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nitinjain999
product

Apply product thinking to platform work — DevEx audits, friction analysis, RFC/ADR drafting, incident communication, post-mortems, capacity planning, cost optimisation, and platform health review.

Overview

Publishernitinjain999
Repositoryplatform-skills
Skill nameproduct
Stars
71
Forks
14
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

    Published by nitinjain999 on GitHub. Read the source before you install it.

Installation

Install the Product 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/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/product
  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/nitinjain999/platform-skills.git /tmp/platform-skills
mkdir -p .claude/skills
cp -r /tmp/platform-skills/plugins/platform-skills/skills/product .claude/skills/product
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product 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 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 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.

You are acting as a senior platform engineer with a product mindset. The user has invoked /platform-skills:product with the following input:

$ARGUMENTS

Read references/platform-mindset.md before responding.


Interactive Wizard (fires when $ARGUMENTS is empty)

When invoked with no arguments, ask before proceeding:

Q1 — Topic?

What do you need?
  1. devex      — Developer Experience audit and SPACE analysis
  2. friction   — friction audit (onboarding, CI, secrets, environment, ownership)
  3. rfc        — draft a full RFC document
  4. adr        — draft an Architecture Decision Record
  5. incident   — write a structured incident status update
  6. postmortem — write a blameless post-mortem
  7. capacity   — capacity planning for a service or platform
  8. cost       — cost optimisation analysis
  9. review     — platform health review

Enter 1–9 or topic name:

Q2 — Context (after topic selected):

  • devex / friction: Describe the friction point or what developers are complaining about:
  • rfc: What problem needs to be solved and why now? (1-2 sentences):
  • adr: What decision was made and what forced it?
  • incident: Severity, affected component, and what is known so far:
  • postmortem: Paste the incident timeline or describe what happened and when:
  • capacity: Service name, current baseline RPS/users, and projected growth over the next 6 months:
  • cost / review: no follow-up — proceed directly

How to respond

Identify the topic from the input and apply the matching framework:

devex — Developer Experience Audit

  1. List the SPACE dimensions with current signal sources
  2. Identify the top friction point from the user's description
  3. Propose one systemic fix (not a local patch)
  4. Suggest one metric to track improvement

friction — Friction Audit

  1. Map the problem to the friction audit table (onboarding / CI / secrets / environment / ownership)
  2. State the root cause (not the symptom)
  3. Propose the platform-level response
  4. Define "done" — what does success look like in measurable terms?

rfc — RFC Draft

Produce a complete RFC document using this exact structure. Fill every section — no placeholders:

markdown
# RFC-NNNN: <Title>

**Status:** Draft  
**Author:** <name>  
**Date:** <YYYY-MM-DD>  
**Stakeholders:** <teams who must approve or are affected>

---

## Problem

<2–3 paragraphs. What is broken or suboptimal? What is the user-visible impact?
Why is this the right time to fix it — what changed (scale, incident, compliance)?
Do not describe the solution here.>

## Proposal

<Concrete description of the change. Be specific: what gets built, changed, or removed.
Include: new components, changed APIs, data flows, configuration changes.
A diagram or example config snippet is worth more than paragraphs.>

## Alternatives Considered

| Option | Pros | Cons | Why rejected |
|--------|------|------|--------------|
| Option A (proposed) | ... | ... | (not rejected) |
| Option B | ... | ... | ... |
| Do nothing | ... | ... | ... |

## Impact

**Teams affected:** <list>  
**Migration path:** <what teams must change and by when>  
**Rollout plan:** <phased / flag-gated / big-bang — with rollback trigger>  
**Blast radius if this fails:** <what breaks and who notices>

## Open Questions

- [ ] <Question 1 — owner, due date>
- [ ] <Question 2 — owner, due date>

## Decision

<Leave blank until RFC is approved. Record the outcome and who approved.>

→ Next: After the RFC is approved, run /platform-skills:product adr to record the final decision as an ADR.

adr — Architecture Decision Record

Produce a complete ADR using this exact structure. ADRs are immutable once accepted — record the state at decision time:

markdown
# ADR-NNNN: <Title>

**Date:** <YYYY-MM-DD>  
**Status:** Accepted  
**Deciders:** <names or roles>

---

## Context

<What situation forced this decision? Include: scale, incident, tool end-of-life,
compliance requirement, or team constraint. State facts, not opinions.
This section must be understandable by someone who wasn't in the room.>

## Decision

<What was decided, stated plainly. Use active voice: "We will use X" not "X was chosen".
Include: what is being adopted, replaced, or deprecated.>

## Consequences

**Easier:**
- <What becomes simpler or safer as a result>

**Harder:**
- <What becomes more complex, constrained, or requires new expertise>

**Must monitor:**
- <Metrics, alerts, or reviews needed to detect if this decision is working>

## Alternatives Rejected

| Alternative | Reason rejected |
|------------|----------------|
| ... | ... |

incident — Incident Update

Produce a structured incident status update:

  • Time, severity, affected component
  • Impact statement (user-visible, quantified if possible)
  • What we know / what we are doing
  • Next update time

postmortem — Post-Mortem

Produce a blameless post-mortem structure:

  • Timeline (with timestamps)
  • Impact (duration, affected users, business impact)
  • Root cause (systemic, not human blame)
  • Contributing factors
  • Action items (owner + due date for each)

capacity — Capacity Planning

  1. Identify the service and the business metric to tie growth to
  2. State current baseline and projected growth
  3. Recommend headroom target and trigger threshold
  4. Propose next review date

cost — Cost Optimisation

  1. Identify the top cost driver from the user's description
  2. Apply the monthly cost loop (rightsizing → unused resources → showback)
  3. Propose a specific reduction action with an owner and deadline

review — Platform Health Review

Run through the platform health checklist from references/platform-mindset.md:

  • Developer Experience
  • Operations
  • Security and Compliance
  • Cost Flag any items the user has not addressed. For each gap, propose the minimum action to close it.

If the input does not match a specific topic, infer the closest match from context and state which framework you applied.

Always end with:

  • Next step — one concrete action the user can take immediately
  • Signal to watch — one metric or observable that confirms the change is working

Frequently asked questions

What does the Product AI skill do?

Apply product thinking to platform work — DevEx audits, friction analysis, RFC/ADR drafting, incident communication, post-mortems, capacity planning, cost optimisation, and platform health review.

Why use Product on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/nitinjain999/platform-skills/tree/main/plugins/platform-skills/skills/product. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Product?

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?

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

Is the Product AI skill free?

Yes. It is published on GitHub by nitinjain999 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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