Competitive Positioning Research

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name: competitive-positioning-research version: 1.0.0 description: "Strategic competitive analysis skill for positioning research. Defines comparison dimensions, selects structural analogues, researches each comp, scores your approach 1-5, and produces ranked recommendations. Use before writing public-facing pages or when product positioning decisions are being made. Hard limit: 4 web searches per session." metadata: { "openclaw": { "emoji": "🔭", "requires": { "bins": [], "env": [] }, "primaryEnv": null, "network": { "outbound": true, "reason": "Uses web_search to research competitor platforms and industry case studies on behalf of the user." }, "security_notes": "Fetches publicly available content (competitor landing pages, case studies, industry articles) on behalf of the user. No private data is transmitted. Web searches are limited to 4 per research session." } }

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Skill: Competitive Positioning Research

_Owner: Archie | Maintained by: Sara_

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When to Use This Skill

Triggers:

"How does our X compare to how [category] leaders do it?"

"Research how successful [category] platforms handle [specific problem]"

"What can we learn from [Platform A / Platform B] for our [page/feature/approach]?"

Pre-ship review Phase 3 (strategic positioning check)

Before writing any public-facing page that has direct category comps

Not for:

Technical claim accuracy — that's the technical accuracy review pattern (fee amounts, hash functions, protocol specs)

Deep product research — that's a full Archie research brief

Pricing analysis — that's Becky

This skill is for *strategic/UX research* — "how did the best examples in this space solve this specific problem, and how do we stack up?" Not "is this claim correct?"

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The Research Pattern

Step 1: Define the comparison dimensions

Before searching, lock down:

What specific problem are we researching? (e.g. "two-sided marketplace landing page hero CTA — which side to prioritise?")

What category are the comps in? (e.g. "developer-facing two-sided marketplace")

3–5 dimensions to score on (e.g. side prioritisation, cold-start handling, social proof, trust signals)

Target output: scored table + ranked recommendations

Don't start searching until you've written these down. Undefined scope = research sprawl.

Step 2: Select comps

Pick 4–6 platforms. More is noise. Selection criteria:

Same audience type (developer, consumer, enterprise)

Same structural problem (two-sided, subscription, usage-based)

Mix of early-stage (how they launched) and mature (how they evolved)

Prioritise structural analogues over direct competitors — defensive bias corrupts the analysis

Step 3: Research each comp

For each platform, find:

How they handled the *specific problem* (not general company history)

What they prioritised early vs. mature stage

What worked and what they changed

One key lesson that applies to your situation

Search patterns that work:

"[platform] landing page teardown"

"[platform] early growth strategy"

"[platform] cold start problem"

"two-sided marketplace [specific problem] best practices"

"[platform] how they solved [problem]"

Model knowledge vs. web search: For well-known platforms (Airbnb, Stripe, Uber, Replicate), Archie has sufficient model knowledge for structural patterns. Use web search for specifics — a changed CTA, a pivot, a dated case study.

Step 4: Score our approach

Build a scoring table against the dimensions from Step 1. Score each 1–5 with a brief, honest note.

A 2/5 with a real explanation is more useful than a 4/5 that flatters the team. Score what exists, not what was intended.

Step 5: Produce recommendations

Ranked by impact, not effort. For each recommendation:

What to change

Why (which comp's evidence supports it)

Approximate effort: one-line fix / section rewrite / new feature

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Output Format

# [Topic] — Competitive Positioning Research
_Date: YYYY-MM-DD | Analyst: Archie_
Executive Summary
[3–4 sentences: headline finding + top recommendation]
Comparison Dimensions
[The 3–5 dimensions being scored, and why they matter]
Case Studies
[Platform]
What they did: ...
When (early vs mature): ...
Key lesson: ...
Scoring Table
| Dimension | Score (1-5) | Notes |
|---|---|---|
Recommendations (ranked by impact)
1. [Change] — [why, which comp supports it] — [effort]
What We Got Right
[Strengths to preserve]

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Time Budget and Scope

| Type | Comps | Time | |---|---|---| | Quick (known category) | 2–4 | 8–10 min | | Full (novel category) | 5–6 | 15–20 min |

Hard limit: 4 web searches. Synthesise from what you find. If you haven't found enough after 4 searches, scope was too broad — narrow the question, not the search count.

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Worked Example

Date: 2026-03-24 Product: Reddi Agent Protocol (two-sided agent marketplace) Problem: Two-sided landing page hero CTA — which side to prioritise? File: projects/reddi-agent-protocol/reviews/archie-marketplace-research-2026-03-24.md

Comps studied: Stripe, Uber, Airbnb, Hugging Face, Replicate (5 — right call, stopped before noise)

Dimensions scored: Side prioritisation, supply-side hook, demand-side hook, chicken-and-egg acknowledgement, social proof, trust signals

Headline finding: Seller-first hero is defensible at pre-supply stage, but the page is missing three things: cold-start acknowledgement, zero-friction demo, and any social proof. The "Browse Agents" CTA risks leading to a near-empty index — an active anti-signal.

Top recommendation: Add a dual-path hero split so both sides feel directly spoken to without diluting the primary message.

Surprise: Replicate — the closest structural analogue — led with *consumers* from day one, and made a live runnable demo the primary conversion mechanism on the landing page. Not a "coming soon" but an actual working model you could run from the hero. That's the bar for our live demo CTA.

Score that stung: Chicken-and-egg handling got 1/5. The page doesn't acknowledge it's early-stage, and "why join a marketplace with no one in it yet?" has no answer anywhere on the page. Honest score, actionable gap.

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Common Mistakes

Too many comps. Six becomes noise. Pick four or five strong structural analogues, research them properly, and stop.

Comparing to direct competitors. Direct comp analysis introduces defensive bias. Structural analogues (same problem, different space) produce better lessons. Airbnb teaches more about marketplace cold starts than any other agent marketplace would.

Generous scoring. A scoring table where everything is 3–4/5 is useless. The purpose of the table is to surface gaps. If nothing scores below 3, you're flattering the work, not analysing it.

Searching too broadly. two-sided marketplace returns 10 years of generic content. Replicate model provider growth strategy returns the specific insight you need. Start specific, widen only if necessary.

Grepping the full repo. Archie times out on grep -r across a full project directory. Always read targeted files by path. Never use recursive search on a large workspace.

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_This skill was written 2026-03-24 by Sara, based on Archie's marketplace research for Reddi Agent Protocol._