Agent skill

Narrative Landscape

by indranilbanerjee in indranilbanerjee/digital-marketing-pro

Map the competitive narrative landscape and score unclaimed positioning gaps.

MITAuto-check passedMarketing & SEO

Install Narrative Landscape

skills CLI
$ npx skills add indranilbanerjee/digital-marketing-pro --skill narrative-landscape -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install indranilbanerjee/digital-marketing-pro narrative-landscape --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/indranilbanerjee/digital-marketing-pro.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/narrative-landscape .claude/skills/narrative-landscape && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
narrative-landscape
GitHub stars
862
Used in
1 other repo
Token cost
~1.9k tokens
SKILL.md length
957 words
Files
1
Skills in repo
162
Repo updated
First seen
Licence
MIT

At a glance

Map the competitive narrative landscape and score unclaimed positioning gaps.

  • Works in 8 steps: Load brand context: Read… → Define narrative dimensions: Validate… → Analyze each competitor's positioning:… → …
  • Tasks that involve Positioning and messaging
  • SKILL.md covers Purpose, Input Required, Process and Output, plus 1 more section
  • Calls python

What it does

Narrative Landscape is an agent skill from indranilbanerjee/digital-marketing-pro. Map the competitive narrative landscape and score unclaimed positioning gaps. "find us a differentiated position"

Its SKILL.md is about 1.9k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Marketing & SEO, covering Positioning and messaging. The repository describes itself as: An open-source AI marketing operating system for strategy, SEO, AEO/GEO, paid media, content, CRM, and analytics - grounded in brand context, human approval, and verifiable… The licence is MIT.

When your agent uses it

  • Tasks that involve Positioning and messaging

Example prompts

  • “find us a differentiated position”
  • “/narrative-landscape”

Requirements

  • Python 3

Workflow steps

8 steps, taken from the first numbered list in SKILL.md.

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load…
  2. Define narrative dimensions: Validate and refine the positioning dimensions for the market — confirm each dimension represents a genuine…
  3. Analyze each competitor's positioning: For every competitor on every dimension, extract positioning signals from the specified messaging…
  4. Map positions via narrative-mapper.py: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/narrative-mapper.py" --brand {slug} --action…
  5. Identify clusters and gaps: Analyze the landscape map for crowded positions where 3+ competitors cluster on similar positioning (high…
  6. Score each gap: Evaluate every identified gap by two factors — customer value (how much do target customers actually want a brand…
  7. Recommend optimal positioning territory: Select the highest-scoring gap as the recommended positioning territory. Justify the…
  8. Generate positioning strategy: For the recommended territory, produce actionable positioning guidance — key messages, proof points to…

What it can do on your machine

Read from SKILL.md and the folder at commit 9e949f3. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Shell commands in SKILL.md call:

    • python

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Narrative Landscape loads about 1.9k tokens when it runs. Until then it costs about 33 tokens; SKILL.md has 957 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~33
When it runs · the whole SKILL.md, loaded when a task matches
~1.9k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from indranilbanerjee/digital-marketing-pro at commit 9e949f3, republished under its MIT licence (© indranilbanerjee). 957 words, ~1,931 tokens.

Download SKILL.mdSave it as .claude/skills/narrative-landscape/SKILL.md (or your agent's skills folder).
name
narrative-landscape
description
Map the competitive narrative landscape and score unclaimed positioning gaps. "find us a differentiated position"

/digital-marketing-pro:narrative-landscape

Script location. If your host does not set ${CLAUDE_PLUGIN_ROOT}, the scripts are in this plugin's scripts/ folder, next to skills/.

Purpose

Map the competitive narrative landscape to identify positioning opportunities the brand can own. Analyze how each competitor positions itself across key market dimensions — price-value, innovation-reliability, specialist-generalist, premium-accessible, or custom dimensions relevant to the industry. Find crowded territories where multiple competitors cluster, unoccupied gaps where no brand has staked a claim, and recommend the highest-value positioning territory for the brand to claim based on customer desirability and brand credibility.

Input Required

The user must provide (or will be prompted for):

  • Competitors to map: List of competitor names to include in the landscape analysis — typically 4-8 direct competitors plus any adjacent or aspirational competitors. Each will be analyzed for positioning on every defined dimension
  • Narrative dimensions to analyze: The positioning axes to map competitors against — common dimensions include price-value (premium vs budget), innovation-reliability (cutting-edge vs proven), specialist-generalist (niche expert vs broad platform), premium-accessible (luxury vs mass market), or custom dimensions specific to the industry (e.g., self-serve vs white-glove, enterprise vs SMB, AI-native vs traditional). Recommend 3-5 dimensions for a comprehensive but readable landscape
  • Competitor messaging sources: Where to extract positioning signals for each competitor — company websites (homepage, about, pricing pages), advertising copy (search ads, social ads, display), social media profiles and content themes, press releases and media coverage, analyst reports or review site positioning. Specify URLs or indicate which sources to prioritize

Process

  1. Load brand context: Read ~/.claude-marketing/brands/_active-brand.json for the active slug, then load ~/.claude-marketing/brands/{slug}/profile.json. Pay special attention to the brand's current positioning, value proposition, target audience, and competitive differentiation claims. Also check for guidelines at ~/.claude-marketing/brands/{slug}/guidelines/_manifest.json — if present, load brand voice and positioning guardrails. Check for agency SOPs at ~/.claude-marketing/sops/. If no brand exists, ask: "Set up a brand first (/digital-marketing-pro:brand-setup)?" — or proceed with defaults.
  2. Define narrative dimensions: Validate and refine the positioning dimensions for the market — confirm each dimension represents a genuine spectrum where competitors can differentiate, ensure dimensions are independent (not redundant), and add any industry-standard dimensions the user may have missed. Define the poles of each dimension with clear labels and examples.
  3. Analyze each competitor's positioning: For every competitor on every dimension, extract positioning signals from the specified messaging sources — homepage headlines and hero copy (what they lead with), pricing page framing (how they present value), ad copy themes (what they emphasize to acquire customers), social content patterns (how they present themselves day-to-day), and PR/media positioning (how they describe themselves to press). Score each competitor's position on each dimension as a value from -5 to +5 representing their placement between the two poles.
  4. Map positions via narrative-mapper.py: Execute python "${CLAUDE_PLUGIN_ROOT}/scripts/narrative-mapper.py" --brand {slug} --action map-landscape --data '{positions_json}' with the competitor position data to generate the narrative landscape map — plotting all competitors on each dimension pair, calculating cluster density, and identifying open territories. The script produces structured positioning data with gap analysis.
  5. Identify clusters and gaps: Analyze the landscape map for crowded positions where 3+ competitors cluster on similar positioning (high competition, difficult to differentiate), contested positions where 2 competitors overlap (direct rivalry), and unoccupied gaps where no competitor has claimed territory (potential opportunities). Classify gaps by size, strategic value, and defensibility.
  6. Score each gap: Evaluate every identified gap by two factors — customer value (how much do target customers actually want a brand positioned here? Is there demand for this positioning?) and brand credibility (can this brand credibly claim this territory given its product, history, and capabilities?). Multiply these scores to produce an opportunity score for each gap. Rank gaps by opportunity score.
  7. Recommend optimal positioning territory: Select the highest-scoring gap as the recommended positioning territory. Justify the recommendation with evidence — why customers want it, why the brand can credibly own it, why competitors have left it open, and what risks exist (competitors may move to contest it).
  8. Generate positioning strategy: For the recommended territory, produce actionable positioning guidance — key messages, proof points to support the claim, content themes that reinforce the position, channels best suited to establish the positioning, and a timeline for claiming the territory through consistent messaging across all brand touchpoints.
Show full SKILL.md (263 more words)Show less

Output

A comprehensive narrative landscape analysis containing:

  • Narrative landscape map: Competitor positions plotted on each dimension pair — showing where each competitor sits on every axis, with clear visualization of clusters, contested zones, and open territories
  • Cluster analysis: Identification of crowded positioning territories where multiple competitors overlap — which positions are contested, how intensely, and what it means for brands trying to differentiate in those areas
  • Gap analysis with opportunity scores: Every unoccupied or underserved positioning territory identified, scored by customer desirability multiplied by brand credibility, ranked from highest to lowest opportunity value
  • Recommended positioning territory: The single highest-value gap the brand should claim — with evidence-based justification covering customer demand, brand credibility, competitive dynamics, and defensibility against future competitor moves
  • Positioning strategy with messaging guidance: Key messages, proof points, supporting themes, and language patterns that establish the brand in the recommended territory — calibrated to brand voice from context
  • Content plan to claim the territory: Specific content types, channels, and cadence to systematically reinforce the positioning over 30, 60, and 90 days — homepage messaging updates, ad copy angles, social content themes, PR narratives, and thought leadership topics

Agents Used

  • competitive-intel — Competitive messaging extraction and analysis across websites, advertising, social media, and press coverage, positioning signal scoring on each narrative dimension, cluster and gap identification through landscape pattern analysis, and competitive response prediction for recommended positioning moves
  • marketing-strategist — Positioning strategy development from gap analysis to actionable territory selection, customer desirability and brand credibility scoring for each gap, messaging framework creation with key messages, proof points, and content themes, and territory-claiming content plan with 30/60/90-day milestones across channels

© indranilbanerjee, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/narrative-landscape of indranilbanerjee/digital-marketing-pro.

Open the folder on GitHubat commit 9e949f3

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in indranilbanerjee/digital-marketing-pro, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Narrative Landscape compared with similar skills
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Categories

Questions about Narrative Landscape

What does Narrative Landscape do?

Map the competitive narrative landscape and score unclaimed positioning gaps. Narrative Landscape is an agent skill from indranilbanerjee/digital-marketing-pro. Map the competitive narrative landscape and score unclaimed positioning gaps.

When should I use Narrative Landscape?

Narrative Landscape fits situations like: tasks that involve Positioning and messaging.

How do I install Narrative Landscape in Claude Code?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill narrative-landscape -a claude-code`. Or copy the skill folder (skills/narrative-landscape in indranilbanerjee/digital-marketing-pro) into .claude/skills/narrative-landscape in your project. Claude Code loads it when a task matches its description.

How do I install Narrative Landscape in Codex?

Run `npx skills add indranilbanerjee/digital-marketing-pro --skill narrative-landscape -a codex`. Or copy the skill folder (skills/narrative-landscape in indranilbanerjee/digital-marketing-pro) into .agents/skills/narrative-landscape in your project. Codex loads it when a task matches its description.

Can I use Narrative Landscape in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add indranilbanerjee/digital-marketing-pro --skill narrative-landscape -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/narrative-landscape, .gemini/skills/narrative-landscape, .github/skills/narrative-landscape and .opencode/skills/narrative-landscape in your project.

What does Narrative Landscape need to run?

Going by SKILL.md and its folder, Narrative Landscape needs the command-line tools its instructions call (python). Our summary lists: Python 3.

Does Narrative Landscape access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Narrative Landscape safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Narrative Landscape use?

Narrative Landscape is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Narrative Landscape use?

About 1.9k tokens (SKILL.md is roughly 7.7k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Narrative Landscape?

Skills that share tags, products or a category with Narrative Landscape: Marketing Os (Yuzzyuk/marketing-os, 540 stars), Revenue Centric Design (heliocosta-dev/revenue-centric-design, 740 stars), Startup Positioning (ferdinandobons/startup-skill, 1.2k stars) and Stanley Druckenmiller Investment (tradermonty/claude-trading-skills, 3k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Narrative Landscape?

indranilbanerjee (a GitHub user) maintains it in indranilbanerjee/digital-marketing-pro, which has 862 GitHub stars. The repository holds 162 skills in this directory. The repository was last updated on October 9, 2026.

Source: indranilbanerjee/digital-marketing-pro on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.