agentCLAWHUBUnverified
Pdlc
AIFLC PDLC Family — 11 injectable workflow packages that guide AI coding agents through professional software delivery: idea evaluation → project initiation... Skill: Pdlc Owner: mbmd Summary: AIFLC PDLC Family — 11 injectable workflow packages that guide AI coding agents through professional software delivery: idea evaluation → project initiation... Tags: latest:1.0.0 Version history: v1.0.0 | 2026-07-07T16:51:06.102Z | auto - Initial release of the AIPDLC skill, offering 11 modular, injectable workflow packages for AI-assisted professional software delivery. - Covers the
OpenClaw
Rank
62
Safety
84
Downloads
1.2k
Updated
Oct 11, 2026
Version
1.0.0
Source
CLAWHUB
About
What it does, and when to use it.
Capability contract not published. No trust telemetry is available yet. 1.2K downloads reported by the source. Last updated 10/11/2026.
Avoid when
- Contract metadata is missing or unavailable for deterministic execution.
Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing
Public facts
Every fact links back to the source it came from.
- Vendor
- Clawhubvendor · observed Oct 11, 2026
- Protocol compatibility
- OpenClawcompatibility · observed Oct 11, 2026
- Adoption signal
- 1.2K downloadsadoption · observed Oct 11, 2026
- Latest release
- 1.0.0release · observed Jul 7, 2026
- Handshake status
- UNKNOWNsecurity
Install and run
Setup complexity: low.
clawhub skill install s177yte0b83dzbtys81qb8x7zh8a30wb:pdlc- Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
- Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.
Contract: missing
curl -s "https://www.xpersona.co/api/v1/agents/clawhub-mbmd-pdlc/snapshot"
Documentation
CLAWHUB
135,972 characters of source documentation, loaded on request.
Extracted files
5 files captured from the source.
ai-adlc/ai-adlc-rule-details/extensions/README.md
# AI-ADLC Extensions — How They Work
## Overview
Extensions are optional rule sets that add specialized architectural pattern guidance on top of the core AI-ADLC workflow. They activate via user opt-in during the workflow — only when your system needs a specific pattern.
---
## The Pattern
Each extension has **two files**:
```
extensions/{name}/
├── {name}.opt-in.md ← Lightweight prompt (always scanned at workflow start)
└── {name}.md ← Full rules (loaded ONLY if user opts in)
```
| File | Size | Loaded When | Purpose |
|------|:----:|-------------|---------|
| `*.opt-in.md` | Small | Always (at workflow start) | Presents the opt-in question to the user |
| `*.md` (rules) | Large | Only if user says "Yes" | Provides detailed design rules, verification criteria, and templates |
---
## The Flow
```
Workflow Start
│
▼
[Scan extensions/ folder — load ONLY *.opt-in.md files]
│
▼ (During relevant stage — Stage 5, 6, or 12)
│
[Present opt-in questions to user]
│
├── User says "Yes" ──► Load {name}.md (full rules)
│ └──► Enforce in subsequent stages
│ └──► Verify compliance at stage completion
│
└── User says "No" ──► Never load full rules
└──► Zero overhead; workflow proceeds normally
```
---
## Example Walkthrough: DDD Tactical Patterns
**Step 1 — Workflow Start:**
AI scans `extensions/` directory. Finds `ddd-tactical/ddd-tactical.opt-in.md`. Reads it (lightweight — just a question prompt and applicability criteria).
**Step 2 — During Stage 12 (Component Design):**
The workflow presents the opt-in question:
> "Would you like to apply DDD Tactical Patterns?
> This adds: Aggregate design rules, Domain Events catalog, Anti-Corruption Layers, Value Objects.
> (a) Yes (b) No"
**Step 3a — User says Yes:**
- AI loads `ddd-tactical/ddd-tactical.md` (the full rules file)
- Those rules become **enforced constraints** for the current and remaining stages
- Example rule: "Every aggregate must define its consistency boundary explicitly"
- At stage completion, the AI verifies compliance and reports findings
- Non-compliance is a blocking finding — stage cannot complete until resolved
**Step 3b — User says No:**
- `ddd-tactical.md` is NEVER loaded into context
- No context/token budget consumed
- Workflow proceeds with standard component design from core rules
- No enforcement, no compliance check for DDD patterns
---
## Why This Design?
| Benefit | Explanation |
|---------|-------------|
| **Context-efficient** | Only load heavy rule files when actually needed — saves AI context budget |
| **Non-intrusive** | Core workflow works perfectly without any extensions activated |
| **User-controlled** | User decides which advanced patterns to apply — never forced |
| **Additive** | Extensions ADD rules on top of core workflow — never ai-adlc/README.md
# AI-ADLC (AI-Driven Architecture Design Life Cycle)
[](./LICENSE)
**Version:** 1.1.0
**Created By:** Maheri — [LinkedIn](https://www.linkedin.com/in/mohammad-maheri-8399565b)
**Inspired By:** [awslabs/aidlc-workflows](https://github.com/awslabs/aidlc-workflows) (MIT-0)
**License:** Apache 2.0 with Attribution Addendum — See `LICENSE` and `NOTICE`
---
## The AI-* PDLC Family
AI-ADLC is part of **AIFLC** (AI Full Life Cycle) — the AI-* PDLC Family of injectable workflow packages.
```
╔════════════════ PORTFOLIO LAYER · scope = MANY projects ════════════════╗
(optional)
AI-ILC ⇢ AI-PILC ⇢ AI-PPM
Decide it Initiate it Govern it (portfolio of N projects)
╚═════════════════════════════════╤═══════════════════════════════════════╝
│
AI-FLO Route it — package-to-package
│ flow on the edge between layers
╔════════════════ PROJECT LAYER · scope = ONE project ════════════════════╗
AI-POLC ──► AI-UXD ──► AI-ADLC ──► AI-DWG ──► AI-DLC v1 (build) ¹
Own it Design UX Design it Prepare it ▲
│
AI-POLC ⇄ AI-DLC v1 (back-and-forth)┘
AI-DLC v1 ⇢ AI-UXD+AI-POLC (feedback)
AI-GCE + AI-TGE ──── alongside AI-DLC v1 (continuous quality) ────►
Guard it Test it
╚═════════════════════════════════════════════════════════════════════════╝
¹ AI-DLC v1 = Amazon's open-source build lifecycle (not ours; we feed it).
```
| Layer | Package | Type | Input | Output |
|-------|---------|------|-------|--------|
| Portfolio | **AI-ILC** ² | Interactive workflow (lifecycle) | Raw idea | Approved Idea Brief / Feature Brief |
| Portfolio | **AI-PILC** | Interactive workflow (lifecycle) | Raw requirement | Project Initiation Package (PIP) |
| Portfolio | **AI-PPM** ³ | Adaptive portfolio engine | Multiple PIPs + Approved Idea Briefs | Portfolio register + cross-project prioritization & governance |
| Edge | **AI-FLO** ³ | Router / orchestration engine | Any package output marker | Routing decision + handoff to next package/layer |
| Project | **AI-POLC** ³ | Interactive workflow (lifecycle) | PIP | Product Backlog Package (PBP) |
| Project | **AI-UXD** ³ | Interactive workflow (lifecycle) | PIP + PBP | UX Design Package (UXP): personas/journeys, IA, user flows, design system + tokens, accessibility baseline |
| Project | **AI-ADLC** | Interactive workflow (lifecycle) | PIP + PBP + UXP | Architecture Package (AP) |
| Project | **AI-DWG** | One-time generator | AP + PBP + UXP | Ready-to-code development workspace (DW) |
| Project | **AI-GCE** | Adaptive governance engine | DW (AI-DWG output) | Compliance enforcement layer |
| Project | **AI-TGE** | Test governance engine | DW / build artifacai-dfe/README.md
# AI-DFE — AI-Driven Data Fabric
[](./LICENSE)
**Version:** 1.0.0
**Created By:** Maheri — [LinkedIn](https://www.linkedin.com/in/mohammad-maheri-8399565b)
**License:** Apache 2.0 with Attribution
---
## What Is AI-DFE?
AI-DFE is the data layer of the AI-* PDLC Family. It gathers the scattered markdown outputs every package produces, shapes them into structured JSON per consumer needs, and distributes them to one read-point — so dashboards, extensions, and reports get clean, machine-readable data without ever knowing where the raw files live.
**In one sentence:** AI-DFE turns the family's scattered, human-readable outputs into a single governed, machine-readable data surface — gather, shape, distribute.
**Tagline:** *Fabric it.*
---
## Family Position
AI-DFE is part of **AIFLC** (AI Full Life Cycle) and the **AI-* PDLC Family**. Like AI-FLO, it lives in **every family** as a continuous adaptive engine — but where FLO routes decisions, DFE fabrics data. It owns one folder, `pdlc-ws/data/`, and is its sole writer.
```
╔════════════════ PORTFOLIO LAYER · scope = MANY projects ════════════════╗
(optional)
AI-ILC ⇢ AI-PILC ⇢ AI-PPM
Decide it Initiate it Govern it (portfolio of N projects)
╚═════════════════════════════════╤═══════════════════════════════════════╝
│
AI-FLO Route it — package-to-package
│ flow on the edge between layers
╔════════════════ PROJECT LAYER · scope = ONE project ════════════════════╗
AI-POLC ──► AI-UXD ──► AI-ADLC ──► AI-DWG ──► AI-DLC v1 (build) ¹
Own it Design UX Design it Prepare it ▲
│
AI-POLC ⇄ AI-DLC v1 (back-and-forth)┘
AI-DLC v1 ⇢ AI-UXD+AI-POLC (feedback)
AI-GCE + AI-TGE ──── alongside AI-DLC v1 (continuous quality) ────►
Guard it Test it
╚═════════════════════════════════════════════════════════════════════════╝
¹ AI-DLC v1 = Amazon's open-source build lifecycle (not ours; we feed it).
```
| Layer | Package | Type | Input | Output |
|-------|---------|------|-------|--------|
| Portfolio | **AI-ILC** ² | Interactive workflow (lifecycle) | Raw idea | Approved Idea Brief / Feature Brief |
| Portfolio | **AI-PILC** | Interactive workflow (lifecycle) | Raw requirement | Project Initiation Package (PIP) |
| Portfolio | **AI-PPM** ³ | Adaptive portfolio engine | Multiple PIPs + Approved Idea Briefs | Portfolio register + cross-project prioritization & governance |
| Edge | **AI-FLO** ³ | Router / orchestration engine | Any package output marker | Routing decision + handoff to next package/layer |
| Project | **AI-POLC** ³ | Interactive workflow (lifecycle) | PIP | Product Backlog Package (PBP) |
| Project | **AIai-dwg/ai-dwg-rule-details/templates/examples/README.md
<!-- Copyright (c) 2026 Mohammad Maheri. Licensed under Apache 2.0. See LICENSE. Attribution required - see NOTICE. -->
---
generatedBy: AI-DWG
generatedVersion: "{version}"
source: "AI-ADLC AP (tech patterns) + AI-UXD UXP (UI component patterns)"
generatedOn: "{generation-date}"
ownership: hybrid
---
# Template: examples/ Directory (SKELETON)
**Generate IF:** At least one peer input is present (any valid input set).
**Cluster:** Cross-cluster (tech patterns from ADLC, UI patterns from UXD)
**Purpose:** Provide AI-DLC v1 and developers with copy-paste starter patterns that demonstrate the correct way to implement common code patterns in this workspace. Seeded from architecture decisions (ADLC) and design system components (UXD).
## Directory Structure
```
{workspace-root}/examples/
├── README.md ← This file (index + usage guide)
├── api-endpoint.{ext} ← IF ADLC: REST endpoint boilerplate
├── database-query.{ext} ← IF ADLC: Query pattern (ORM/raw)
├── service-layer.{ext} ← IF ADLC: Service/use-case pattern
├── error-handling.{ext} ← IF ADLC: Error pattern
├── test-unit.{ext} ← IF ADLC: Unit test pattern
├── test-integration.{ext} ← IF ADLC: Integration test pattern
└── ui-component.{ext} ← IF UXD: Component using design system tokens
```
## Template: examples/README.md
```markdown
<!-- AI-DWG generated | source: AP + UXP example patterns | date: {generation-date} -->
# Code Examples
Starter patterns demonstrating the correct implementation approach for this workspace. These examples are **prescriptive** — they show the ONE correct way, not alternatives.
## How to Use
1. Find the pattern closest to what you're building
2. Copy the file as a starting point
3. Replace placeholders with your implementation
4. Follow the inline comments for guidance
## Available Patterns
| Pattern | File | Source | When to Use |
|---------|------|--------|-------------|
| API Endpoint | `api-endpoint.{ext}` | AP: API Architecture + Tech Stack | New REST/GraphQL endpoint |
| Database Query | `database-query.{ext}` | AP: Data Architecture + Tech Stack | New data access method |
| Service Layer | `service-layer.{ext}` | AP: Component Design patterns | New business logic unit |
| Error Handling | `error-handling.{ext}` | AP: Error patterns + API standards | Custom error scenarios |
| Unit Test | `test-unit.{ext}` | AP: Testing strategy + Tech Stack | New unit under test |
| Integration Test | `test-integration.{ext}` | AP: Testing strategy | New integration scenario |
| UI Component | `ui-component.{ext}` | UXP: Design System + Component Inventory | New frontend component |
## Rules
- MUST follow these patterns — don't invent new approaches
- MUST use the project's design tokens (see `design-system.md`) for UI components
- MUST follow naming conventions (see `naming-conventions.md`)
- Patterns aai-dwg/README.md
# AI-DWG — AI-Driven Workspace Generator
[](./LICENSE)
**Version:** 1.0.0
**Transform architecture into a ready-to-code development workspace.**
---
## What It Does
AI-DWG composes a complete development workspace from one or more design-time peer inputs — Architecture Package (from AI-ADLC), Product Backlog Package (from AI-POLC), and/or UX Design Package (from AI-UXD). Any non-empty combination is valid; none is privileged. It generates Kiro steering files, project instructions, repository structure, configuration files, and operational documents — scoped to the input clusters actually present.
**Input:** Any non-empty subset of {Architecture Package (AI-ADLC), Product Backlog Package (AI-POLC), UX Design Package (AI-UXD)} — all structured markdown documents. At least one is required; the more you provide, the richer the workspace.
**Output:** Ready-to-code workspace with governance, structure, and rules
---
## The AI-* PDLC Family
AI-DWG is part of **AIFLC** (AI Full Life Cycle) — the AI-* PDLC Family of injectable workflow packages.
```
╔════════════════ PORTFOLIO LAYER · scope = MANY projects ════════════════╗
(optional)
AI-ILC ⇢ AI-PILC ⇢ AI-PPM
Decide it Initiate it Govern it (portfolio of N projects)
╚═════════════════════════════════╤═══════════════════════════════════════╝
│
AI-FLO Route it — package-to-package
│ flow on the edge between layers
╔════════════════ PROJECT LAYER · scope = ONE project ════════════════════╗
AI-POLC ──► AI-UXD ──► AI-ADLC ──► AI-DWG ──► AI-DLC v1 (build) ¹
Own it Design UX Design it Prepare it ▲
│
AI-POLC ⇄ AI-DLC v1 (back-and-forth)┘
AI-DLC v1 ⇢ AI-UXD+AI-POLC (feedback)
AI-GCE + AI-TGE ──── alongside AI-DLC v1 (continuous quality) ────►
Guard it Test it
╚═════════════════════════════════════════════════════════════════════════╝
¹ AI-DLC v1 = Amazon's open-source build lifecycle (not ours; we feed it).
```
| Layer | Package | Type | Input | Output |
|-------|---------|------|-------|--------|
| Portfolio | **AI-ILC** ² | Interactive workflow (lifecycle) | Raw idea | Approved Idea Brief / Feature Brief |
| Portfolio | **AI-PILC** | Interactive workflow (lifecycle) | Raw requirement | Project Initiation Package (PIP) |
| Portfolio | **AI-PPM** ³ | Adaptive portfolio engine | Multiple PIPs + Approved Idea Briefs | Portfolio register + cross-project prioritization & governance |
| Edge | **AI-FLO** ³ | Router / orchestration engine | Any package output marker | Routing decision + handoff to next package/layer |
| Project | **AI-POLC** ³ | Interactive workflow (lifecycle) | PIP | Product Backlog Package (Github ReposUpdated 1d agoRank 70
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Machine-readable data
The same record, as JSON, for agents and crawlers.
{
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"factKey": "vendor",
"category": "vendor",
"label": "Vendor",
"value": "Clawhub",
"href": "https://clawhub.ai/mbmd/skills/pdlc",
"sourceUrl": "https://clawhub.ai/mbmd/skills/pdlc",
"sourceType": "profile",
"confidence": "medium",
"observedAt": "2026-10-11T04:11:01.573Z",
"isPublic": true
},
{
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"href": "https://clawhub.ai/mbmd/pdlc",
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}
]
}Record generated Oct 11, 2026.