specsoloist 0.4.0

Spec-as-Source Framework: AI-driven development where specifications are the source of truth.

SpecSoloist

SpecSoloist is a "Spec-as-Source" AI coding framework. It treats specifications as the source of truth and uses AI agents to compile them into executable code.

Now with Spechestra features: compose systems from natural language, conduct parallel builds, and orchestrate multi-step workflows.

Why SpecSoloist?

Code is often messy, poorly documented, and prone to drift from original requirements. SpecSoloist flips the script:

1. Write Specs: You write requirements-oriented specifications (Markdown).
2. Compile to Code: AI agents read your specs and write implementations directly.
3. Self-Healing: If tests fail, agents analyze the failure and patch the code.
4. Orchestrate: Define complex workflows where agents collaborate, share state, and pause for human input.

Code is a build artifact. Specs are the source of truth.

Installation

pip install specsoloist

Quick Start

1. Clone the repository (or create a new folder):

   git clone https://github.com/symbolfarm/specsoloist.git
   cd specsoloist
   

2. Set your API Key (Gemini or Anthropic):

   export GEMINI_API_KEY="your_key_here"
   # or
   export ANTHROPIC_API_KEY="your_key_here"
   

3. Create a new specification:

   sp create calculator "A simple calculator with add and multiply"
   

   This creates src/calculator.spec.md.

4. Compile it to code:

   sp compile calculator
   

   This generates build/calculator.py and build/test_calculator.py.

5. Run the tests:

   sp test calculator
   

6. (Optional) If tests fail, try auto-fix:

   sp fix calculator
   

Orchestration (Spechestra)

SpecSoloist allows you to chain multiple specs into a workflow.

1. Draft Architecture: Use sp compose to vibe-code your system.

   sp compose "A data pipeline that fetches stocks and calculates SMA"
   

   This generates a component architecture and draft specs.

2. Conduct Build: Compile all components via agent orchestration.

   sp conduct
   

   The conductor agent resolves dependency order and spawns soloist agents to compile each spec in parallel.

3. Perform Workflow: Execute a workflow spec.

   sp perform my_workflow '{"symbol": "AAPL"}'
   

CLI Reference

Command	Description
sp list	List all specs in src/
sp create	Create a new spec manually
sp compose	Draft architecture & specs from natural language
sp conduct [dir]	Build project via conductor/soloist agents
sp perform	Execute an orchestration workflow
sp validate	Check spec structure
sp verify	Verify schemas and interface compatibility
sp compile	Compile single spec to code + tests
sp test	Run tests for a spec
sp fix	Auto-fix failing tests (Agent-first)
sp respec	Reverse engineer code to spec
sp build	Compile all specs (direct LLM, no agents)
sp graph	Export dependency graph (Mermaid.js)

Commands that use agents (compose, conduct, respec, fix) default to detecting an available agent CLI (Claude Code or Gemini CLI). Use --no-agent to fall back to direct LLM API calls.

Configuration

You can configure SpecSoloist via environment variables or a .env file:

export SPECSOLOIST_LLM_PROVIDER="gemini"  # or "anthropic"
export SPECSOLOIST_LLM_MODEL="gemini-2.0-flash"  # optional

Arrangement Files

An Arrangement is SpecSoloist's makefile — it bridges language-agnostic specs to a concrete build environment by specifying the target language, output paths, build commands, and constraints.

See arrangements/arrangement.python.yaml for a complete example:

target_language: python
output_paths:
  implementation: src/mymodule.py
  tests: tests/test_mymodule.py
environment:
  tools: [uv, ruff, pytest]
  setup_commands: [uv sync]
build_commands:
  lint: uv run ruff check .
  test: uv run pytest
constraints:
  - Must use type hints for all public function signatures

Usage:

# Explicit path
sp compile myspec --arrangement arrangement.yaml
sp build --arrangement arrangement.yaml
sp conduct --no-agent --arrangement arrangement.yaml

# Auto-discovery: place arrangement.yaml in your project root
# and it will be picked up automatically
sp compile myspec

External Dependencies

Specs describe your code. External libraries are inputs to the build. There are three patterns depending on how well the LLM knows the library:

1. Well-known libraries — constraints only

For React, pytest, lodash, etc. the soloist already knows the API. Just mention it in the arrangement or spec constraints:

constraints:
  - Use React hooks (useState, useEffect) for state management
  - Use Tailwind CSS for styling

2. Obscure or new libraries — interface spec

For newer libraries (e.g. FastHTML) where LLMs may hallucinate the API, write a type spec capturing the subset you actually use. Your other specs list it as a dependency, giving every soloist accurate documentation:

---
name: fasthtml_interface
type: type
status: stable
---
# FastHTML Interface Contract

The subset of FastHTML used in this project.

## Components
- `Div(**attrs, *children)` — renders a div
- `Form(hx_post, hx_swap, *children)` — HTMX-enabled form
- `Input(name, type, placeholder)` — form input

## App
- `@rt(path)` — route decorator
- `serve()` — start dev server

3. Complex SDKs — adapter spec

For SDKs with many moving parts (e.g. Vercel AI SDK), write a thin adapter spec that wraps the SDK. Everything else in your project depends on your adapter, not the SDK directly. If you swap the underlying SDK, only the adapter spec changes:

---
name: ai_client
type: bundle
dependencies: []
---
# AI Client

Wraps the Vercel AI SDK for this project.

## `streamChat(messages, options)`
Streams a chat completion using `streamText()` from the `ai` package.
Returns an AI SDK `StreamingTextResponse`.

See examples/ for concrete interface and adapter spec examples.

Sandboxed Execution (Docker)

For safety, SpecSoloist can run generated code and tests inside an isolated Docker container.

1. Build the sandbox image:

   docker build -t specsoloist-sandbox -f docker/sandbox.Dockerfile .
   

2. Enable sandboxing:

   export SPECSOLOIST_SANDBOX=true
   # Optional: override the image (default: specsoloist-sandbox)
   # export SPECSOLOIST_SANDBOX_IMAGE="my-custom-image"
   

3. Run tests: sp test my_module will now wrap execution in docker run.

For Anthropic:

export SPECSOLOIST_LLM_PROVIDER="anthropic"
export ANTHROPIC_API_KEY="your_key_here"
export SPECSOLOIST_LLM_MODEL="claude-sonnet-4-20250514"  # optional

Native Subagents (Claude & Gemini)

For the full agentic experience, SpecSoloist provides native subagent definitions for Claude Code and Gemini CLI. These allow the AI to delegate tasks to specialized agents:

Agent	Purpose
compose	Draft architecture and specs from natural language
conductor	Orchestrate builds — resolves dependencies, spawns soloists
soloist	Compile a single spec — reads spec, writes code directly
respec	Extract requirements from code into specs
fix	Analyze failures, patch code, and re-test

Usage with Claude Code:

> conduct score/
> respec src/specsoloist/parser.py to score/parser.spec.md

Usage with Gemini CLI:

> compose a todo app with user auth

The subagent definitions are in .claude/agents/ and .gemini/agents/.