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Lifecycle Play Advisor

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deanpeters
lifecycle-play-advisor

Diagnose where a product sits in its lifecycle and which play fits — extend, replace, or retire. Use when a product is fading and you need the call, not just the worry.

Overview

Publisherdeanpeters
RepositoryProduct-Manager-Skills
Skill namelifecycle-play-advisor
Stars
7K
Forks
831
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Lifecycle Play Advisor 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/deanpeters/Product-Manager-Skills.git /tmp/Product-Manager-Skills
mkdir -p .claude/skills
cp -r /tmp/Product-Manager-Skills/skills/lifecycle-play-advisor .claude/skills/lifecycle-play-advisor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Lifecycle Play Advisor 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 Lifecycle Play Advisor 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 Lifecycle Play Advisor 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.

Lifecycle Play Advisor

Purpose

Work out what to do with a product that has stopped growing. Three plays are available — extend, replace, retire — and this skill gets you to the right one through diagnosis rather than debate, then hands you to whichever skill executes it.

Most teams argue the play before establishing the stage. The argument is unwinnable that way, because nobody has agreed on what's actually happening to the product. Four questions fixes that.

This skill is deliberately willing to say "nothing yet." A mature product throwing off margin with manageable support cost doesn't need a play; it needs to be left alone and watched.

Input

Works best with: The product, and what makes you think something needs to change.

Also useful: Revenue trend and over how long, support load, what customers say, whether an internal driver (cost, capacity, strategy) is really behind the question, and what investment appetite exists.

Anything supplied with the invocation itself — text after the skill name, a pasted context dump, or an appended ARGUMENTS: line — counts as answers already given. Use it and skip whatever it covers; don't re-ask.

Arriving empty-handed? That works too. The diagnosis runs on questions a PM can answer from what they already know — no report-pulling required. "I don't know" is a valid answer and becomes a labeled assumption in the recommendation.

Example invocations:

  • Our reporting module has been flat six quarters and people want to rebuild it. Extend, replace, or retire?
  • Leadership wants to kill our parts module. Walk me through whether that's right.

Key Concepts

The Three Plays

  • Extend — add a variant or capability to the existing line. The product keeps running; you add alongside. Cheapest, lowest risk, most often skipped because it isn't exciting.
  • Replace — ship a successor and phase the old one out. GTM and EOL run simultaneously, for two products that compete with each other. The expensive play.
  • Retire — phase out with no successor of your own. Customers land elsewhere, possibly with a competitor. The goal becomes losing the product without losing the customer.

And the fourth answer that isn't a play: harvest — stop investing, keep running, set a review date.

Diagnosis Before Play

The seven transition questions establish stage; the pressure source discriminates the plays. Run both before recommending anything, and show your work — a recommendation the user can audit is one they can defend to someone else.

The Extension Bias

Default toward the cheapest play that addresses the actual pressure. Extension is under-considered in almost every room, because replacement is more interesting to build and retirement is more decisive to announce. Test extension explicitly, and record why it failed when it does.

Anti-Patterns (what this is NOT)

  • Not a growth strategy tool. Where the next tranche of growth comes from is a different question — see ansoff-matrix and organic-growth-advisor.
  • Not a kill switch. "Nothing yet" and "harvest" are real outcomes.
  • Not the execution. It routes to the skill that runs the chosen play.

Facilitation Source of Truth

Use workshop-facilitation as the interaction protocol. Give the heads-up at the start — four questions, about fifteen minutes — and let the user dump context to skip ahead.


Application

This skill asks up to 4 adaptive questions, then recommends a play with its reasoning, its hazards, and a route out.


Question 1 of 4 — What product, and what makes you think something's wrong?

Agent asks:

"Which product are we looking at, and what prompted the question? The prompt matters as much as the product — flat revenue, rising support costs, a competitor move, and an exec remark lead to very different diagnoses."

Listen for whether the trigger is internal or external. An internal trigger — manufacturing wants the line, engineering wants to rewrite, finance wants the cost out — is legitimate, but it means the customer-facing case for change is weak and the transition has to be carried entirely by you. Name that early; it changes what the play costs.


Question 2 of 4 — Where is it actually in the lifecycle?

Agent asks:

"Let's establish the stage. Seven quick reads — yes or no, gut answers are fine:

  1. Is defending market share still profitable?
  2. Are loyalty efforts no longer retaining users?
  3. Are legacy support costs becoming unsustainable?
  4. Should we be running migration campaigns instead of loyalty ones?
  5. Are we losing money on certain distribution channels?
  6. Are our value-add bundles losing effectiveness?
  7. Is our data shifting from predictive to transitional?"

Score it:

Yes countStageImplication
0-1Mature and healthyNo play needed. Invest or leave alone
2-3Mature and softeningExtension territory; watch quarterly
4-5Crossing into declinePick a play deliberately, now
6-7In declineReplace or retire; extension likely just delays

Say the count back and name the pattern, not just the number. Yeses clustered on 5 and 6 (channel and price) point somewhere very different from yeses on 3 and 7 (support cost and data) — the first pattern is a distribution problem wearing a product costume.


Question 3 of 4 — What's actually driving the pressure?

Agent asks:

"Three sources. Which is loudest?

  1. Demand-side — customer needs shifted, segments moved, a competitor differentiated
  2. Supply/cost-side — components, production cost, support load, or a strategy change
  3. Capability-side — the technology is obsolete or the architecture has hit its limit

Pick one as primary, even if two apply."

Why this discriminates:

PressurePoints towardBecause
Demand-sideExtendThe core still solves a real problem for someone — find them
Supply/cost-sideReplaceThe problem is your economics, not the customer's need
Capability-sideReplace or RetireDepends on whether the need survives the technology

Name the trap out loud: supply-side pressure arrives at the roadmap disguised as a customer problem. "We need to rebuild this" often means "our costs are bad." That's a legitimate reason for a replacement — but saying it plainly changes what success looks like and who has to carry the transition.


Question 4 of 4 — Test the extension play

Agent asks:

"Before I recommend anything more expensive, four questions on extending what you have:

  1. Is there a segment a variant could serve?
  2. Is there a capability you could add without re-architecting?
  3. Would a repackage or rebrand reach a different buyer?
  4. Is the decline in the product, or in the channel or price around it?"

Question 4 catches the most expensive misdiagnosis in the set: a healthy product condemned because the distribution channel stopped working or the bundle lost its edge. Fixing the lever is far cheaper than replacing the product — and the broken lever carries straight over to the successor if you don't.

If all four are no, extension is genuinely off the table, and now the user has the "no, because…" on record for when someone asks in three months.


Final Step: Recommend the play

Deliver this structure:

markdown
## Lifecycle Play Recommendation: [Product]

**Stage:** [stage] — [n]/7 transition questions, clustered on [which]
**Primary pressure:** [demand / supply-cost / capability] — [evidence]
**Trigger was:** [internal / external] — [what that means for the transition]

### Recommended Play: [Extend / Replace / Retire / Harvest / Nothing yet]

**Because:** [two or three specifics from their answers]

**Extension test:** [passed and how, or failed and why — always state it]

**What this play costs:** [rough shape]

**What you're accepting by choosing it:** [the tradeoff, plainly]

### Hazards to plan for
- [The 2-3 hazards that apply to this play, from the seven]

### Assumptions I made
- [Anything answered "I don't know"]

Then offer numbered next steps:

"Where next?

  1. Run the retirement properlyeol-readiness-advisor for the go/no-go, or eol-process for the whole thing
  2. Build the replacement risk register — see product-lifecycle-plays, especially the seven hazards
  3. Plan the extensionorganic-growth-advisor for which growth path the variant serves
  4. Run this across the whole line — the portfolio worksheet in product-lifecycle-plays

Pick a number, combine them, or tell me what you'd rather do."

On a Replace recommendation, always add one line: "A replacement is two funded workstreams — a launch and a retirement. If the retirement isn't funded, you've chosen an extension with extra steps."


Examples

Example A — SaaS, the answer is "your channel is broken, not your product"

PM: "Our Parts module has been flat for five quarters. Leadership wants to fold it into the core platform and stop selling it separately."

Agent (Q1): "What prompted it — a customer signal, or an internal one?"

PM: "Internal. It's the smallest line item and someone noticed."

Agent: "Noted — that's an internal trigger, which means there's no customer pressure to help carry a transition. Let's see whether the product agrees with leadership."

Agent (Q2): runs the seven questions. Yeses on 5 (losing money on certain channels) and 6 (bundles losing effectiveness). Everything else no.

Agent: "2 out of 7 — mature and softening, not declining. And the pattern is the interesting part: both of your yeses are on channel and price. Nothing about the product itself came back negative. Support cost is fine, retention is fine."

Agent (Q3): "What's driving the pressure?"

PM: "Honestly? We sell it through the same motion as everything else and it doesn't fit. Reps don't lead with it."

Agent (Q4): extension test. Segment — yes, distributors have asked for a read-only tier. Capability — yes, a read-only view is minor work. Repackage — yes, that's exactly the ask. Channel or product — channel.

Recommendation: Extend, and fix the channel. Stage says mature-softening. Both decline signals sit on distribution and pricing, not the product. A read-only distributor tier addresses a segment that has actively asked, and it's small work. Folding it into the core would have retired a healthy product to solve a sales-motion problem — and the sales-motion problem would have survived the merge.

What you're accepting: a distributor tier means supporting a new buyer type. Small, but not zero.


Example B — Industrial, the answer is "replace, and fund the retirement"

PM: "Manufacturing wants the NFA-200 line for NFA-500 capacity. So we're retiring the NFA-200."

Agent (Q1): "That's an internal trigger — a capacity constraint, not a customer need. Worth flagging up front: the customer-facing case for change is weak here, so your organization carries the entire transition. What's the revenue trend?"

PM: "Flat four quarters. Service revenue's actually growing."

Agent (Q2): runs the seven. Yeses on 4, 6, 7, and a soft 1. Support costs fine, retention strong, channels profitable.

Agent: "4 out of 7 — mature and softening, not in decline. Your installed base is loyal, under contract, and service margin is healthy. That matters, because it means this isn't a product failing. It's a factory floor you want back."

Agent (Q3): "Primary pressure?"

PM: "Supply side. Manufacturing capacity."

Agent (Q4): extension test. Segment — partially; a modular I/O variant would open bigger jobs. Capability without re-architecture — no, needs a new backplane. Repackage — no, industrial buyers specify on capability. Channel or product — neither.

Agent: "Here's the thing worth saying out loud: the extension your customers would want is the NFA-500. That's a strong signal the replacement play is right — and it raises the question you need to answer before committing. Is the NFA-500 a drop-in for the installed base?"

PM: "...I'd assumed so. I don't actually know."

Recommendation: Replace — with that question as a gate.

Hazards to plan for: internal misalignment (medium), cannibalization (deliberate, low impact), and poor EOL management (high impact) — which is entirely gated on the drop-in question. If the NFA-500 needs different mounting or site work, you don't have a migration path, you have a project, and an EOL date would be a promise you can't keep.

Assumption labeled: drop-in compatibility unverified.

And the line that always goes on a Replace: a replacement is two funded workstreams — a launch and a retirement. Retrofit engineering unfunded means you've chosen an extension with extra steps.


Common Pitfalls

Pitfall 1: Recommending a Play Before the Stage Is Established

Symptom: The user names a play in their first message and the conversation optimizes it.

Consequence: You've validated an opinion rather than run a diagnosis, and the expensive assumption inside it goes unexamined.

Fix: Run Q2 even when the user arrives certain. It takes two minutes and it either confirms them or saves them a year.


Pitfall 2: Letting the Count Replace the Pattern

Symptom: "4 out of 7, so you're in decline."

Consequence: Four yeses clustered on channel and price mean something completely different from four on support cost and architecture. The count alone routes people wrong.

Fix: Always name which questions came back yes, and say what that cluster means.


Pitfall 3: Accepting the Internal Trigger at Face Value

Symptom: "Manufacturing wants the line" becomes "the product should be retired."

Consequence: You run a full retirement when an End of Sale, a price change, or a repackage would have satisfied the actual need more cheaply.

Fix: Name the trigger as internal, then ask what specifically it needs. Often the cheaper move delivers it.


Pitfall 4: Skipping the Extension Test on an Obvious Case

Symptom: The product is clearly dying, so Q4 gets waved through.

Consequence: No written record of why extension failed. In three months someone asks, and the answer is a shrug.

Fix: Ask all four regardless. On a genuinely dead product it takes ninety seconds and produces a defensible "no, because…"


Pitfall 5: Treating "Nothing Yet" as a Non-Answer

Symptom: Every run produces a play, because producing a play feels like producing value.

Consequence: Healthy mature products get projects they didn't need, funded from budget that had somewhere better to be.

Fix: 0-1 yeses means no play. Say so, set a review date, and stop.


References

Related Skills

These stand on their own — none is a prerequisite for this skill, and this skill isn't a prerequisite for them.

External Frameworks

  • Product Life Cycle (PLC) — the five-stage economic model
  • PLC strategy grid — marketing-mix levers by stage

Provenance

  • Distilled from practitioner experience running product lifecycle transitions and product retirements across software, hardware, and regulated industries.

Frequently asked questions

What does the Lifecycle Play Advisor AI skill do?

Diagnose where a product sits in its lifecycle and which play fits — extend, replace, or retire. Use when a product is fading and you need the call, not just the worry.

Why use Lifecycle Play Advisor on TypingMind?

Because you install it once and use it with any model. Lifecycle Play Advisor 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 Lifecycle Play Advisor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/deanpeters/Product-Manager-Skills/tree/main/skills/lifecycle-play-advisor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Lifecycle Play Advisor?

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 Lifecycle Play Advisor?

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

Is the Lifecycle Play Advisor AI skill free?

It is published on GitHub by deanpeters. Check the repository for licensing terms. 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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