analyzing, and from whose perspective? List the competitors (3-7 works best). What's the deal context - what's a typical buyer trying to accomplish?"
Step 2: Pick the Dimensions
Generate a candidate list of 12-15 dimensions, then ruthlessly cut to 4-6 by applying two filters:
- Decision relevance - does this dimension actually move buying decisions?
- Discriminating power - do competitors meaningfully differ on it? (Dimensions where everyone scores the same get cut.)
Step 3: Score with Evidence
For each (competitor × dimension) cell, score 1-5 with a single sentence of evidence - a public artifact, a customer quote, a product behavior - not opinion.
Step 4: Identify Gaps and Risks
Two scans across the matrix:
- Gap - any dimension where the highest score is ≤3 → market is underserved, opportunity
- Risk - any dimension where a competitor scores 5 and we score ≤3 → defensive priority
Step 5: Implied Moves
Translate the gap and risk maps into 2-3 concrete moves: build, partner, position, retreat. Each move includes the evidence from the matrix that justifies it.
Tips
- Evidence-only scoring. A score without evidence is opinion; opinions don't survive the next leadership review.
- Cut the matrix until it hurts. A 4-dimension matrix that gets used beats a 12-dimension matrix that gets ignored.
- Re-run quarterly. Competitive position shifts; rerun cadence keeps the analysis honest.
Source & license
This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.
- Author: varunk130
- Source: varunk130/ai-customer-discovery-skills
- License: MIT
- Homepage: https://github.com/varunk130
Install and usage instructions live in the source repository linked above.
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Versions
- v0.1.0 Imported from the upstream source.