Product Boot logo

Product Boot

Organization
tikalk
product-boot

Use when product or feature work starts, or a PDR-worthy decision (scope, persona, monetization) emerges — injects the PDR index (docs/adlc/memory/pdr/, legacy .adlc/memory/pdr/ fallback) as session context and suggests the decision for capture via /product-specify (never writes drafts itself); invoked from team-boot's Class Boots catalog.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameproduct-boot
Stars
144
Forks
2
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the Product Boot 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/tikalk/adlc-team-skills/tree/main/skills/product/product-boot
  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/tikalk/adlc-team-skills.git /tmp/adlc-team-skills
mkdir -p .claude/skills
cp -r /tmp/adlc-team-skills/skills/product/product-boot .claude/skills/product-boot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Boot 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 Boot 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 Boot 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-boot

Overview

One of the five class boots surfaced by team-boot's Class Boots catalog. team-boot injects the always-relevant team context (constitution, CDR index, skills registry) at session start; this skill loads the product decision layer on demand — the Accepted PDR index from project memory — and pairs it with a capture suggestion so new product choices are routed to /product-specify rather than evaporating. The boot never writes files: it suggests the capture skill, and the user runs it.

Reading decisions and recording decisions are one loop: existing PDRs inform the work; decisions made during the work flow back into the same record system through the capture skill.

When to Use

Invoke at the START of a matching task — before planning the todo list and before implementation — so the PDR context informs planning. Never defer to session end.

Invoke when:

  • Starting product work (PRD generation, feature definition, roadmap planning, GTM decisions).
  • The session turns product-shaped mid-flight: feature scope, persona definition, monetization, pricing, or target-market choices emerge.
  • Reviewing an existing product's decision backdrop (/product-analyze, /product-roadmap benefit from the loaded index).

Core Process

Step 1: Locate the PDR Index

From the current working directory (do NOT walk up parent directories):

  1. Primary: docs/adlc/memory/pdr/pdr.md (generated by /product-implement; rows start with | PDR)
  2. Fallback: .adlc/memory/pdr/pdr.md (legacy layout, legacy)

If neither exists but docs/adlc/memory/pdr/PDR-*.md or legacy .adlc/memory/pdr/PDR-*.md files do, synthesize a lean table from each file (ID from filename; Feature-Area/Status/Title from frontmatter or first heading). If the directory is empty or absent, report 0 PDRs — never fabricate rows.

When no local memory index exists in the current working directory and the directory sits inside a workspace (detected via a .gitmodules marker in an ancestor), read the workspace root's docs/adlc/memory/ index instead (the dual-read order applies: docs/adlc/memory first, legacy .adlc/memory fallback).

Step 1b: Read the PDR Drafts Index

Check .adlc/drafts/pdr/ for PDR-*.md files with status: proposed in frontmatter. These are draft PDRs pending clarification. Collect ID / Title / Type / Status / Date from each file. If the directory is empty or absent, report 0 pending drafts.

Absent Context

If team-boot injected no team context this session (no Team Context & Decisions section in the first user message — unconfigured project or hook failure): say so in one line, emit the section heading with a 0 PDRs matched (no team context injected — run /team-setup) source line, and continue the task on the directly-read PDR index. Never treat a missing injection as an empty record set. Recovery: run /team-diagnose.

Step 2: Inject PDR Context (Output Contract)

Emit before the task answer:

markdown
## PDR Context

| ID | Feature-Area | Status | Title |
|--|--|--|--|
| AI triage | triage | Accepted | AI triage scoring |

_Searched N PDRs, K matched._

## Drafts Pending Review

| ID | Title | Type | Status | Date |
|----|-------|------|--------|------|
| (from .adlc/drafts/pdr/) |

_N pending drafts — run /product-clarify to review._
  • Render ID / Feature-Area / Status / Title from the index (the full index also carries Category, Date, Owner — read the individual PDR-*.md when a task matches a row).
  • N = total index rows; K = rows relevant to the current task. K MUST equal the table rows shown. 0 rows matched → emit the section heading + the _Searched N PDRs, K matched._ line only — no table. A 0-row table header collapses into unrendered single-line markdown; never emit one. Emit the section as markdown blocks — heading, table rows, and counts line each on their own lines.

Step 3: Suggest Product Decision Capture

TriggerAction
Feature scope change, in/out decisionsPDR → suggest /product-specify
Persona definition or revisionPDR → suggest /product-specify
Monetization, pricing, target-market choicePDR → suggest /product-specify
PDR-class decision already in the ledgerverify the suggestion was surfaced; if not, re-surface

Emit one line per detected decision, in the visible response:

markdown
_Suggested capture: <decision name> → run /product-specify._

Never write a draft (or any file) yourself — /product-specify writes the .adlc/drafts/pdr/ draft when the user runs it, and /product-clarify gates it afterwards.

Add/refresh rows in Team Context & Decisions (ID | Name | Type | Rel | Status | Clarify) for every PDR-class decision detected this session — including ones from before this boot was invoked. Mirror each decision as a task-list todo (suggest /product-specify → matching clarify skill at session end for drafts that exist); after code-modifying tasks, add a trailing todo to sweep Team Context & Decisions until Unrecorded: 0 pending · Unclarified: 0 drafts (a draft leaves Unclarified only via its clarify skill or an explicit user handoff to a named clarify or execute skill). At session end, deliver the clarify prompt naming each uncaptured decision (ID + capture skill) and each captured PDR draft in .adlc/drafts/pdr/ (ID + clarify skill); if the user defers clarify, mark those rows handed off.

Failure Handling

  • Missing index + missing records → emit the section heading + _Searched 0 PDRs, 0 matched._ only — no table — and continue the user's task; never block.
  • Unparseable index rows → skip malformed rows, note the skip count.

Red Flags

  • Fabricating PDR rows or inflating K beyond the table shown.
  • Injecting the index but ignoring the capture suggestion — the pairing is the point.
  • Writing a draft (or any file) yourself — the boot only suggests; /product-specify writes.
  • Walking up parent directories to find .adlc/.

Verification

  • PDR Context table emitted with _Searched N PDRs, K matched._ (K = table rows).
  • Detected PDR-class decisions added as Team Context & Decisions rows.
  • No files written; each detected PDR-class decision carries a _Suggested capture: … → run /product-specify._ line.

Frequently asked questions

What does the Product Boot AI skill do?

Use when product or feature work starts, or a PDR-worthy decision (scope, persona, monetization) emerges — injects the PDR index (docs/adlc/memory/pdr/, legacy .adlc/memory/pdr/ fallback) as session context and suggests the decision for capture via /product-specify (never writes drafts itself); invoked from team-boot's Class Boots catalog.

Why use Product Boot on TypingMind?

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

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

Which AI models can use Product Boot?

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 Boot?

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

Is the Product Boot AI skill free?

Yes. It is published on GitHub by tikalk 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇