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SKILL verified MIT Self-run

Product Init

skill-tikalk-adlc-team-skills-product-init · by tikalk

Reverse-engineer Product Decision Records (PDRs) from an existing codebase and documentation using multi-agent feature-area analysis (brownfield). Use when documenting product decisions inferred from an already-built product.

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Install

$ agentstack add skill-tikalk-adlc-team-skills-product-init

✓ scanned · ✓ verified, works with Claude Code, Cursor, and more.

Security review

✓ Passed

No issues found. Passed automated security review. · v0.1.0 How review works →

  • Prompt-injection patterns
  • Secret / credential exfiltration
  • Dangerous shell & filesystem operations
  • Untrusted network calls
  • Known-malicious package signatures

What it can access

  • Network access No
  • Filesystem access No
  • Shell / process execution No
  • Environment & secrets No
  • Dynamic code execution No

From automated source analysis of v0.1.0. “Used” means the capability is present in the source — more access means more to trust, not that it’s unsafe.

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Reliability & compatibility

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Declared compatibility

Claude CodeClaude Desktop

Compatibility is declared by the source manifest. End-to-end runtime verification is coming, see below.

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About

product-init

What this skill does

Reverse-engineers product decisions from an existing product using a three-phase analysis pipeline:

  1. Discovery Agent: Scans each feature-area for raw product signals (code, docs, pricing)
  2. Pattern Agent: Classifies signals into PDR categories, scores strategic importance
  3. Synthesis Agent: Cross-feature-area analysis, flags inconsistencies

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

When to use

  • Existing product with no formal PDRs
  • Brownfield codebase needs product documentation
  • Team onboarding — walking through product rationale
  • Post-acquisition or inherited codebase

When NOT to use

  • New product (use /product-specify instead)
  • Minor PDR updates (use /product-clarify instead)

Execution Steps

Phase 0: Environment Setup

Run setup script to resolve paths and detect feature-areas:

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

Setup output (JSON):

{
  "REPO_ROOT": "/path/to/project",
  "PDR_DRAFTS_DIR": "/path/to/project/.adlc/drafts/pdr",
  "PRD_FILE": "/path/to/project/PRD.md",
  "feature_areas": ["core", "business", "growth"],
  "next_pdr": "001"
}

Create directories:

mkdir -p "{REPO_ROOT}/.adlc/drafts/pdr"
mkdir -p "{REPO_ROOT}/.adlc/product"

Phase 1: Feature-Area Detection

Detect feature-areas from three sources:

| Source | Detection Pattern | |---|---| | Directory Structure | src/auth/, features/payments/, modules/ | | Documentation | README sections, existing PRD, ROADMAP | | Pricing Tiers | Starter/Pro/Enterprise feature mapping |

Present detected areas:

## Detected Feature-Areas

| # | Feature-Area | Sources | Evidence |
|---|--------------|---------|----------|
| 1 | **Core** | Directory + Docs | src/users/, README "Core Features" |
| 2 | **Business** | Directory + Pricing | src/billing/, pricing.md tiers |

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

Threshold Logic:

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

Phase 2: Discovery Agent (Per Feature-Area)

For each feature-area in order:

  1. Scan directory for monetization signals, user flows, features
  2. Analyze documentation (README, PRD, pricing)
  3. Identify pricing tier mappings
  4. Document evidence

Progress report per area:

Discovery Agent: business feature-area
├── Directory signals: 4
├── Documentation signals: 2
├── Pricing signals: 1
└── Status: ✓ Completed

Phase 3: Pattern Agent (Per Feature-Area)

For each feature-area:

  1. Categorize signals into PDR categories (Problem, Persona, Scope, Metric, etc.)
  2. Score strategic importance (0.0-1.0)
  3. Check for duplicates against existing PDRs
  4. Identify cross-area candidates

Phase 4: Synthesis Agent (Cross-Feature-Area)

1. Cross-Area Pattern Detection:

{
  "pattern_id": "P001",
  "pattern_name": "Admin Persona",
  "feature_area_presence": {"core": true, "business": true},
  "is_cross_area": true
}

2. Inconsistency Detection:

  • Priority conflicts
  • Duplicate problems
  • Inconsistent metrics
  • Generate flags (embedded in affected PDRs)

3. PDR Generation: High-strategic patterns (>0.7) and cross-area patterns (≥2 areas)

Phase 5: Write Individual PDR Files

For each discovered PDR:

  1. Assign ID: PDR-{NNN} using next available number
  2. Write file: {REPO_ROOT}/.adlc/drafts/pdr/PDR-{NNN}.md

PDR file format (individual file, no frontmatter):

## PDR-001: [Decision Title]

### Status

**Discovered** (Inferred from existing product)

### Date

YYYY-MM-DD

### Owner

[Inferred from codebase/authors]

### Category

[Problem | Persona | Scope | Metric | Prioritization | Business Model | Feature | NFR]

### Feature-Area

[core | business | growth | ...]

### Cross-Feature-Area Metadata
- **Appears in**: [business, growth]
- **Cross-area count**: 2
- **Is cross-area pattern**: ✓

### ⚠️ Inconsistency Flags
*None* (or flag details if detected)

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

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

### Decision
**Decision Statement:**
[Clear statement]

**Rationale:**
[Why this option]

### 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]

Phase 6: Regenerate PDR Index

Generate {REPO_ROOT}/.adlc/drafts/pdr/pdr.md from all PDR-*.md files:

# For each PDR-*.md file, extract ID, title, status, date
# Build index table

Index format:

# Product Decision Records

## PDR Index

| ID | Feature-Area | Category | Status | Date | Owner |
|----|--------------|----------|--------|------|-------|
| PDR-001 | business | Business Model | Discovered | 2026-01-20 | [Inferred] |
| PDR-002 | core | Persona | Discovered | 2026-01-20 | [Inferred] |

---

## Cross-Feature-Area Analysis Summary

### Cross-Area Patterns
| Pattern | Feature-Areas | PDR |
|---------|---------------|-----|
| Admin Persona | core, business | PDR-002 |

### Inconsistencies Flagged
| Flag ID | Type | PDRs Affected | Severity |
|---------|------|---------------|----------|
| FLG-001 | Priority Conflict | PDR-003 | Medium |

---

*Individual PDR files are in this directory (PDR-*.md)*

Phase 7: Output Summary

## Product Init Complete ✓

### Execution Stats
- **Feature-areas analyzed**: 3
- **Discovery Agent runs**: 3
- **Pattern Agent runs**: 3
- **Synthesis Agent runs**: 1

### PDRs Generated
| Category | Count | Cross-Area |
|----------|-------|------------|
| Business Model | 2 | ✓ |
| Persona | 3 | ✓ |
| Problem | 2 | |
| Prioritization | 1 | |
| **With Inconsistency Flags** | **2** | |

### Next Steps
1. Review PDRs: `{REPO_ROOT}/.adlc/drafts/pdr/`
2. **Resolve inconsistencies**: Run `/product-clarify`
3. Generate PRD: Run `/product-implement`

PDR Numbering Rules

  • Scan {REPO_ROOT}/.adlc/drafts/pdr/ for existing PDR-*.md files
  • Extract numeric suffix, find maximum
  • Next PDR = max + 1, zero-padded to 3 digits (PDR-001, PDR-002, ...)
  • Never reuse numbers, even for deleted PDRs

State Management

State file: {REPO_ROOT}/.adlc/product/state.json

{
  "version": "1.0",
  "command": "product-init",
  "created_at": "2026-01-20T10:00:00Z",
  "phase": "completed",
  "feature_areas": [
    {"id": "core", "name": "Core", "progress": {"discovery": "completed", "pattern": "completed"}}
  ],
  "pdrs_generated": 9,
  "cross_area_patterns": 5,
  "inconsistencies": 2
}

Configuration

  • PDR_DRAFTS_DIR{REPO_ROOT}/.adlc/drafts/pdr (individual PDR files)
  • PDR_INDEX{REPO_ROOT}/.adlc/drafts/pdr/pdr.md (auto-generated index)
  • PRD_FILE{REPO_ROOT}/PRD.md
  • STATE_FILE{REPO_ROOT}/.adlc/product/state.json

12-Factor Alignment

  • Factor III (Mission Definition): Discovers the "what & why" behind existing code
  • Factor IX (Traceability): Every inferred decision is documented with evidence

Common Rationalizations

| Rationalization | Reality | |-----------------|---------| | "The code is self-documenting." | Code shows how, not why. PDRs capture rationale that code cannot. | | "I'll just read the README." | READMEs describe features, not decisions. PDRs capture the decision tree. | | "Brownfield products don't need PDRs." | Every product has implicit decisions. Making them explicit prevents repeated mistakes. |

Red Flags

  • Generating PDRs without codebase evidence — brownfield PDRs must be grounded in actual code/docs, not fabricated rationales.
  • Skipping inconsistency flags — cross-area conflicts are real signals, not noise.
  • Assigning "Proposed" status to discovered PDRs — brownfield PDRs are Discovered, not proposed.

Verification

  • [ ] Setup script returns valid JSON with all paths
  • [ ] .adlc/drafts/pdr/ directory exists
  • [ ] At least one PDR-*.md file created with status "Discovered"
  • [ ] pdr.md index auto-generated with correct table
  • [ ] State file written to .adlc/product/state.json
  • [ ] Inconsistency flags embedded in affected PDRs (not separate files)
  • [ ] Cross-feature-area analysis summary in index
  • [ ] No duplicate PDR IDs

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

Install and usage instructions live in the source repository linked above.

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Versions

  • v0.1.0 Imported from the upstream source.