Product Specify logo

Product Specify

Organization
tikalk
product-specify

Use when you want guided trade-off analysis, multi-option comparison, or structured product decision facilitation before documenting. Class boots only suggest capture — they never write drafts; running this skill writes the lightweight PDR draft.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameproduct-specify
Stars
144
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 tikalk on GitHub. Read the source before you install it.

Installation

Install the Product Specify 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-specify
  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-specify .claude/skills/product-specify
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Specify 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 Specify 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 Specify 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-specify

What this skill does

Transforms a high-level product idea into documented Product Decision Records (PDRs) through interactive exploration and trade-off analysis.

Key insight: Discussion and exploration happen before committing to formal documentation. The goal is to surface trade-offs, validate assumptions, and make informed decisions collaboratively.

Output: Individual PDR-{NNN}.md files (status Proposed) in .adlc/drafts/pdr/ with an auto-generated pdr.md index.

When to use

Note: Class boots detect decisions and suggest this skill — running it is how the capture happens (it writes the lightweight draft to .adlc/drafts/). Use it for interactive deep-dive exploration — when you want guided trade-off analysis, multi-option comparison, or structured decision facilitation before documenting.

  • New product from scratch
  • Major product pivots
  • Documenting verbal decisions formally
  • Team onboarding — walking through product rationale

When NOT to use

  • Existing product (use /product-init instead)
  • Minor PDR updates (use /product-clarify instead)

Execution Steps

Phase 0: Environment Setup

bash
sh: scripts/bash/setup-product-specify.sh [--json]
ps: scripts/powershell/setup-product-specify.ps1

Requires: the product-clarify skill (provides pdr-lib.sh): adlc-cli skills add tikalk/adlc-team-skills --skill product-clarify

Setup output (JSON):

json
{
  "REPO_ROOT": "/path/to/project",
  "PDR_DRAFTS_DIR": "/path/to/project/.adlc/drafts/pdr",
  "PRD_FILE": "/path/to/project/PRD.md",
  "next_pdr": "001"
}

The script reports next_pdr — the next free PDR slot (status line Next PDR: PDR-{NNN}); Phase 6 numbers the new PDRs from it

Phase 1: Feature-Area Decomposition (Optional)

Analyze the product for distinct business domains. Auto-decompose if multiple domains detected. Use --no-decompose to skip.

Present detected areas:

markdown
## Detected Feature Areas

| # | Feature Area | Key Domains | Rationale |
|---|--------------|-------------|-----------|
| 1 | **Auth** | Authentication, Authorization | Core user entry |
| 2 | **Core** | User Management, Profiles | Core data |
| 3 | **Business** | Payments, Subscriptions | Revenue domain |

Reply: Y to confirm, n for monolithic, or suggest changes.

Threshold:

  • ≤3 areas: Auto-approve
  • 4-6 areas: Confirm with user
  • 6 areas: Suggest grouping

Phase 2: Product Analysis

Extract product drivers:

  1. Problem Drivers: Core problem, who experiences it, current workarounds
  2. Market Drivers: Target segments, competitive landscape, trends
  3. Business Drivers: Revenue model, scaling expectations, strategic importance
  4. Constraint Drivers: Technology mandates, budget, team skills, regulatory
  5. Load Constitution: Read {REPO_ROOT}/docs/adlc/memory/constitution.md (legacy {REPO_ROOT}/.adlc/memory/constitution.md fallback — dual-read) if either exists
  6. Check Existing Docs: Scan README.md, AGENTS.md, CONTRIBUTING.md for context

Phase 3: Product Exploration (Interactive)

For each major decision area, present options and facilitate discussion:

Decision areas (5-7 key decisions):

  1. Problem Scope
  2. Target Personas
  3. Solution Approach (build vs buy vs partner)
  4. Monetization
  5. Go-to-Market
  6. Success Metrics

Exploration format:

markdown
## Product Decision: [Decision Area]

**Context**: [Why this decision matters]

**Options**:
| Option | Description | Trade-offs |
|--------|-------------|------------|
| A | [Option A] | Pros: [X] / Cons: [Y] |
| B | [Option B] | Pros: [X] / Cons: [Y] |

**Recommended**: Option [X]

**Questions**:
1. [Question about constraints]
2. [Question about trade-off priorities]

Rules:

  • Present one decision area at a time
  • Always provide a recommended option with reasoning
  • Ask targeted questions to surface hidden requirements
  • Skip decisions already determined by context or constitution
  • Limit to 5-7 key decisions

Phase 4: Cross-Feature-Area Pre-Analysis

During exploration, watch for:

PatternDetectionAction
Shared PersonasSame user type in multiple areasNote for cross-area metadata
Priority TensionsAreas prioritize differently⚠️ Flag potential conflict
Feature OverlapSimilar features in different areas⚠️ Flag for consolidation
Metric ConflictsSame metric, different targets⚠️ Flag for alignment

Phase 5: Decision Documentation

After each decision is confirmed, create a PDR file.

PDR file format (individual file, YAML frontmatter — use the shared template):

Use the template at {REPO_ROOT}/.agents/skills/product/templates/pdr-template.md as the canonical PDR format. The template includes YAML frontmatter (status, date, owner, category, feature-area, title) as the single source of truth for index generation, plus the standard PDR body sections (Context, Decision, Consequences, Alternatives, Links).

markdown
---
status: proposed
date: YYYY-MM-DD
owner: [User/AI collaboration]
category: Feature
feature-area: [core | business | growth | ...]
title: [Decision Title]
---

# PDR-[NNN]: [Decision Title]

## Context
**Problem/Opportunity:**
[Clear description]

**Market Forces:**
- [Market factor 1]
- [Customer feedback]

## Decision
**Decision Statement:**
[Clear statement of what was decided]

**Rationale:**
[Why this option was chosen]

### Consequences
#### Positive
- [Benefit 1]

#### Negative
- [Trade-off 1]

#### Risks
- [Risk with mitigation]

### Success Metrics
| Metric | Target | Measurement Method |
|--------|--------|-------------------|
| [Metric] | [Target] | [Method] |

### Alternatives Considered
#### Option A: [Alternative Name]
**Description:** [Brief description]
**Trade-offs:** [Neutral comparison]

### Constitution/Vision Alignment
| Principle | Alignment | Notes |
|-----------|-----------|-------|
| [Vision Principle] | ✅ Compliant / ⚠️ Deviation | [Explanation] |

Phase 6: Write PDR Files and Regenerate Index

  1. Number PDRs sequentially: Start from PDR-001 for new projects, or continue from the next free slot the setup script computes (next_pdr in its JSON/status output) — never from "the highest existing PDR number" guessed by inspection: a stemmed record (e.g. PDR-002-amendment-2.md) is easy to miss, and its slot is still taken
  2. Write individual files: {REPO_ROOT}/.adlc/drafts/pdr/PDR-{NNN}.md
  3. Regenerate index: {REPO_ROOT}/.adlc/drafts/pdr/pdr.md

Index format:

markdown
# Product Decision Records

## PDR Index

| ID | Feature-Area | Category | Status | Date | Owner |
|----|--------------|----------|--------|------|-------|
| PDR-001 | System | Target Market | Proposed | 2026-03-09 | User/AI |
| PDR-002 | Auth | Primary Persona | Proposed | 2026-03-09 | User/AI |

---

*Individual PDR files: PDR-*.md in this directory*

Phase 7: Summary Report

markdown
## Feature Area Decomposition Summary

### Feature Areas Identified: 3

| # | Feature Area | PDRs Created |
|---|--------------|--------------|
| 1 | System-Level | PDR-001: Target Market |
| 2 | Auth | PDR-002: Primary Persona, PDR-003: Authentication Approach |
| 3 | Business | PDR-004: Pricing Model, PDR-005: Payment Integration |

### Next Steps
1. Review PDRs with /product-clarify
2. Generate PRD.md with /product-implement

PDR Numbering Rules

  • Number from the next free slot the setup script computes (next_pdr), never from "the highest existing number" guessed by inspection: a stemmed record (e.g. PDR-002-amendment-2.md) is easy to miss, and its slot is still taken
  • The slot scan reads every PDR-* stem in {REPO_ROOT}/.adlc/drafts/pdr/ and published memory (docs/adlc/memory/pdr/) — the same source next_pdr reports
  • PDR IDs are zero-padded to 3 digits
  • Never reuse numbers

Key Rules

Exploration First

  • Do NOT generate PRD directly from product description
  • Engage in discussion to validate assumptions
  • Surface trade-offs before committing to decisions
  • Allow iteration — user can revisit earlier decisions

Constitution Compliance

  • PDRs must align with constitution principles
  • Flag conflicts between product requirements and constitution
  • Constitution violations require explicit override with justification

Incremental PDRs

  • Create focused PDRs — one decision per PDR
  • Link related PDRs when decisions interact
  • Defer decisions that can be made later
  • Mark provisional decisions that may need revision

Configuration

  • PDR_DRAFTS_DIR — {REPO_ROOT}/.adlc/drafts/pdr
  • PDR_INDEX — {REPO_ROOT}/.adlc/drafts/pdr/pdr.md
  • PRD_FILE — {REPO_ROOT}/docs/adlc/product/PRD.md
  • CONSTITUTION — {REPO_ROOT}/docs/adlc/memory/constitution.md (legacy .adlc/memory/constitution.md fallback)

12-Factor Alignment

  • Factor III (Mission Definition): Defines the product mission before execution
  • Factor XI (Directives as Code): PDRs are version-controlled decision records

Common Rationalizations

RationalizationReality
"Let's just write the PRD directly."PRDs without PDRs lack traceable rationale. Decisions become undocumented assumptions.
"We already know what to build."Even "obvious" decisions have alternatives. Documenting them prevents future reversal.
"Exploration takes too long."A 10-minute discussion now prevents weeks of rework later.

Red Flags

  • Generating PRD before PDRs are accepted — /product-implement requires Accepted status; Proposed PDRs will be skipped.
  • Skipping the constitution check — misaligned decisions propagate into the PRD and become expensive to fix.
  • No alternatives documented — a PDR without alternatives is a statement, not a decision.

Verification

  • Setup script returns valid JSON with all paths
  • .adlc/drafts/pdr/ directory exists
  • At least one PDR-*.md file created with status "Proposed"
  • pdr.md index auto-generated with correct table
  • Constitution alignment checked (if constitution exists)
  • Cross-feature-area conflicts flagged
  • No duplicate PDR IDs
  • Each PDR has at least 2 alternatives documented

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 Specify AI skill do?

Use when you want guided trade-off analysis, multi-option comparison, or structured product decision facilitation before documenting. Class boots only suggest capture — they never write drafts; running this skill writes the lightweight PDR draft.

Why use Product Specify on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/tikalk/adlc-team-skills/tree/main/skills/product/product-specify. 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 Specify?

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 Specify?

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

Is the Product Specify 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.

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