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Competitive Analysis

OrganizationPopular
ginlix-ai
competitive-analysis

Competitive landscape analysis: positioning, scorecards, moat assessment, market share trends. Triggers on competitive analysis, competitive landscape, competitor benchmarking, moat assessment, market share, who are the competitors.

Overview

Publisherginlix-ai
RepositoryLangAlpha
Skill namecompetitive-analysis
Stars
1.8K
Forks
288
Bundled files
4
LicenseApache-2.0
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.

  • 4 bundled files

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

  • Open source

    Published by ginlix-ai on GitHub. Read the source before you install it.

Installation

Install the Competitive 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.
  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/ginlix-ai/LangAlpha.git /tmp/LangAlpha
mkdir -p .claude/skills
cp -r /tmp/LangAlpha/plugins/langalpha_research/skills/competitive-analysis .claude/skills/competitive-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitive 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 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 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 Landscape Mapping

Who competes, on what, and which advantages survive. The product is a comparison, so the comparison has to be real: same period, same metric definition, same basis, with each figure's provenance visible. A table of numbers that were measured differently looks like analysis and is not one.

Evidence labels, source tiers, staleness, the readiness posture and the intake limits: .agents/skills/research-conventions/SKILL.md, read before the first deliverable.

Reference files

  • The deliverable is a deck or a formatted document, or the request specifies titles, chart types or exact figures: .agents/skills/competitive-analysis/references/presentation.md.
  • The set is judged on customer economics rather than units shipped or stores opened: .agents/skills/competitive-analysis/references/unit-economics.md, which carries the benchmark bands (Rule of 40 on EBITDA margin, NDR, LTV:CAC, CAC payback), the cohort matrix and the traps that make two companies look comparable when they are not.
  • Building an M&A transaction table, a scenario table, or a slide skeleton: .agents/skills/competitive-analysis/references/schemas.md.
  • Choosing the two axes for a positioning matrix: .agents/skills/competitive-analysis/references/frameworks.md.

Data standards

These bind every run, whatever the deliverable is.

Provenance on every competitor metric

Each cell in a competitor table carries the figure, its as-of, and one label from the closed set in .agents/skills/research-conventions/references/evidence.md, which also holds the freshness thresholds and the six staleness states. Three of the seven carry most of a competitor table: fact for the company's own filed statements, company claim for a management figure that is not in them (market share, customer count, addressable market), and street estimate for a consensus or single-analyst number, with its vintage and analyst count.

A third-party sizing of a private company is a street estimate: fact is reserved for a primary document or a data-tool figure for a period that has closed. A research house's published sizing carries the house as its estimator and the publication date as its vintage, and anything with no traceable publisher stays needs-source until one is found.

The as-of is the fiscal period for a reported figure, the publication date for an estimate, and the retrieval date for anything built on price. A private-company figure carries a label and an as-of like any other cell, and prints the reader-facing word its label maps to in .agents/skills/research-conventions/references/evidence.md.

When a competitor metric does not exist

Label the gap rather than filling it. The cell reads not disclosed, and the table note says what was searched. Where the missing metric is load-bearing, meaning the ranking or the moat conclusion moves without it, take one of the two exits in the unsupported-claim rule: drop the conclusion that rests on it, or carry a bound ("the range consistent with the reported total is A to B"), label it assumption, and re-read the posture from the ladder in .agents/skills/research-conventions/SKILL.md against the new input state. A plausible-looking estimate with no flag is exactly the failure that rule exists to catch: evidence.md.

Comparability

  • Periods match. Every competitor metric comes from the same fiscal period, and an exception is flagged in the cell: "(FY24)" against "(H1 2024)". Fiscal-period labelling, LTM and NTM windows, one common base date for comparative returns, and margin numerator and denominator pairing: .agents/skills/research-conventions/references/market-data-rules.md.
  • Definitions match. One calculation methodology across the set, stated once where two companies would define the metric differently.
  • Currency normalised. Convert to USD for international sets, and note the rate and the date used.
  • Missing data reads not disclosed per the gap rule in Step 1.
  • Every number cites its source, in the form [Company] [Document] ([Date]).

Source files provided by the user

  • Extract values directly. Use the numbers as they appear rather than recomputing them.
  • Keep one value per metric across every slide and table in the deliverable.
  • Verify anything you are asked to calculate against related figures in the same source.
  • Match the source's precision. Round as it rounds.

Where a source file and a filing disagree, the conflict register in evidence.md decides it and the artifact shows the selection.

Source hierarchy

The general tiers by claim family are in evidence.md and govern. Two additions specific to this work: sell-side research is the usual route to a private competitor's size and is a street estimate with its vintage, and industry research houses are the usual route to a share figure and carry the house and the publication date.

Depth

Default working analysis: 8 to 12 pages, or 12 to 20 slides, plus the comparison workbook. A rapid competitive read is a first pass at up to five slides. Bands and cut order: .agents/skills/research-conventions/references/depth.md.

Workflow phases

Phase 1, scope it. Confirm: single-company deep dive or multi-company comparison; deck or written memo; the specific competitors, dimensions or strategic question in play; whether an investment context needs scenarios and signposts; and which source files exist and which values come out of them.

Phase 2, research, outline, review, build. Run Steps 0 through 9 below, show the outline with the real numbers already in it, and build the final artifact after the outline has been reviewed. That review is an intake exception and yields under .agents/skills/research-conventions/references/intake.md: when this skill runs as an input to another workflow, or the user asked for the finished artifact in one request, present the outline and keep building without waiting on it.

Analysis workflow

Step 0: Identify the industry-defining metrics

Before any pull, name the three to five metrics this industry is actually judged on:

IndustryKey metrics
SaaSARR, NRR, CAC payback, LTV/CAC, Rule of 40 on EBITDA margin
PaymentsGPV, take rate, attach rate, transaction margin
MarketplacesGMV, take rate, buyer/seller ratio, repeat rate
RetailSame-store sales, inventory turns, sales per square foot
LogisticsVolume, cost per unit, on-time delivery, capacity utilisation

For an industry not listed, take the three to five metrics investors and operators use to benchmark it. Use the same set for every company in the comparison.

Step 1: Market context

Market size now and projected, with the source and its vintage. Growth drivers, headwinds, and the trends reshaping the industry.

Correct: "The embedded payments market is $80B to $100B in 2024, growing 20% to 25% a year (research house, 2024)." Not usable: "The market is large and growing rapidly."

Step 2: Industry economics

Map where the value flows, in the shape the industry actually has:

  • Vertically structured: the value chain layers, with typical margin at each.
  • Platform or network: the participants and the value moving between them.
  • Fragmented: the consolidation dynamic, and how margin differs with scale.

Step 3: Target company profile

MetricValueAs-ofLabel
Revenue$4.96BFY2024fact
Growth+26% y/yFY2024fact
Gross margin45%FY2024fact
Profitability$373M adj. EBITDAFY2024fact
Customers134KQ4 FY2024company claim
Retention92%Q4 FY2024company claim
Market share~15%2024street estimate

For a multi-segment company, add the segment breakdown:

SegmentRevenueRev y/yRev %EBITDAEBITDA y/yMargin
Seg A$25.1B+26%57%$6.5B+31%26%
Seg B$13.8B+31%31%$2.5B+64%18%
Seg C$5.1B-2%12%-$74M-16%-1%
Total$44.0B+18%100%$6.5B15%

Note unallocated corporate costs where the segments do not foot to the total.

Step 4: Competitor mapping

Group the set with whichever cut is real for this industry: by business model (platform, vertical, horizontal), by segment served (enterprise, SMB, consumer), by posture (direct, adjacent, emerging), or by origin (incumbent, disruptor, new entrant).

Step 5: Positioning visualisation

VisualisationBest for
2x2 matrixTwo dominant competitive factors
RadarMulti-factor comparison
Tier diagramNatural clustering into strategic groups
Value chain mapVertical industries
Ecosystem mapPlatform markets

Step 6: Competitor deep dives

Metrics, on the Step 0 set, each row carrying its as-of and label as in Step 3.

Qualitative:

CategoryAssessment
BusinessWhat they do, one sentence
StrengthsTwo or three bullets
WeaknessesTwo or three bullets
StrategyCurrent priorities

Step 7: Comparative analysis

DimensionCompany ACompany BCompany C
Scale●●● $160B●●○ $45B●○○ $8B
Growth●●○ +26%●●● +35%●●○ +22%
Margins●●○ 7.5%●○○ 3.2%●●● 15%

Row-merge discipline. Two competitors share a row only where "Row-merge discipline" in .agents/skills/research-conventions/references/judgment.md allows it.

Step 8: Strategic context

M&A transactions with their multiples and the strategic logic, partnership and integration patterns, capital-raising activity, and regulatory developments.

Step 9: Synthesis

Moat assessment. The rating is a judgement and is written as one: each row shows what was observed, then what we conclude from it.

Moat typeObservedRatingWhy the observation supports it
Network effectsthe flywheel evidence, cross-side or same-sideStrong / Moderate / Weakone clause
Switching costsintegration depth, contractual lock-in, habit
Scale economiesunit cost at volume, minimum efficient scale
Intangible assetsbrand, proprietary data, licences, patents

A Strong with an empty observed column is an opinion in a table. Keep the observation and the rating in separate columns so a reader can disagree with the second while keeping the first. How far the evidence lets the language go: evidence.md.

Then three things: the durable advantages, mapped to the rows above; the structural vulnerabilities that are hard to fix; and the current state against the trajectory, which is where the two diverge.

For an investment context:

ScenarioProbabilityKey driver
Bull30%Share gains, margin expansion
Base50%Current trajectory continues
Bear20%Competitive pressure, margin compression

The probability set follows "Probabilities" in .agents/skills/research-conventions/references/judgment.md, and each scenario names the competitive driver that produces it.

Quality checklist

Verify before delivery. Deck and document formatting has its own checklist in .agents/skills/competitive-analysis/references/presentation.md.

Comparability

  • Every competitor metric is from the same fiscal period, with exceptions flagged in the cell.
  • One metric definition across the whole set.
  • International figures converted at a stated rate and date.

Provenance

  • Every figure carries an as-of and one evidence label.
  • Every number cites its source in [Company] [Document] ([Date]) form.
  • Missing metrics read not disclosed with a table note saying what was searched, and no cell holds an unlabelled estimate.
  • Values taken from user-supplied files match those files exactly, and one metric shows one value everywhere it appears.

Analysis

  • Moat ratings sit beside their observed evidence.
  • Scenario probabilities sum to one, or the weighting is withheld.
  • The comparison table's rows merge only where the hub's row-merge discipline allows.
  • The artifact is inside its depth band and states its readiness posture.

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 Analysis AI skill do?

Competitive landscape analysis: positioning, scorecards, moat assessment, market share trends. Triggers on competitive analysis, competitive landscape, competitor benchmarking, moat assessment, market share, who are the competitors.

Why use Competitive Analysis on TypingMind?

Because you install it once and use it with any model. Competitive 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 Analysis in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ginlix-ai/LangAlpha/tree/main/plugins/langalpha_research/skills/competitive-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 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 Analysis?

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

Is the Competitive Analysis AI skill free?

Yes. It is published on GitHub by ginlix-ai under the Apache-2.0 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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