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Market Intelligence

Community
mia-ppp
market-intelligence

Use when the user wants a market overview, external market intelligence, category analysis, or emerging opportunity investigation. Research market structure, demand conditions, category conventions, and testable opportunity hypotheses. For where/whether to enter, see market-entry-brief; for choosing our category/frame, see positioning-strategy; for competitor facts and landscapes, see competitor-profiling.

Overview

Publishermia-ppp
RepositoryPMM-Skills
Skill namemarket-intelligence
Stars
76
Forks
2
Bundled files
3
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.

  • 3 bundled files

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

  • Open source

    Published by mia-ppp on GitHub. Read the source before you install it.

Installation

Install the Market 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/mia-ppp/PMM-Skills/tree/main/skills/market-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/mia-ppp/PMM-Skills.git /tmp/PMM-Skills
mkdir -p .claude/skills
cp -r /tmp/PMM-Skills/skills/market-intelligence .claude/skills/market-intelligence
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Market 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 Market 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 Market 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.

Market Intelligence

Observe and synthesize external market conditions. Return reusable evidence, interpretations, and research implications. Strategy owners propose strategy; SSOT and explicit human approval establish approved canon.

Before starting

  • Resolve the active project/product scope. Keep other projects' evidence and approval references out of this return; IDs are project-scoped.
  • Read ../_shared/evidence-gaps.md and ../_shared/ssot-consumption.md.
  • Read ../_shared/opportunity-discovery.md for broad opportunity or repositioning questions. Generate business-use-case candidates before discussing product opportunities; classify workloads, requirements, problems, and triggers separately.
  • Read ../_shared/context-establishment.md when available. It owns Phase 1 evidence, readiness, and handoff/return semantics. If unavailable, use the compatible minimum in references/research-return.md. Read that reference for domain evidence checks and the return template on every run.
  • Load relevant manifest-mapped approved context. Record source/version, authority, health, and gaps. Bootstrap context stays provisional, never canon.
  • Accept research handoffs or standalone requests. Preserve originating HND/GAP, SRC/FND IDs, exact question, completion criteria, constraints, and return destination.
  • Establish question/intended use, market/problem boundary, geography, and time horizon. Ask only for missing facts that materially change the work. Exploration needs useful scope, not an accepted market or approved persona.
  • Inventory supplied evidence and prior research first. Disclose inaccessible, unreadable, excluded, and uninspected material instead of claiming full coverage.

Choose the smallest useful mode

Read the relevant section of references/modes.md. Do not run all modes merely to fill a report.

ModeExact job and triggersOutput
Market overviewExplain market structure, demand conditions, drivers, constraints, and changes. “What changed in this market?” “Reconcile these reports.”Scoped synthesis with dated findings and reconciled definitions.
Category analysisDescribe how solution categories are defined, understood, discovered, evaluated, and changing. “What does this category mean to buyers?”Category-definition and terminology map with evaluation evidence.
Emerging opportunity investigationInvestigate changes that could create newly addressable needs. “What deserves investigation?” “What would falsify this opportunity?”Testable hypotheses, counterevidence, and validation tasks.

Modes can iterate. Category uncertainty may narrow a market overview. Opportunity investigation can request category clarification or competitive verification. Keep shared finding IDs rather than duplicating studies.

Research and synthesis

  1. Retrieve existing information, analyze unprocessed evidence, and clarify definitions before requesting internal facts or collecting new research.
  2. Choose sources/methods for the question. Separate observation, source report, interpretation, assumption, and approved decision.
  3. Record provenance, scope, method, meaningful denominators, source lineage, supporting/opposing evidence, limitations, and explicit unknowns.
  4. Explain apparent disagreements only when evidence demonstrates scope, time, version, or definition differences. Otherwise retain the contradiction and test.
  5. State what evidence supports and cannot establish. Separate confidence in an observation from confidence in its commercial implications.
  6. Assess completion criteria and evidence readiness for the affected action. Return the smallest useful investigation, never automatic strategy.

Ownership and consumers

NeedOwner and boundary
Company-specific TAM/SAM/SOM, candidate scoring, where/whether to entermarket-entry-brief. Supply definitions, anchors, and constraints without selecting markets.
Customer jobs, behavior, demand explanations, interviews, VoCcustomer-research. Reuse findings and request primary validation there.
ICP, personas, who to target, buying groupicp-and-buyer-personas. An observed population is not our selected target.
Alternative universe, competitor facts/offers/claims/changescompetitor-profiling. Reuse dossiers and request focused verification.
Our category/frame and strategic differentiationpositioning-strategy. Describe conventions without choosing our category.
Messaging architecturemessaging-framework. Research terminology does not authorize message changes.
Pricing/packaging and willingness-to-pay conclusionspricing-strategy. Attention or spending is not willingness to pay.
GTM, investment, or roadmap decisionsgo-to-market-strategy and relevant business/product decision owners. Return hypotheses, not commitments.
Cross-domain integration, consolidated gaps, goal-relative readinessproduct-marketing-context. Return domain analysis without becoming an orchestrator.
Approved canon, approval, persistencessot-context-loop. Human approval remains required for canonical changes.

Never write canonical files, decisions, manifests, or state.md. Market/category findings may support living evidence proposals. Accepted market boundaries may use SSOT-owned scoped decision/reference records. Our category choice remains a positioning proposal for its existing core home. Suggested destinations are unapproved and grant no permission to persist.

Deliver and save

Return to the originating PMC workflow or named requester. Attach the specialist packet from research-return.md to the domain artifact. An inconclusive result may complete research without making the downstream decision ready.

When saving is requested or workflow-authorized, use the user's existing research location for a noncanonical artifact. Do not create an intelligence SSOT or write into this repository during production runs. A research question or cadence does not authorize outreach, spending, installed monitoring, or sending alerts.

Output rules

  • Open internal deliverables with Inputs and assumptions.
  • Use Sourced / Assumed / Gap with kind, confidence/rationale, and limitations.
  • Expose derivations and labeled inputs; Derived is not a fourth evidence tag.
  • Preserve relevant thin-evidence hypotheses and say how to test them.
  • End with ranked Evidence gaps: top five in the body, every remainder in an appendix. Give owners and resolution milestones, explicitly marking suggested assignments.
  • Use no em dashes. Keep sentences short and bullets led by outcomes.

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 Market Intelligence AI skill do?

Use when the user wants a market overview, external market intelligence, category analysis, or emerging opportunity investigation. Research market structure, demand conditions, category conventions, and testable opportunity hypotheses. For where/whether to enter, see market-entry-brief; for choosing our category/frame, see positioning-strategy; for competitor facts and landscapes, see competitor-profiling.

Why use Market Intelligence on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mia-ppp/PMM-Skills/tree/main/skills/market-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 Market 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 Market Intelligence?

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

Is the Market Intelligence AI skill free?

Yes. It is published on GitHub by mia-ppp 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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