Competitive Analysis Skill

How this skill behaves (read first)

This is a generative skill, and "do a competitive analysis" is where an AI assistant produces the most confident, least useful output: a flat feature-comparison table of the few obvious direct competitors, with invented specifics, no market context, no gaps identified, and no "so what." Two failure modes compound it — fabricated competitor facts (made-up features, pricing, or market share that read as authoritative) and no strategic conclusion (a report that lists but never decides). A real competitive analysis is scoped to a decision, grounded in honest public evidence, structured by the right framework, and ends in implications and a positioning choice. So this skill gates:

Establish the research objective and the industry context — what decision this informs, and the market it sits in, so competitor moves are read in context rather than in a vacuum.
Apply the always-true core — find all three competitor types, profile from real public sources, use the right framework, prioritize, and translate into strategic implications and a position.
Surface the context-dependent decisions (which framework, primary vs. secondary research, competitor depth, positioning stance, expansion, AI's role) with trade-offs.

Then it hands off to
pm-assumption-rigor-audit
(are the competitor facts, market-size estimates, and "gap exists" claims evidenced or assumed/hallucinated?) and pm-prioritization-rigor-audit
(when it ranks opportunities or competitor tiers).

Scope: this skill owns the competitive/market analysis and the positioning conclusion that follows from it. It defers full product strategy to

pm-vision-strategy

, pricing strategy to

ux-pricing

, go-to-market / market-entry timing to

pm-gtm-plan

, the broader research process and ethics to

pm-discovery

, and problem framing to

pm-problem-statement

Step 0 — Establish context before analyzing

Ask if not known; state the assumption if proceeding without an answer:

What decision does this inform? Set specific research objectives tied to a decision ("which competitor features drive acquisition?") rather than "understand competitors." The objective scopes the work and prevents an endless, unfocused scan.
What's the industry context? Sketch the overview first — market size and growth (TAM/SAM/SOM), key trends and drivers, regulatory/economic environment, and structural shifts (new entrants, substitutes). A rival losing share in a shrinking segment faces different pressures than one in a growing market; context changes the meaning of every move.
Who are the real competitors? Not just the obvious direct rivals — also indirect (same need, different approach) and potential/aspirational ones. Surface them by asking customers what they considered, following where capital flows, monitoring industry news, and searching like a customer would.
What's the stage and resource budget? This sets how much primary vs. secondary research, and how deep the analysis goes.

The always-apply core (true for any competitive analysis)

Frame the industry before benchmarking rivals. Lead with market size, trends, regulatory forces, and structural changes so individual competitor insights are interpreted in context, not as isolated comparisons.
Cover all three competitor types. Direct (similar product, same customers), indirect (same need, different solution — Uber vs. public transport), and potential/aspirational (could enter, or set a benchmark worth learning from). Listing only direct rivals is the most common blind spot.
Profile systematically, and mine real customer reviews. For each competitor capture features, pricing, UX, and how they position themselves (speed? quality? community?). Reviews are the richest signal: analyze patterns — a complaint echoed across dozens of reviews is a structural weakness; recurring praise is a strength you'll have to match or beat.
Use the right framework for the question — not all of them. Perceptual/landscape map (2×2 on dimensions customers care about) to find white space; feature/competitive matrix to compare capabilities and separate table-stakes (everyone must have) from differentiators (what actually drives choice); SWOT to assess your position (the value is in the intersections — a strength that meets an opportunity — not the four lists); Porter's 5 Forces for industry profitability; TAM/SAM/SOM for whether the market is worth pursuing.
Prioritize competitors into tiers. Primary (closest match, directly affects you), secondary (partial overlap), tertiary (could become relevant). Spend strategic energy on the primary tier rather than spreading thin across everyone.
Gather intelligence honestly, from public sources. Public reviews, case studies, marketing materials, free-tier features, conferences, and honest conversations are legitimate. Fake accounts, misrepresenting identity, and ToS-violating scrapers are not. And — critically for an AI — do not invent competitor facts: every feature, price, and share figure must trace to a real source, or be flagged as unverified. The purpose is to understand and differentiate, not to copy or find ways to mislead.
End with strategic implications and a position — the "so what." Translate findings at three levels: feature (where to match the standard, where to differentiate), positioning (which narratives are overcrowded, where you can own a distinct claim), and strategic (which moves need an immediate response vs. which are long-term trends to prepare for). Then land a clear position: pick one of cheapest / highest-quality / most-specialized (claiming two creates mixed signals that erode trust), tie it to a specific persona-problem-promise, and — for early stage — win a focused niche before expanding, one dimension at a time.

The context-dependent decisions (surface, don't auto-apply)

Present each with its trade-off and a recommendation tied to Step 0; let the user choose. Running every framework, or analyzing every competitor equally, is the failure mode.

Decision	Apply when	Avoid / adapt when	Default recommendation
Which framework	Perceptual map to find white space; feature matrix to compare capabilities; SWOT for your position vs. a key rival; Porter's for industry attractiveness; TAM/SAM/SOM for market worth	Running all of them as ritual ("framework theater") that buries the insight	Pick 1–2 that answer the objective; always include the table-stakes vs. differentiator cut
Primary vs. secondary research	Secondary (reports, reviews, public data) first for breadth and speed; primary (interviews, surveys, focus groups) to fill specific gaps	Spending on primary research for questions public data already answers	Secondary to frame the landscape, targeted primary to validate the gaps that matter
Competitor depth	Tier them — deep on primary, lighter on secondary, monitor tertiary	Analyzing dozens equally (thin everywhere) or fixating on one rival	Depth proportional to tier; revisit tiers as the market shifts
Positioning stance	Cheapest, highest-quality, or most-specialized — the one your cost structure and strengths can actually sustain	Claiming two at once (premium + cheap, broad + specialized) — customers disbelieve at least one	Specialized/focused for early stage; one defensible stance, consistent across touchpoints
Expansion direction (if scope includes growth)	One dimension at a time — geography, customer size, or product line — matched to current strengths	Expanding multiple dimensions at once, or before winning the initial niche	Win the niche first; expand the single dimension your proven strengths support
AI's role	AI to draft landscapes, matrices, SWOTs, and positioning from data you provide or will verify	Trusting AI-generated competitor "facts" (hallucinated features, pricing, share)	Use AI to structure and accelerate; verify every competitor claim against a public source

Validate the result (orchestration)

Hand-offs name each lens by its installable skill
name
. Invoke one only if that skill is installed; if it isn't, this skill's own core already carries these rules — proceed without it rather than blocking.

After producing or revising, hand it to the audit lenses rather than declaring it done. These are candidate lenses — posture per

docs/orchestration-policy.md

, or route the whole artifact through

pm-product-review

. Here assumption-rigor
is the always-relevant lens (auto-runs); the others are offered, tied to what the artifact actually contains. If the user invoked this skill for one specific thing, respect that scope.

pm-assumption-rigor-audit
(auto-runs) — the grounding check, and the antidote to AI's biggest risk here: are the competitor facts, the market-size figures, and the "this gap exists / is worth $X" claims backed by real public evidence, or are they assumed, anecdotal, or fabricated? Flag every load-bearing claim that hasn't been verified.
pm-prioritization-rigor-audit
(offer — if it ranks tiers or sets opportunity priorities) — when the analysis ranks competitor tiers or scores market opportunities: is the ranking evidence-based and tied to the decision, or false-precision scoring and a HiPPO pick?

Composition (per
docs/orchestration-policy.md
§9): the analysis feeds downstream rather than auditing it. The problem (

pm-problem-statement

) is an upstream input; the full strategy (

pm-vision-strategy

) and pricing (

ux-pricing

) are downstream (offer to produce next, don't auto-generate). If the audits show the analysis rests on unverified competitor claims or a comparison with no conclusion, resolve toward the purpose: a competitive analysis exists to make a sharper strategic choice — if it doesn't change a decision and isn't grounded in real evidence, it's a table, not an analysis.

Common do/don't patterns

❌ Don't	✅ Do
List only the obvious direct competitors	Cover direct + indirect + potential/aspirational
Invent competitor features, pricing, or market share	Ground every claim in a public source; flag the unverified
Benchmark rivals with no market context	Frame industry size, trends, and structural shifts first
Dump a flat feature checklist	Separate table-stakes from differentiators; map the white space
Treat the SWOT as four lists	Mine the intersections — strengths that meet opportunities, threats that hit weaknesses
Analyze every competitor equally	Tier them; spend depth on the primary tier
Use fake accounts / ToS-violating scrapers	Public reviews, case studies, free tiers, honest conversations
Claim "cheapest and best"	Pick one stance — cheapest, quality, or specialized — and keep it consistent
Stop at a report	End with feature/positioning/strategic implications and a clear position
Trust an AI-generated landscape as fact	Use AI to structure; verify each fact yourself
Ship the analysis unchecked	Hand off to assumption-rigor (+ prioritization-rigor)

pm-competitive-analysis

NPX Install

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