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Competitive Platform Analysis

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affaan-m
competitive-platform-analysis

Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.

Overview

Publisheraffaan-m
RepositoryECC
Skill namecompetitive-platform-analysis
Stars
270.2K
Forks
40.4K
Bundled files
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LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by affaan-m on GitHub. Read the source before you install it.

Installation

Install the Competitive Platform Analysis AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/affaan-m/ECC/tree/main/.agents/skills/competitive-platform-analysis
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/affaan-m/ECC.git /tmp/ECC
mkdir -p .claude/skills
cp -r /tmp/ECC/.agents/skills/competitive-platform-analysis .claude/skills/competitive-platform-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitive Platform Analysis in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Competitive Platform Analysis on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Competitive Platform Analysis is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Competitive Platform Analysis

Use this skill to decide who to benchmark and where to find them before any scoring begins. A competitive analysis is only as good as its frame: the wrong set makes the client look either unbeatable or doomed. The goal is a defensible, decision-relevant set — not an exhaustive census.

When to Activate

  • About to start a competitive benchmarking project and need to define the competitor set first.
  • Unsure which companies belong in Direct / Adjacent / Aspirational tiers.
  • Need a defensible, pruned scope for a market landscape report.
  • Has a positioning brief and wants to identify who contests that position.
  • First step before running benchmark-methodology.

Client positioning brief (establish first)

Before scoping the set, establish the client's positioning brief. If you don't already have it, run a short brand-discovery interview to elicit it — do not invent one and do not scope the set blind. The brief supplies:

  • Identity / aesthetic register — what kind of studio or company this is and how it presents itself.
  • Offer — what services or products it delivers.
  • Target clients — who it sells to.
  • Differentiator — the moat or positioning argument the client believes in.
  • Scoping consequence — the implication for how to weight competitors (e.g., prioritize by distinctiveness vs. capability overlap vs. price).
  • Strategic tension — the paired axes that define the client's white-space (e.g., memorability × hireability).

Do not proceed without the positioning brief. A competitor list scoped without the client's lens is noise, not intelligence. The scoping consequence in particular determines which competitors are strong rivals (those that contest the client's moat) vs. merely overlapping on service menu.

Selection criteria

For each candidate, capture these axes — they decide both inclusion and tier:

  • Size / model — solo, micro-studio (2–8), boutique (sub-30), mid-size agency. Match the client's own band; same-band studios are the realistic head-to-head set.
  • Niche / specialization — how closely the candidate's focus overlaps with the client's offer. Tighter overlap = more direct.
  • Geography / market — EU vs US vs global-remote; language; time-zone reach. Note whether they win the same clients the client targets.
  • Pricing & engagement model — productized sprints, retainer, project, day-rate; transparent vs "contact us". Signals positioning maturity.
  • Portfolio style — generic vs. opinionated/editorial vs. contrarian. Closer to the client's aesthetic register = more they contest the client's distinctiveness.
  • Technical depth / craft maturity — relevant if the client's credibility story includes public process work, open tooling, or documented systems.
  • Brand strength — does the studio have an ownable verbal/visual identity, or is it interchangeable? Weight this per the client's scoping consequence.

Player taxonomy — axes to populate across

Don't sort competitors into niche-specific buckets; sort them along a few generic axes so the landscape isn't skewed toward one archetype. These axes apply to any creative-service market (design, motion, copywriting, branding, content, film, etc.). Aim for breadth across each axis first, then prune to the most instructive.

  1. Positioning stance — brand-led / editorial (competes on identity, voice, POV) vs capability-led (competes on craft, throughput, outcomes). Populate both poles; the client's closest mirror sits at its own end.
  2. Specialization — specialist (one tight discipline or vertical) vs generalist (broad service menu). Tighter overlap with the client's focus = more direct.
  3. Size / model — solo / micro vs boutique vs mid-size vs enterprise-scale. Same-band players are the realistic head-to-head; larger bands are the aspirational/commercial-maturity reference.
  4. Engagement format — productized (named sprints, audits, fixed packages) vs bespoke (custom project / retainer). Signals positioning maturity.
  5. Distinctiveness posture — conventional / safe vs contrarian / manifesto-driven. The opinionated end is key for distinctiveness benchmarking in any niche.
  6. Evidence / credibility model — outcome-led (metrics, named clients, case depth) vs aesthetic-led (portfolio, awards). Tells you how each player earns trust.
  7. Brand strength of the operator — interchangeable vs cult / ownable identity (including senior independents who prove the "memorable solo brand" model).
  8. Market / reach — local / regional vs global-remote; note whether they win the same clients the client targets.

Plot each candidate on the relevant axes; a competitor is direct when it sits near the client on positioning, specialization, size, and market at once.

Competitive tiers (how the set resolves)

Group the final set into three tiers — this structure carries through to the report:

  • Direct — same band, overlapping offer, same client targets. The realistic head-to-head.
  • Adjacent — partial overlap (one capability, or a different client size) that pressures at the edges.
  • Aspirational — players the client is not competing with today but whose brand or commercial maturity sets the bar to aim at.
  • (Watch also for substitutes: no-code/AI tools, in-house teams, generalist freelancers — note as a threat vector, not a profiled competitor unless materially relevant.)

Data sources (where to look)

Match the source to the dimension you need. The platform types below are generic; substitute the ones native to the client's niche (e.g. Dribbble/Behance for design, showreel/Vimeo for motion, writing samples/published work for copy):

  • Portfolio / craft platforms — craft quality, range, aesthetic register (e.g. Dribbble, Behance, Vimeo, or the niche's equivalent showcase).
  • Awards / curated showcases — craft ambition and editorial recognition; over-indexes on flashy, so cross-check commercial credibility (e.g. Awwwards, industry award lists).
  • Competitor's own site — primary source for positioning, voice, offer packaging, pricing posture, named clients, manifesto/POV.
  • LinkedIn — team size/model, founder narrative, post cadence, client logos, geography.
  • Review directories — reviews, named clients, project sizes, engagement models; strongest signal for commercial credibility and enterprise-readiness (e.g. Clutch.co or the niche's equivalent).
  • Open / public work — process repos, published samples, open creative output: depth and craft-transparency evidence.
  • Conference talks / podcasts / newsletters — thought-leadership depth and POV ownership.

Always verify claims across at least two sources before treating a competitor attribute as fact (self-reported site copy ≠ verified outcome). Carry an adversarial-verification discipline into every profile.

Scoring matrix template (selection stage)

A lightweight pre-filter to decide who graduates into full benchmarking. Score 1–5; keep candidates that score high on either distinctiveness or credibility — the client's strategic tension means both poles are instructive.

CandidatePositioning stanceSpecializationSize bandTierOffer overlap (1–5)Distinctiveness (1–5)Commercial credibility (1–5)Craft proximity (1–5)Include?

Rules of thumb (apply per the client's scoping consequence in the positioning brief):

  • High distinctiveness and high credibility → must-profile (proves the client's target tension is achievable).
  • High distinctiveness, low credibility → cautionary case (memorable but un-hireable — a potential failure mode to learn from).
  • High credibility, low distinctiveness → "competent but forgettable" mass the client defines itself against.
  • Low on both → drop unless needed for landscape breadth.

Output of this stage

A scoped, tiered competitor set (typically 10–18 candidates → 8–12 profiled), each tagged with its axis positions, tier, and source links, ready to hand to benchmark-methodology.

Anti-Patterns

  • Scoping without a positioning brief. A competitor list built without the client's lens is noise. The brief determines what counts as a real rival.
  • Listing every similar company. The goal is a defensible 10–18 candidate set, not a census. Breadth without pruning makes benchmarking unmanageable.
  • Blurring the Direct/Adjacent/Aspirational tiers. These tiers serve different strategic purposes. Mixing them produces a flat list that can't drive decisions.
  • Relying on a single source per competitor. Self-reported site copy is marketing, not fact. Verify attributes across at least two sources.
  • Jumping straight to scoring. This skill scopes and tiers the set. Benchmark-methodology handles scoring. Don't conflate the two steps.

Examples

Scenario: A boutique brand-identity studio (2-person, EU-remote, productized sprints, contrarian/manifesto-driven aesthetic) wants to scope its competitive set before benchmarking. The strategic tension from the positioning brief is memorability × hireability.

Step 1 — eight-axis population (sample candidates):

CandidatePositioning stanceSpecializationSize bandEngagementDistinctivenessEvidence modelBrand strengthMarket
Studio Abrand-led / editorialidentity onlymicroproductizedcontrarianaesthetic-ledcultglobal-remote
Studio Bcapability-ledbroad DS+motionboutiquebespokeconventionaloutcome-ledinterchangeableUS
Agency Ccapability-ledbrand+digitalmid-sizeretainerconventionaloutcome-ledinterchangeableEU
Freelancer Dbrand-ledbrand voice onlysoloday-rateeditorialaesthetic-ledownableglobal
Studio Ebrand-ledbrand strategymicroproductizedmanifesto-drivenoutcome-ledcultEU-remote

Step 2 — pre-filter scoring (client scoping consequence: weight distinctiveness because the client's moat is POV-first, not capability breadth):

CandidateOffer overlap (1–5)Distinctiveness (1–5)Commercial credibility (1–5)Craft proximity (1–5)TierInclude?
Studio A5535Direct✓ must-profile
Studio B3253Adjacent✓ credibility anchor
Agency C2152Aspirational✓ scale reference
Freelancer D4424Direct✓ cautionary case
Studio E5544Direct✓ must-profile

Step 3 — output handed to benchmark-methodology: Five candidates (3 Direct, 1 Adjacent, 1 Aspirational), each tagged with axis positions, tier, and source links. Studio A and Studio E are the sharpest head-to-head rivals; Freelancer D is the "memorable but un-hireable" cautionary case to learn from.

Related Skills

  • brand-discovery — use first to establish the positioning brief and strategic tension that scopes the competitor set.
  • benchmark-methodology — the next step; takes the tiered set and scores each competitor across nine dimensions.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Competitive Platform Analysis AI skill do?

Use when scoping a competitive landscape — identifying, categorising, and score-filtering a competitor set before any benchmarking begins. Decides who counts as a competitor, which tier they belong to, and which sources to mine. First step in the three-skill competitive pipeline; precedes benchmark-methodology.

Why use Competitive Platform Analysis on TypingMind?

Because you install it once and use it with any model. Competitive Platform Analysis is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Competitive Platform Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/affaan-m/ECC/tree/main/.agents/skills/competitive-platform-analysis. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Competitive Platform Analysis?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Competitive Platform Analysis?

As many as you like. As long as a model supports skills, you can use Competitive Platform Analysis with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Competitive Platform Analysis AI skill free?

Yes. It is published on GitHub by affaan-m under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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