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Product Marketing Context

Community
mia-ppp
product-marketing-context

Establish reusable PMM understanding from a user goal, company folder, mixed materials or existing context. Use for context setup, research inventory, synthesis, consequential gaps and context readiness, whether research is substantial, partial or absent. Routes domain work to existing specialists and assembles proposed context in .agents/product-marketing-context.md. For research methods, see customer-research; for governed approval and persistence, see ssot-context-loop.

Overview

Publishermia-ppp
RepositoryPMM-Skills
Skill nameproduct-marketing-context
Stars
76
Forks
2
Bundled files
2
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.

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

Installation

Install the Product Marketing Context 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/product-marketing-context
  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/product-marketing-context .claude/skills/product-marketing-context
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Marketing Context 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 Product Marketing Context 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 Product Marketing Context 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.

Product Marketing Context

Establish what is known, interpreted, assumed, conflicting and missing before strategy or execution. Own intake, cross-domain synthesis, decision-specific readiness, research routing and integration of returned findings.

Boundaries and contracts

Read ../_shared/evidence-gaps.md, ../_shared/ssot-consumption.md and ../_shared/context-establishment.md before establishing context. Use its inventory, provenance, contradiction, gap, handoff, return, readiness and proposal contracts. Preserve source IDs, evidence labels, approval and limitations.

Do not perform specialist methodologies, select a market, generate personas, choose positioning/pricing, or author final messaging. Route those to their existing owners. Only ssot-context-loop writes canon, decisions, manifest and reconciler state. This is a bounded understanding workflow, not a GTM orchestrator.

Workflow

1. Resolve goal, scope and existing context

Identify the company/project, product, applicable region/segment and intended research question, decision or deliverable. Reuse supplied answers. Ask only for unresolved information that changes scope or the next consequential choice. A folder without a goal can be inventoried; do not declare readiness or prescribe an extensive study before the goal is clear.

Load applicable manifest-mapped SSOT and approved decisions first. Check .agents/product-marketing-context.md and legacy .claude/product-marketing-context.md. Keep existing content and full bootstrap profiles intact. On a scoped refresh, change only relevant context and flag consequential dependencies. Do not silently move a legacy file, replace approved claims, or infer approval from file existence.

2. Inventory mixed materials

Accept folders/files, pasted statements and accessible links: interviews/calls, surveys, CRM/win-loss, support/sales feedback, product docs/demos, decks, analyst reports, competitive research, pricing, positioning/messaging, personas, journey, SSOT and historical approvals. Codebase extraction and conversation remain input modes; neither is the default substitute for company evidence.

Create the source inventory before synthesis. Identify originals/derivatives, dates, source roles, scope, coverage, readability and reviewed portions. Inspect goal-relevant sources; keep unreadable/inaccessible/uninspected material visible. Do not fabricate extraction or claim comprehensive review from partial inspection. Reuse existing research and raw stores before gathering anything new.

3. Extract and synthesize candidate understanding

Record source-linked evidence separately from interpretations, assumptions, historical decisions, current approved decisions and unknowns. A deck establishes what it says, not that its claims are validated. User statements need scope and authority before they establish an approved decision. Preserve exact quotes.

Integrate existing domain findings; route raw material requiring domain analysis to its owner. Relate findings across domains without inventing their conclusions. For example, different buyer priorities by segment may be supported; selecting one segment remains market-entry-brief's decision. Classify disagreements using the shared contradiction register. Resolve apparent scope/time/definition conflicts only with demonstrated evidence; route unresolved factual/decision conflicts. Never favor newer or repeated wording over approved canon.

4. Diagnose decision-specific readiness

Identify what the actual goal needs. Assess support, authority and health separately for each requirement. Use exactly PROCEED, PROCEED PROVISIONALLY or HOLD THIS DECISION with the shared meanings. For provisional work, record the assumption, risk, allowed scope and validation trigger. A hold names the affected decision, gap, owner and closure criteria; continue unrelated work. Evidence sufficiency never grants human approval. Missing approval is a separate dependency. Avoid completeness scores or forcing irrelevant context sections.

5. Route consequential research gaps

Consolidate overlapping gaps into the smallest useful backlog, retaining context and lineage. Rank by what changes if wrong, uncertainty, urgency and effort. Retrieve → analyze existing evidence → clarify definitions → request internal facts → collect new research. Use the shared owner table and HND contract; one primary owner per question, contributors reuse the same evidence.

  • Substantial research: synthesize first; route only unanswered domain analysis or consequential gaps, not a default new research program.
  • Partial research: identify supported versus assumed/conflicting/missing context; route the gaps that change the user's goal and defer non-blocking ones.
  • Starting from scratch: establish problem/product boundaries and constraints; route customer/category/competitive exploration before requiring an accepted market, complete persona or positioning. External market/category/opportunity research goes to market-intelligence; competitive landscape/facts/changes to competitor-profiling. Market-entry-brief owns entry evaluation and where/whether to enter. Then return evidence for market choice, personas within that scope, and strategy. Do not create circular prerequisites.

Handoffs do not authorize outreach or spending. If an owner is unavailable, keep its gap explicit and name the next action; do not silently absorb its methodology.

6. Integrate specialist returns

Check each return's scope, source locators, method, limitations and GAP completion criteria. Preserve supporting/opposing evidence and the original evidence/approval status. A report or increased citation count does not close a gap. Update answered, partial/inconclusive/unavailable work and reassess readiness. Follow up narrowly or state a stopping condition. Never loop until a preferred conclusion appears. Retain historical findings with supersession links instead of erasing them.

7. Assemble proposed PMM context

Read references/context-workspace-template.md when creating/updating the workspace. Keep the familiar headings below; append registers and references to existing domain artifacts. Reference covered approved context; retain full uncovered bootstrap personas, committee/anti-persona detail and attribute sources. Do not flatten them into summary rows. Missing applicable context stays Gap.

Prepare PRP records for supported portions with wording, destination, findings, assumptions/conflicts, domain owner, permitted use, pending validation and the human decision required. Do not wait for every non-blocking gap. Held choices remain unresolved, not disguised as settled proposals.

8. Review and hand off to SSOT

Show the inventory coverage, integrated context, readiness, consequential gaps, contradictions and concrete proposals for review. Save the provisional workspace when authorized, without changing canon. Corrections or saving do not approve it. No SSOT: hand proposals to ssot-context-loop /setup and await Gate 2. Existing SSOT: hand findings/proposals to /scan, Gate 1, /propose and Gate 2. Only SSOT persists approved changes. After an approved return, record replacement references while retaining pending/rejected/superseded proposal history. Tell the user which context is provisional and which approved references readers can use. Do not imply that every saved section is cleared for execution.

Sections to Capture

1. Product Overview

  • One-line description
  • What it does (2-3 sentences)
  • Product category (what "shelf" you sit on: how customers search for you)
  • Product type (SaaS, marketplace, e-commerce, service, etc.)
  • Business model and pricing

2. Target Audience

  • Primary audience plus alternates, each labeled Sourced, Assumed, or Gap. Keep alternates that lack proof; see ../_shared/segment-selection.md
  • Target company type (industry, size, stage)
  • Target decision-makers (roles, departments)
  • Primary use case (the main problem you solve)
  • Jobs to be done (2-3 things customers "hire" you for)
  • Specific use cases or scenarios

3. Personas

Reuse existing profiles from icp-and-buyer-personas; do not rebuild approved personas during context setup or refresh. Keep the summary below for scanning. Preserve full bootstrap profiles using that skill's attribute format, including source/version, approval, provenance/confidence and gaps. With applicable SSOT, reference its approved profiles instead of duplicating covered canonical facts. Do not collapse profiles into a summary or message map when updating the context. For B2B buying committees, capture:

  • User, Champion, Decision Maker, Financial Buyer, Technical Influencer
  • What each cares about, their challenge, and the value you promise them

4. Problems & Pain Points

  • Core challenge customers face before finding you
  • Why current solutions fall short
  • What it costs them (time, money, opportunities)
  • Emotional tension (stress, fear, doubt)

5. Competitive Landscape

  • Direct competitors: Same solution, same problem (e.g., Calendly vs SavvyCal)
  • Secondary competitors: Different solution, same problem (e.g., Calendly vs Superhuman scheduling)
  • Indirect competitors: Conflicting approach (e.g., Calendly vs personal assistant)
  • How each falls short for customers

6. Differentiation

  • Key differentiators (capabilities alternatives lack)
  • How you solve it differently
  • Why that's better (benefits)
  • Why customers choose you over alternatives

7. Objections & Anti-Personas

  • Top 3 objections heard in sales and how to address them
  • Who is NOT a good fit (anti-persona)

8. Switching Dynamics

The JTBD Four Forces:

  • Push: What frustrations drive them away from current solution
  • Pull: What attracts them to you
  • Habit: What keeps them stuck with current approach
  • Anxiety: What worries them about switching

9. Customer Language

  • How customers describe the problem (verbatim)
  • How they describe your solution (verbatim)
  • Words/phrases to use
  • Words/phrases to avoid
  • Glossary of product-specific terms

10. Brand Voice

  • Tone (professional, casual, playful, etc.)
  • Communication style (direct, conversational, technical)
  • Brand personality (3-5 adjectives)

11. Proof Points

  • Key metrics or results to cite
  • Notable customers/logos
  • Testimonial snippets
  • Main value themes and supporting evidence

12. Goals

  • Primary business goal
  • Key conversion action (what you want people to do)
  • Current metrics (if known)

13. Positioning

Supplied by positioning-strategy as a referenced proposal or approved input. Approved mapped SSOT remains authoritative for covered positioning; this section supplies uncovered provisional context. Saving it does not approve strategy or modify canonical SSOT.

  • Statement: one sentence in the form "[Product] is a [category + one short differentiating phrase] for [segment]."
  • Derivation trace: Category, Differentiating phrase, Segment, and Trigger (the event that starts the search), each with its source and label
  • Capability table: every differentiating capability, with what it does for the buyer, proof, buyer voice, and label

messaging-framework uses approved mapped SSOT capabilities where applicable and this table for uncovered provisional context. If the section is empty, approved SSOT inputs remain available; recommend positioning-strategy only for missing positioning.

14. Persona message map

Supplied by icp-and-buyer-personas as a referenced proposal or approved input. messaging-framework uses approved mapped SSOT persona messaging where applicable, and this map for uncovered provisional context. Preserve evidence labels and proposal/approval status; the map does not change canon.

  • Persona: role-based name with the buying role in brackets, such as (champion) or (financial buyer)
  • What matters to them: 1 to 2 short phrases
  • Message: the core message in 1 to 2 sentences
  • Proof label: Sourced, Assumed, or Gap for the proof behind the message

Output rules

Open internal output with Inputs and assumptions. End with ranked Evidence gaps, including all assumptions/gaps and suggested owners/milestones when unknown. No invented proof, source versions, quotes or approvals. No em dashes.

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 Product Marketing Context AI skill do?

Establish reusable PMM understanding from a user goal, company folder, mixed materials or existing context. Use for context setup, research inventory, synthesis, consequential gaps and context readiness, whether research is substantial, partial or absent. Routes domain work to existing specialists and assembles proposed context in .agents/product-marketing-context.md. For research methods, see customer-research; for governed approval and persistence, see ssot-context-loop.

Why use Product Marketing Context on TypingMind?

Because you install it once and use it with any model. Product Marketing Context 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 Product Marketing Context in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/mia-ppp/PMM-Skills/tree/main/skills/product-marketing-context. 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 Product Marketing Context?

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 Product Marketing Context?

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

Is the Product Marketing Context 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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