sdlckit 0.2.1

SDLCKit — AI-governed SDLC lifecycle engine with SAGE loop

SDLCKit

Governed AI quality loops for every SDLC phase.

SDLCKit wraps each phase of the software development lifecycle in a SAGE loop — a governed cycle where humans declare intent, AI produces artifacts, and independent scorers review against weighted quality dimensions. The result: measurable confidence in AI-generated artifacts before they reach production.

SDLCKit is a governance layer, not an SDLC. You keep your existing process and add SDLCKit on top.

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Why Governance?

AI agents can produce code, specs, and designs — but without governance, there is no measurable quality signal. SDLCKit provides that signal through four properties:

Property	What It Means
Independent Scoring	Scorer agent is sandboxed (read-only tools). Cannot collude with producer.
Dimensional Confidence	Not pass/fail. N weighted dimensions scored independently (e.g., clarity 0.35, completeness 0.30).
Bounded Fix Loops	Max N automated iterations. Oscillation detection stops thrashing.
Human Gates	Two-pass: (1) deterministic structural check, (2) human quality review with scorecard.

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The SAGE Loop

Every phase — refine, architect, build, review — runs the same governed cycle:

  SCOPE                           SAGE LOOP
  +------------------+            +------------+
  | User input       |       +--->|  ANALYZE   |
  | Discovery output |       |    |  Frame goal + constraints   |
  | Feedback signals |------>+    +-----+------+
  | Prior state      |       |          |
  +------------------+       |          v
                             |    +------------+
                             |    | GENERATE   |
                             |    |  AI produces artifacts      |
                             |    +-----+------+
                             |          |
                             |          v
                             |    +------------+
                             |    | EVALUATE   |
                             |    |  Independent scorer scores  |
                             |    |  N weighted dimensions      |
                             |    +-----+------+
                             |          |
                             |     confidence < threshold?
                             |          |
                             |     YES: Fix Loop (target weakest dim)
                             |          |
                             |     NO:  Human Gate
                             |          |
                             |    [Approve] -> advance
                             |    [Revise]  -> feedback
                             +----[Pause]   -> save state

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Quick Start

Prerequisites

• Python 3.11+
• Claude Code

Install

pip install sdlckit

Installs the latest published release. To pin a specific version:

pip install sdlckit==0.2.0

Initialize a Project

sdlckit init

Scaffolds your project: sdlc.yaml, skills, agents, templates, schemas, and the /sdlc slash command.

Customize

After init, update these for your team:

• sdlc.yaml — lifecycle phases, scoring dimensions, thresholds, plugin config
• .sdlckit/conventions/ — layered convention files loaded in sorted order:
  • 00-architecture.md — diagram standards, naming, classDef palette (built-in)
  • 01-security-architecture.md — trust zones, data classification (built-in)
  • 10-your-team.md, 11-your-domain.md — add your own with 10+ prefix

Run a Phase

# In Claude Code:
/sdlc refine "user onboarding feature"

The SAGE engine produces REFINE.md, scores it across weighted dimensions, runs fix loops if below threshold, then presents the human gate.

Check Status

/sdlc status

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Commands

Slash commands (in Claude Code):

Command	What It Does
/sdlc <phase> "input"	Run a phase with direct input
/sdlc <phase>	Run a phase (inputs auto-resolved from upstream)
/sdlc <phase> amend "feedback"	Re-enter a completed phase with feedback
/sdlc status	Lifecycle dashboard
/sdlc reconcile	Re-run stale phases after an amendment
/sdlc signal list|show|dismiss|inject	Manage feedback signals
/sdlc connectors	View connector plugin status

Phase commands are dynamic — a custom phase compliance in sdlc.yaml becomes /sdlc compliance.

CLI commands:

Command	What It Does
sdlckit init [--type component|initiative]	Scaffold a project
sdlckit assign <path>	Import an assignment into a component repo
sdlckit observe <title> --components <names>	Record an operational observation
sdlckit archive <name>	Pre-merge cleanup (manifests, state)
sdlckit --version	Show installed version

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Two Modes

Component mode (sdlc.yaml) — single project, linear phase sequence: refine -> architect -> build -> review.

Initiative mode (sdlc-initiative.yaml) — multi-component, multi-repo projects. Discovery and Delivery stages with per-component SAGE loops, stage reviews, knowledge extraction, and assignment handoffs.

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Extending with Plugins

SDLCKit separates engine (how the loop runs) from domain knowledge (what the loop produces). Plugins provide richer implementations:

• Phase plugins replace built-in phases with specialized skills, agents, dimensions, and templates
• Connector plugins deliver scored artifacts to external systems (Jira, CI/CD, etc.)

See the Developer Guide in the repository for plugin authoring.

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Roadmap

Feature	Target
Checkpoint / resume (crash recovery)	v0.3
SAGE-wrapped knowledge extraction	v0.3
Connector plugin execution	v0.3
auto_advance phase chaining	v0.3
Plugin registry + remote sources	Post-1.0

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Documentation

The repository includes detailed guides:

• Consumer Guide — installation, configuration, daily usage
• Contributor Guide — engine internals, testing
• Developer Guide — building plugins, skills, agents, schemas
• Architecture Reference — SAGE engine, state management, plugin model, agent model

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License

Apache License 2.0