stellars-claude-code-plugins 1.0.17

Claude Code plugins for autonomous development workflows

stellars-claude-code-plugins

A plugin marketplace for Claude Code providing structured workflows for software development, document analysis, data science, and project management. Each plugin is pure configuration (skills, commands, YAML) - install one or all depending on your needs.

/plugin marketplace add stellarshenson/claude-code-plugins

The marketplace includes a shared YAML-driven orchestration engine (autobuild) that pulls agents through structured phases with quality gates, a semi-data-science document critic (devils-advocate) with Fibonacci risk scoring, production SVG infographics (svg-infographics) with grid-first design and automated validation, data science project standards (datascience) with notebook scaffolding and compliance fixes, structured document processing (document-processing) with source grounding, and project journaling (journal).

[!NOTE] Read the full article on the orchestration approach: Your AI Agent Will Cut Corners. Here's How to Stop It.

Plugins

Plugin	What it solves
autobuild	Executes code and artefact builds toward an objective with iterations driven by a calculated outcome benchmark - enforces structured phases with multi-agent review
devils-advocate	Produces high-quality documents for a specific audience using a scientific, measured, iterative approach - quantified critique with Fibonacci risk scoring and per-iteration residual measurement
svg-infographics	Produces high-quality standardised SVG infographics - grid-first design, theme-driven styling, dark/light mode, and 5 automated checkers for layout, contrast, and alignment
datascience	Produces high-quality data science projects and notebooks following consistent standards - scaffolds projects from copier templates, enforces notebook structure, applies rich output styling, and supports prompt engineering techniques
document-processing	Processes documents according to user requests with grounding in source materials - source tracing, compliance checking, PDF automation
journal	Produces a work journal marking key changes, implementations, and decisions - append-only audit trail with continuous numbering and archiving

autobuild

Runs structured multi-iteration development cycles where each iteration passes through a full phase lifecycle with quality gates. A program defines what to build, a benchmark measures progress, and the engine enforces the workflow until the objective is met or iterations are exhausted.

• Shallow fixes - forces research and hypothesis before implementation
• Scope creep - plan locks scope, review catches deviations
• Lost context - hypothesis catalogue and failure context persist across iterations
• Unchecked quality - two independent gates (readback + gatekeeper) per phase
• No accountability - every phase records agents, outputs, and verdicts in YAML audit logs
• Benchmark gaming - guardian agent checks for benchmark-specific tuning vs genuine improvement

Skills: autobuild (orchestrator), program-writer, benchmark-writer

Workflow types

Type	Phases	Use when
full	RESEARCH → HYPOTHESIS → PLAN → IMPLEMENT → TEST → REVIEW → RECORD → NEXT	Feature work, improvements
fast	PLAN → IMPLEMENT → TEST → REVIEW → RECORD → NEXT	Clear objective, no exploration needed
gc	PLAN → IMPLEMENT → TEST → RECORD → NEXT	Cleanup, refactoring
hotfix	IMPLEMENT → TEST → RECORD	Targeted bug fix
planning	RESEARCH → PLAN → RECORD → NEXT	Work breakdown (auto-chains before full)

Usage

# Describe what you want - the plugin handles the rest
/autobuild improve error handling in the API layer

The plugin writes PROGRAM.md and BENCHMARK.md from your prompt, asks you to approve, then runs the orchestrator autonomously.

See autobuild/README.md for the full phase lifecycle, agent architecture, and configuration details.

devils-advocate

Systematically critiques documents from the perspective of their toughest audience. Builds a devil persona, harvests verifiable facts, generates a risk-scored concern catalogue, and iterates corrections until residual risk is acceptable.

Skills: setup (build persona + fact repository), evaluate (concern catalogue + baseline scorecard), iterate (apply corrections or re-score), run (full workflow end-to-end)

Risk scoring uses a Fibonacci scale (1-8) for likelihood and impact, producing risk scores from 1-64. Each concern is scored 0-100% on how well the document addresses it, and the residual risk (what remains unaddressed) drives iteration priority.

Usage

# Full end-to-end workflow
/devils-advocate:run

# Step by step
/devils-advocate:setup       # Build persona, harvest facts
/devils-advocate:evaluate    # Generate concerns + baseline scorecard
/devils-advocate:iterate     # Apply corrections, re-score (repeat)

See devils-advocate/README.md for scoring formula details, artefact format, and the full concern catalogue methodology.

svg-infographics

Creates production-quality SVG infographics with a mandatory 6-phase workflow (research, grid, scaffold, content, finishing, validation). Every coordinate is Python-calculated, every colour traces to an approved theme swatch, and five validation tools check overlaps, WCAG contrast, alignment, and connector quality before delivery.

Skills: svg-standards (grid layout, CSS classes, cards, arrows), workflow (6-phase process), theme (palette approval), validation (checker tools)

Usage

# Create infographic(s) with full workflow
/svg-infographics:create card grid showing 4 platform modules

# Generate theme swatch for approval
/svg-infographics:theme corporate blue palette

# Run validation on existing SVGs
/svg-infographics:validate docs/images/*.svg

# Fix style/contrast issues
/svg-infographics:fix-style docs/images/overview.svg

# Fix layout/overlap issues
/svg-infographics:fix-layout docs/images/overview.svg

Includes 64 production SVG examples, 5 Python validation tools, and theme swatches. See svg-infographics/README.md for design principles and workflow details.

datascience

Enforces data science project standards derived from production notebook workflows. Five skills auto-trigger when working with notebooks, datasets, rich output, prompts, or progress bars. Nine commands fix existing code, scaffold new projects, and apply prompt engineering techniques.

Skills: datascience (project conventions), notebook-standards (section order, GPU-first), rich-output (semantic colors), prompt-engineering (7 research-backed techniques), progressbars (tqdm/rich)

Usage

# Create a new project from copier template
/datascience:new-project

# Fix an existing notebook to comply with standards
/datascience:fix-notebook notebooks/01-kj-analysis.py

# Apply rich styling fixes (wrong colors, multiple prints)
/datascience:apply-style notebooks/02-kj-train.py

# Add or fix progress bars (choose tqdm or rich)
/datascience:apply-progressbar notebooks/02-kj-train.py

# Apply prompt engineering technique (CoT, CoD, ToT, few-shot, etc.)
/datascience:apply-prompt-technique

# Full psychological prompting stack for hard problems
/datascience:challenge

# Port legacy project to copier-data-science template
/datascience:fix-project

See datascience/README.md for the full list of standards enforced.

journal

Project journal management with append-only entry format, continuous numbering, and automatic archiving. Auto-triggers after substantive work to maintain a consistent audit trail in .claude/JOURNAL.md.

Skills: journal (auto-triggered after substantive work)

Usage

# Create a new entry for completed work
/journal:create added retry logic to API client

# Update the most recent entry with corrections
/journal:update also fixed the timeout parameter

# Archive older entries (keeps last 20)
/journal:archive

See journal/README.md for entry format, detail levels, and archiving rules.

document-processing

Structured document processing with source grounding and quality control. Takes input documents through a verified workflow (analyze, draft, ground, uniformize) and produces outputs where every factual claim is traceable to source material.

Skills: process-documents (4-phase workflow), validate-document (grounding + compliance), pdf (basic operations), pdf-pro (production workflows)

Usage

# Full workflow from objective
/document-processing:run synthesize expert opinions into position paper

# Update existing output with new source material
/document-processing:update add new hearing transcript to timeline

# Validate a document against its sources
/document-processing:validate

See document-processing/README.md for the grounding methodology, folder structure, and PDF processing details.

Install

pip install stellars-claude-code-plugins

As a Claude Code plugin marketplace:

/plugin marketplace add stellarshenson/claude-code-plugins

Building a new plugin

Plugins are pure configuration - no Python code required. Create a directory with skills and register it in the marketplace:

my-plugin/
  .claude-plugin/plugin.json           # Plugin registration and skill triggers
  skills/
    my-skill/SKILL.md                  # Skill definition with description and instructions

The plugin.json registers your skills with Claude Code, defining when they trigger and what tools they have access to. Each SKILL.md contains the instructions Claude follows when the skill is invoked. The shared orchestration engine (pip install stellars-claude-code-plugins) provides the orchestrate CLI command that handles state management, FSM transitions, gate execution, and audit logging.

Register your plugin in the marketplace by adding an entry to .claude-plugin/marketplace.json.

Development

make install          # create venv, install deps, editable install
make test             # run tests
make lint             # ruff format + check
make format           # auto-fix formatting
make build            # clean, test, bump version, build wheel
make publish          # build + twine upload to PyPI

License

MIT License