Competitor Content Intelligence logo

Competitor Content Intelligence

Organization
sandbaseai
competitor-content-intelligence

Analyze competitor content coverage and uncover differentiated content opportunities using web search, page extraction, and DataForSEO content analysis. Use when asked for competitor content research, editorial gap analysis, content brief inputs, or topic whitespace.

Overview

Publishersandbaseai
Repositorysandbase-skills
Skill namecompetitor-content-intelligence
Stars
200
Forks
21
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

    Published by sandbaseai on GitHub. Read the source before you install it.

Installation

Install the Competitor Content Intelligence 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/sandbaseai/sandbase-skills/tree/main/marketing/competitor-content-intelligence
  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/sandbaseai/sandbase-skills.git /tmp/sandbase-skills
mkdir -p .claude/skills
cp -r /tmp/sandbase-skills/marketing/competitor-content-intelligence .claude/skills/competitor-content-intelligence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Competitor Content Intelligence 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 Competitor Content Intelligence 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 Competitor Content Intelligence 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.

Competitor Content Intelligence

Compare public content evidence to identify differentiated editorial and landing-page opportunities. This Skill calls the named search, extraction, and analysis capabilities in the SandBase API map through the SandBase MCP gateway. Use an authorized SandBase MCP connection and the discover → inspect → run workflow below; never request, print, or store an API key in the research output.

Read example workflows when the user needs a starting prompt or wants to understand the output.

Operating principles

  • Focus on content strategy differentiation, not content copying. The goal is to find what's missing, underserved, or poorly served.
  • Treat web data and content analysis as evidence; treat gap identification, angle suggestions, and content briefs as judgment clearly separated from observations.
  • Respect intellectual property: do not reproduce competitor text beyond short necessary excerpts for analysis.
  • Select geography, language, and audience deliberately. A gap in one market may be saturated in another.
  • Optimize for content that serves the target's audience and conversion goals, not content volume alone.
  • Keep user strategy, positioning, and competitor lists confidential unless sharing is explicitly requested.

Workflow

1. Frame the analysis

Collect or infer: the target audience and their problems, the topic or category, 2–5 competitor domains or brands, the target's positioning and differentiation, content types in scope (blog, landing page, documentation, comparison, tool), and the intended use of findings (content calendar, brief backlog, editorial strategy).

Classify the request as one or more of:

  • Coverage mapping: what topics do competitors cover vs. not?
  • Depth analysis: where is competitor content superficial or outdated?
  • Angle discovery: what perspectives, formats, or audiences are underserved?
  • Question gap: what buyer questions remain unanswered across competitors?
  • Format opportunity: what content types are missing (interactive, data, video, tool)?

2. Select and call SandBase capabilities

Read the SandBase API map before selecting tools. Treat each listed tool_name as a capability identifier to resolve through the SandBase gateway:

  1. Use sandbase_discover with the provider and capability to find the current endpoint name.
  2. Pass the returned name to sandbase_inspect; read inputSchema, pricing, and execute_as.
  3. Follow execute_as to call sandbase_run using execute_as.arguments.name and schema-defined arguments. If it returns a run_id, poll sandbase_run_get within the task budget until completed or failed; report pending or failed runs without automatically resubmitting them.
  4. Keep the returned endpoint name, query parameters, and result metadata with the returned data.

3. Discover competitor content

Use exa_search and cloudsway_search to map the competitor content landscape:

StrategyApproach
Topic coverageSearch for the category + each competitor's domain to find their published content
Question targetingSearch for buyer questions in the category and see which competitors answer them
Format varietySearch for comparisons, guides, tools, calculators, templates in the category
Freshness gapsSearch with date filters to find where competitor content is outdated

Tips:

  • Use the domain restriction option from the inspected schema to search within a specific competitor’s site.
  • Use semantic queries that describe the ideal content (Exa responds well to this).
  • Search for the same topic across multiple competitors to build a coverage matrix.
  • Search for "[category] + [buyer question]" to find question-gap opportunities.
  • Use cloudsway_search for broader web coverage that may surface sources Exa doesn't index.

4. Extract and analyze selected pages

Use context_dev_scrape_markdown and dataforseo_v3_on_page_content_parsing_live for deeper analysis:

When to extract full pages:

  • Competitor's top-ranking content for a target topic (to understand depth and angle)
  • Pages that rank for queries you want to target (to understand what Google rewards)
  • Content that gets high engagement or links (to understand format patterns)

What to analyze in extracted content:

DimensionQuestions to answer
DepthHow thorough is the coverage? What subtopics are included/excluded?
FreshnessWhen was it last updated? Are examples and data current?
AudienceWho is it written for? (beginner/advanced, role, industry)
AngleWhat perspective or opinion does it take?
FormatHow is information structured? (listicle, guide, comparison, tutorial)
ConversionWhat CTA or next step does it offer?
GapsWhat questions does a reader still have after reading?

5. Validate topic patterns

Use dataforseo_v3_content_analysis_search_live to validate broader patterns:

  • Confirm whether a content gap is real (no one covers it) vs. just missed by your competitor sample.
  • Check content volume and freshness for a topic to assess saturation.
  • Identify emerging subtopics with growing coverage.

6. Synthesize opportunities

For each identified opportunity, document:

  • The gap: what's missing, superficial, or underserved
  • The evidence: which competitors were checked, what they cover vs. don't
  • The audience: who would benefit from this content
  • The differentiation: why the target can serve this better than competitors
  • The format: recommended content type and structure
  • The priority: based on audience value, competitive vacuum, and feasibility

Output

Return a structured content intelligence brief:

Coverage matrix

Topic / QuestionCompetitor 1Competitor 2Competitor 3TargetGap type
...✅ deep⚠️ shallow❌ absent❌ absentFull gap
...✅ deep✅ deep✅ deep⚠️ outdatedFreshness gap
...❌ absent❌ absent❌ absent❌ absentMarket gap

Priority opportunities

#OpportunityGap typeEvidenceAudience valueDifferentiation angleRecommended formatPriority
1.....................

Content brief backlog

For the top 3–5 opportunities, provide actionable briefs:

Brief: [Topic]

  • Target query/question: ...
  • Audience: ...
  • Angle that differentiates from competitors: ...
  • Key subtopics to cover: ...
  • Format and structure: ...
  • Evidence/data to include: ...
  • Conversion path: ...
  • Success metric: ...

Competitive patterns

Observations about competitor content strategy (publishing cadence, preferred formats, topic clusters, content quality trends).

Evidence gaps

What couldn't be verified, what additional research would help, and confidence levels.

Tips for better results

  • Compare, don't copy: The goal is finding whitespace, not replicating what works for competitors.
  • Think in questions: Frame gaps as buyer questions that lack good answers.
  • Check the SERP, not just competitor sites: Sometimes the gap exists because no one ranks well for a query (use with SEO Keyword Insights skill).
  • Look for format gaps: Sometimes the information exists but the format is wrong (text wall when a comparison table would serve better).
  • Consider the full journey: Map content to funnel stages; gaps at one stage may be more valuable than gaps at another.
  • Freshness is a strategy: Outdated competitor content is an opportunity even if the topic is "covered".
  • Non-obvious competitors: Include content publishers (blogs, media sites) not just product competitors.

Failure handling

  • If SandBase is unavailable or unauthorized, report the failed capability and ask the user to connect or authorize SandBase; do not silently substitute a direct provider API.
  • If scraping is blocked (authentication, paywall, JavaScript-heavy), note the gap and work with available metadata and search results.
  • If competitors have minimal public content, note the early-market signal and suggest alternative research approaches (Reddit, community forums, sales conversations).
  • If the topic is highly saturated, shift focus from "topic gaps" to "angle gaps", "depth gaps", or "format gaps".

Quality gate

Before delivering, verify that:

  • The coverage matrix is based on actual page analysis, not assumptions about competitor content.
  • Each opportunity has specific evidence of the gap (not just "they don't cover X").
  • Differentiation angles are tied to the target's actual capabilities or positioning.
  • Content briefs are specific enough for a writer to execute without additional research.
  • The priority considers audience value and business impact, not just ease of creation.
  • Competitive analysis is fair — acknowledge where competitors do well, not just where they're weak.

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 Competitor Content Intelligence AI skill do?

Analyze competitor content coverage and uncover differentiated content opportunities using web search, page extraction, and DataForSEO content analysis. Use when asked for competitor content research, editorial gap analysis, content brief inputs, or topic whitespace.

Why use Competitor Content Intelligence on TypingMind?

Because you install it once and use it with any model. Competitor Content Intelligence 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 Competitor Content Intelligence in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/sandbaseai/sandbase-skills/tree/main/marketing/competitor-content-intelligence. 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 Competitor Content Intelligence?

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 Competitor Content Intelligence?

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

Is the Competitor Content Intelligence AI skill free?

Yes. It is published on GitHub by sandbaseai 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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