Product Implement logo

Product Implement

Organization
tikalk
product-implement

Use when accepted PDRs exist and PRD.md must be generated or updated.

Overview

Publishertikalk
Repositoryadlc-team-skills
Skill nameproduct-implement
Stars
144
Forks
2
Bundled files
7
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.

  • 7 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 Implement 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-implement
  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-implement .claude/skills/product-implement
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Product Implement 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 Implement 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 Implement 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-implement

What this skill does

Transforms accepted PDRs into a comprehensive, self-contained PRD.md using a three-phase DAG:

  1. Plan Agent: Analyze PDRs, detect feature-areas, generate customized DAG, get user approval
  2. Execute Agent: Generate sections per feature-area with mandatory checkpoint after Requirements
  3. Summarize Agent: Aggregate sections, resolve conflicts, produce unified PRD.md

Output:

  • docs/adlc/product/PRD.md — self-contained product requirements (the shared layout; legacy repo-root PRD.md stays read-compatible)
  • {REPO_ROOT}/.adlc/product/sections/{feature-area}/{section}.md — intermediate section files
  • Accepted PDRs moved to {REPO_ROOT}/docs/adlc/memory/pdr/ (the shared layout canonical memory root)

When to use

  • After /product-clarify has approved PDRs
  • After /product-init to document existing product
  • PDR updates requiring PRD regeneration

When NOT to use

  • No Accepted PDRs (run /product-clarify first)
  • Minor PRD edits (edit PRD.md directly)

Pre-Flight Validation

Before starting, verify prerequisites:

  1. Check PDRs exist: {REPO_ROOT}/.adlc/drafts/pdr/PDR-*.md
  2. Check for Accepted PDRs: Count files with status "Accepted"
    • If zero: STOP and output:
      Cannot proceed: No Accepted PDRs found.
      Run /product-clarify to review and approve PDRs first.
    • If ≥1: Proceed

Three-Phase DAG Workflow

┌─────────────────────────────────────────────────────────┐
│ PHASE 1: PLAN (Plan Agent)                              │
│ Load PDRs → Detect Feature-Areas → Generate DAG → Approve│
└─────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────┐
│ PHASE 2: EXECUTE (Execute Agent)                        │
│ Overview → Problem → Goals → Metrics → Personas         │
│ → [REQUIREMENTS CHECKPOINT] ← MANDATORY USER APPROVAL   │
│ → NFRs → Out-of-Scope → Risks → Roadmap → PDR-Summary   │
└─────────────────────────────────────────────────────────┘
                            ↓
┌─────────────────────────────────────────────────────────┐
│ PHASE 3: SUMMARIZE (Summarize Agent)                    │
│ Read sections → Detect conflicts → Resolve → PRD.md     │
└─────────────────────────────────────────────────────────┘

Execution Steps

Phase 1: Plan

Step 1.1: Load and Analyze PDRs

  1. Read all PDR-*.md files from .adlc/drafts/pdr/
  2. Filter to Accepted status only
  3. Parse feature-area from each PDR
  4. Group PDRs by feature-area

Step 1.2: Detect Feature-Area Characteristics

CharacteristicDetection PatternDAG Customization
B2B SaaSEnterprise, admin, SSOInclude compliance sections
Consumer AppMobile, freemium, socialSimplify requirements
PlatformAPI, integrations, developerExpand NFRs
MarketplaceMulti-sided, transactionAdd business model sections

Step 1.3: Generate Customized DAG

Default DAG (all 15 sections):

Document Information → Executive Summary → Overview → Problem
→ Market Opportunity → Goals → Metrics → Personas
→ [CHECKPOINT: Requirements]
→ NFRs → Out-of-Scope → Risks → Investment
→ Roadmap → Go-to-Market → PDR-Summary

Section numbering (fixed):

    1. Document Information
  • 1.5 Executive Summary
    1. Overview
    1. The Problem
  • 3.5 Market Opportunity
    1. Goals & Objectives
    1. Success Metrics
    1. Personas
    1. Functional Requirements
    1. Non-Functional Requirements
    1. Out of Scope
    1. Risks & Mitigation
  • 10.5 Investment & Resources
    1. Roadmap & Milestones
  • 11.5 Go-to-Market Strategy
    1. PDR Summary

Step 1.4: Present Plan for Approval

markdown
## DAG Execution Plan

**Feature-Areas detected**: 3
**Total sections**: 15

**Feature-Area: Core**
**PDRs**: PDR-001, PDR-005, PDR-008
**DAG**: Document Info → Executive Summary → Overview → Problem
→ Market Opportunity → Goals → Metrics → Personas
→ [Requirements Checkpoint] → NFRs → Out-of-Scope → Risks
→ Investment → Roadmap → GTM → PDR-Summary

**Approve this plan?** [Yes/Modify/Cancel]

Step 1.5: Write state.json

json
{
  "version": "1.0",
  "phase": "plan_approved",
  "feature_areas": [
    {
      "id": "core",
      "name": "Core",
      "pdrs": ["PDR-001", "PDR-005"],
      "dag": ["document-info", "executive-summary", "overview", "problem", ...],
      "progress": {}
    }
  ],
  "checkpoint": {
    "enabled": true,
    "after_section": "requirements",
    "status": "pending"
  }
}

Phase 2: Execute

For each section in the DAG:

  1. Check dependencies — ensure all prerequisites completed
  2. Load section template — ../templates/sections/{section}.md
  3. Generate content — fill template with PDR-derived content
  4. Write section file — .adlc/product/sections/{feature-area}/{section}.md
  5. Validate — run scripts/bash/validate-prd.sh {section}.md
  6. Update state.json — mark section as "completed"

Section template usage (MANDATORY):

  • Read template FIRST
  • Fill ALL [PLACEHOLDERS]
  • NEVER generate from scratch

In-section diagrams (MANDATORY):

  • Use ```mermaid code blocks
  • Use flowchart keyword (NOT deprecated graph)
  • ASCII box-drawing characters are PROHIBITED
  • Diagrams embedded in their home sections
SectionDiagram TypeSubsection
2. OverviewFeature Hierarchy (flowchart TD)2.4
2. OverviewArchitecture (flowchart TB)2.5
6. PersonasUser Journey (journey)6.4
7. RequirementsReq Dependencies (flowchart LR)7.4
7. RequirementsFeature Dependencies (flowchart LR)7.5
11. RoadmapGantt Chart (gantt)11.1

Requirements Checkpoint (MANDATORY):

After generating Requirements section:

markdown
## CHECKPOINT: Requirements Section Complete

The Requirements section has been generated.

**Why checkpoint here?** Requirements shapes:
- NFRs (how requirements are met)
- Out-of-Scope (what's NOT required)
- Risks (technical feasibility)
- Roadmap (priority and sequencing)

**Options**:
A) Approve — Continue to remaining sections
B) Modify — Edit requirements, then continue
C) Restart — Regenerate from Problem phase
D) Cancel — Stop execution

Phase 3: Summarize

Step 3.1: Read All Sections FROM DISK

CRITICAL: Read each section file from filesystem. Do NOT use content from memory.

  1. Scan .adlc/product/sections/ for all .md files
  2. Read each file
  3. Validate: ≥20 lines, proper headers

Step 3.2: Detect Cross-Feature-Area Conflicts

Conflict TypeDetectionResolution
Duplicate requirementsSame requirement, different wordingStandardize to PDR terminology
Priority mismatchSame feature, different priorityDefer to PDR
Metric inconsistencySame metric, different definitionUse PDR definition

Step 3.3: Aggregate into PRD.md

CRITICAL: PRD.md MUST be SELF-CONTAINED.

  • ALL diagrams embedded IN-SECTION
  • ZERO reader-facing links to .adlc/ paths
  • Use in-document anchors only: [Section 2.4](#24-feature-hierarchy)
  • PDR references as plain text: plain-text PDR references (NOT linked)

PRD structure (must match template):

markdown
# Product Requirements Document: [Product Name]

## 1. Document Information
[Quick Stats, revision history, approval]

## 1.5 Executive Summary
[Business case, ROI, recommendation]

## 2. Overview
[Product description, scope]
### 2.4 Feature Hierarchy [MERMAID flowchart TD]
### 2.5 Architecture Overview [MERMAID flowchart TB]

## 3. The Problem
[Problem statement, validation evidence]

## 3.5 Market Opportunity
[TAM/SAM/SOM, competitive landscape]

## 4. Goals & Objectives
[Primary, technical, business goals traced to PDRs]

## 5. Success Metrics
[Adoption, engagement, quality]
### 5.5 Business Outcome Metrics
### 5.6 Financial Metrics

## 6. Personas
[Primary, secondary, anti-personas]
### 6.4 User Journey [MERMAID journey]

## 7. Functional Requirements [CHECKPOINT]
[User stories, REQ-XXX IDs, priority matrix]
### 7.4 Requirement Dependencies [MERMAID flowchart LR]
### 7.5 Feature Dependencies [MERMAID flowchart LR]

## 8. Non-Functional Requirements
[Performance, security, reliability, scalability]

## 9. Out of Scope
[Feature, technical, market exclusions]

## 10. Risks & Mitigation
[Risk summary, technical, market, operational]
### 10.4 Business Risks

## 10.5 Investment & Resources
[Team, budget, ROI, go/no-go criteria]

## 11. Roadmap & Milestones
### 11.1 Roadmap Overview [MERMAID gantt]
[Milestone details with demo sentences]

### 11.2 Milestone Gates & Progress
[Per milestone: done-means definition, feature rollup, gate table, issue/evidence status — sourced from milestone PDRs]

## 11.5 Go-to-Market Strategy
[Launch phases, pricing, messaging]

## 12. PDR Summary
[Key decisions, constitution alignment — NO external links]

Phase 4: PDR Lifecycle Management (MANDATORY)

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

Step 4.1: Move Accepted PDRs to Memory (atomic — script-driven)

Source the PDR lifecycle library and call move_pdr for each Accepted PDR. This performs an atomic mv (no copy-then-delete duplication risk) and regenerates both scopes' indexes automatically.

bash
source "{REPO_ROOT}/.agents/skills/product-clarify/scripts/bash/pdr-lib.sh"
# Or on Windows: . "{REPO_ROOT}/.agents/skills/product-clarify/scripts/powershell/pdr-lib.ps1"

for pdr_id in <list of Accepted PDR IDs>; do
  move_pdr "$pdr_id" drafts memory
done
  • PDRs with status "Accepted" are moved (not copied) from .adlc/drafts/pdr/ to docs/adlc/memory/pdr/.
  • Do NOT change status to "Completed" — keep status as "Accepted" so the index header ("Accepted PDRs only") remains truthful.
  • Proposed/Discovered PDRs remain in drafts (not moved).
  • Both drafts/pdr/pdr.md and memory/pdr/pdr.md indexes are regenerated by move_pdr.

Step 4.2: Generate Memory PDR Index (MANDATORY — script-driven)

The move_pdr call in Step 4.1 already regenerates the memory index. To manually regenerate (e.g., after bulk edits to PDR files):

bash
source "{REPO_ROOT}/.agents/skills/product-clarify/scripts/bash/pdr-lib.sh"
generate_pdr_index memory

This writes {REPO_ROOT}/docs/adlc/memory/pdr/pdr.md using a frontmatter-primary + heading-fallback parser that handles both ## (H2 legacy) and ### (H3 current) metadata. Blank cells trigger a stderr warning and defaults are applied — no silent blank rows.

The generated index has this format:

markdown
# Product Decision Records (Memory)

> Auto-generated by /product-implement. Accepted PDRs only.
> Source: docs/adlc/memory/pdr/PDR-*.md

## PDR Index

| ID | Feature-Area | Category | Status | Date | Owner | Title |
|----|--------------|----------|--------|------|-------|-------|
| PDR-001 | control-plane | Governance | Accepted | 2026-08-04 | User/AI collaboration | Lit Factory Operating Model |

This index is consumed by team-boot for session-start injection (similar to how CDR.md is used for team-level context).

Step 4.3: Update state.json

json
{
  "phase": "completed",
  "pdr_lifecycle": {
    "pdrs_promoted": [N],
    "memory_pdr_moved": true,
    "drafts_retained": true,
    "drafts_reason": "Proposed/Discovered PDRs remain",
    "memory_index_generated": true
  }
}

Phase 5: Final Verification

Before marking complete, verify ALL checks:

#CheckExpected
1Section files on diskN files in .adlc/product/sections/
2PRD.md existsYes
3PRD.md content size>200 lines
4PRD.md has all sectionsSections 1-12 + sub-sections
5PRD.md is self-contained0 .adlc/ links
6Diagrams embedded≥4 mermaid blocks
7Memory PDRs writtendocs/adlc/memory/pdr/PDR-*.md exist
8Memory PDR index generateddocs/adlc/memory/pdr/pdr.md exists with correct table
9state.json consistentAll sections "completed"

Gate Rule: If ANY check fails → do NOT mark as completed. Report failures.

Claiming Ledger Slots (do this before writing any numbered id)

A number computed from a stale base is wrong even when you compute it correctly — the working tree simply does not know what the merge target has already used. Check first, then read each number from the document; never eyeball it.

bash
# 1. Refuse to proceed on a stale base (returns 1 and explains).
source {REPO_ROOT}/.agents/skills/product-clarify/scripts/bash/pdr-lib.sh
require_current_base "${MERGE_TARGET:-origin/main}"

# 2. Read the next free number for each slot type.
next_record_number PDR "{REPO_ROOT}/.adlc/drafts/pdr" "{REPO_ROOT}/docs/adlc/memory/pdr"  # record id -> 138 (drafts + published)
next_prd_section  "{PRD_FILE}"   # `## 26.` heading  -> 27
next_req_id       "{PRD_FILE}"   # `REQ-216`         -> 217
next_feature_number "{PRD_FILE}" # `| 69 |` row      -> 70

Each scan is deliberately over-inclusive: a match anywhere in the document raises the result. That is the safe direction — an unused number is harmless, a reissued one collides. next_ad_delta_version in setup-architect.sh is the AD.md counterpart and belongs to architect-implement.

PDR Traceability Rules

  • Every section must reference source PDRs with ID
  • Every requirement (REQ-XXX) must trace to a PDR
  • No content without PDR backing
  • PDRs are source of truth for conflict resolution

Configuration

  • PDR_DRAFTS_DIR — {REPO_ROOT}/.adlc/drafts/pdr
  • PDR_MEMORY_DIR — {REPO_ROOT}/docs/adlc/memory/pdr
  • PRD_FILE — {REPO_ROOT}/docs/adlc/product/PRD.md
  • SECTIONS_DIR — {REPO_ROOT}/.adlc/product/sections
  • STATE_FILE — {REPO_ROOT}/.adlc/product/state.json

12-Factor Alignment

  • Factor III (Mission Definition): Compiles mission decisions into actionable requirements
  • Factor IV (Structured Planning): DAG orchestration separates planning from execution
  • Factor IX (Traceability): Every PRD element traces back to a PDR

Common Rationalizations

RationalizationReality
"I'll skip the checkpoint and just generate everything."Requirements shapes NFRs, Out-of-Scope, Risks, and Roadmap. Skipping the checkpoint risks cascading errors.
"The PRD can reference section files."PRD.md MUST be self-contained. External references break when section files are moved or deleted.
"I don't need to move PDRs to memory."Without promotion, drafts and memory diverge. The next clarify session sees stale data.

Red Flags

  • Generating PRD from non-Accepted PDRs — implement skips Proposed/Discovered; the PRD will be incomplete.
  • Writing PRD.md directly from PDRs — content MUST come from section files to ensure validation passed.
  • Missing the Requirements checkpoint — this is the cornerstone section; errors here cascade.
  • Leaving .adlc/ links in PRD.md — breaks self-containment; readers cannot follow internal paths.

Verification

  • Pre-flight: ≥1 Accepted PDR exists
  • Plan approved by user
  • state.json written with DAG
  • Each section file ≥20 lines
  • validate-prd.sh passes for each section
  • Requirements checkpoint approved by user
  • PRD.md >200 lines with all 15 sections
  • Zero .adlc/ links in PRD.md
  • ≥4 Mermaid diagrams embedded in-section
  • All requirements trace to PDRs
  • Accepted PDRs moved to docs/adlc/memory/pdr/
  • Final completion verification: all 8 checks pass

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 Implement AI skill do?

Use when accepted PDRs exist and PRD.md must be generated or updated.

Why use Product Implement on TypingMind?

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

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

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 Implement?

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

Is the Product Implement 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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