AI Decision System
Replayable, offline-verifiable records for AI-assisted go/no-go decisions: experiment promotions, typed model answers and human-approved actions.
A Claude Code plugin that turns fuzzy product ideas into validated spec packages ready for agent handoff. /optimize runs it in reverse on shipped products.
Developer toolsPython · JavaScriptGitHub @clownwareOnlineOwner unconfirmed
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AI-assisted product development, from fuzzy idea to technical spec — a Claude Code plugin of guided UX-research prompt flows, skills, and a spec-compiling subagent.
Taken from the repository README
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A Claude Code plugin that compiles fuzzy product ideas into validated, agent-consumable specification packages.
Takes a product idea and walks you through structured product thinking — problem definition, persona, hypothesis, user flows — then compiles the output into a spec package that an implementation agent (Claude Code, Cursor, etc.) can build from without guessing.Read the full README ↗A Claude Code plugin that compiles fuzzy product ideas into validated, agent-consumable specification packages.
Takes a product idea and walks you through structured product thinking — problem definition, persona, hypothesis, user flows — then compiles the output into a spec package that an implementation agent (Claude Code, Cursor, etc.) can build from without guessing.
"I have an idea for a → Guided conversation → spec-package/
tea tracking app" (14 prompts) ├── manifest.yaml
├── context/ (why)
├── spec/ (what)
├── docs/ (decisions)
└── CLAUDE.md (handoff)
The spec package has three layers:
A validation pipeline checks referential integrity across all spec files before the package is marked as ready.
/compile uses it (to run the bundled plugin/scripts/compile_spec.py). The ideation/flow/spec workflow and /summary brief work without it. If Python is absent, /compile explains and points you to /summary and the raw artifacts instead of failing with a traceback./compile from plugin/scripts/requirements.txt when Python is presentFrom the Clownware marketplace, inside Claude Code:
/plugin marketplace add clownware/plugins
/plugin install product-dev@clownware
Or from a terminal:
claude plugin marketplace add clownware/plugins
claude plugin install product-dev@clownware
To run it from a checkout of this repo without installing, for development:
claude --plugin-dir ./plugin
Works in Claude Code CLI, Desktop app, and VS Code extension.
| Command | What It Does |
|---|---|
/idea | Start from a product idea — "a tea tracking app" |
/problem | Start from a problem domain — "specialty tea enthusiasts waste tea" |
/spec | Generate technical specs (data models, API, business rules, NFRs) |
/compile | Assemble artifacts into a validated spec package + handoff instruction |
/summary | Generate a consolidated project brief |
/optimize | Audit an existing product and build a prioritized UX optimization spec |
/product-dev:status | Show project progress and suggest next steps |
/idea tea tracking app → Captures concept, defines problem, creates persona,
forms hypothesis (Phases 00-03, ~20 min)
→ Maps user flow, identifies screens, scopes prototype
(Phase 04, ~15 min)
/spec → Generates data models, API contracts, business rules,
constraints as structured YAML (Tech Spec, ~20 min)
/compile → Assembles spec-package/, runs 20 validation checks,
generates CLAUDE.md handoff instruction (~30 sec)
Then copy spec-package/ and CLAUDE.md into a fresh project directory and point an implementation agent at it.
Default is Tier 1 (quick exploration, ~60 min total). The framework escalates to Tier 2 when you give detailed responses or ask to go deeper.
├── plugin/ # Claude Code plugin (self-contained, installable)
│ ├── .claude-plugin/plugin.json # Plugin manifest
│ ├── commands/ # 6 commands (/idea, /problem, /spec, /compile, /summary, /optimize)
│ ├── skills/ # 5 skills (4 workflows + status)
│ ├── agents/ # 2 subagents (tech-spec-writer, ux-extractor)
│ ├── prompts/ # Bundled prompt library
│ │ ├── 01_ux_research/ # Prompts: phases 00-06
│ │ ├── 02_tech_requirements/ # Tech spec prompts: 4 areas + consolidation
│ │ └── 03-05_*/ # Tool setup, architecture bridge, impl docs
│ └── scripts/ # Bundled compile pipeline
│ ├── compile_spec.py # Spec package compiler
│ ├── validate_spec.py # 20-check cross-reference validator
│ ├── generate_handoff.py # CLAUDE.md handoff generator
│ └── requirements.txt # Python deps (pyyaml)
│
├── prompts/dev/ # Non-framework reference material (not bundled)
│ ├── build guides/ # Stack starter guides
│ ├── ide_rules/ # IDE configuration prompts
│ └── portfolio/ # Portfolio case-study prompts
│
├── scripts/ # Dev/test material (not bundled)
│ ├── archive/ # One-time migration utilities
│ ├── test-chain.md # End-to-end test doc
│ └── test-registry.md # Registry test doc
│
├── examples/tea-tracker/ # Reference spec package (test fixture)
│ ├── context.json # Sample registry
│ ├── artifacts/ # Sample working artifacts (12 files)
│ ├── spec-package/ # Compiled output (16 files)
│ └── CLAUDE.md # Generated handoff instruction
│
├── skills-claude-ai/ # Claude.ai Agent Skill ports (ideation + flow)
│ ├── product-ideation/ # SKILL.md + bundled prompts (phases 00-03)
│ └── product-flow/ # SKILL.md + bundled prompts (phases 04-06)
│
└── docs/
├── spec-package-schema.md # Spec package YAML schema (v1.0.0)
├── adrs/ # Architecture Decision Records
└── updates/ # PRD v3, refactor roadmap, enhancement guides
See docs/spec-package-schema.md for the full schema definition:
entities.yaml — Data model with typed fields, constraints, indexesflows.yaml — User journey with screen + API call references per stepscreens.yaml — UI inventory with content elements, data sources, actionsendpoints.yaml — API contracts with request/response schemasrules.yaml — Business logic as IF/THEN pseudocode with enforcement locationsconstraints.yaml — Performance targets, security config, prototype scopeAll IDs are cross-referenced. The validation pipeline checks 20 rules across referential integrity, completeness, and consistency.
Decisions are recorded as ADRs. The index — one row per record with its status, the checks that enforce it, and what amended it — is generated from the records themselves: docs/adrs/README.md.
Every ADR declares its testable consequences in an appended ## Enforcement
section, verified by a check suite:
python checks/run_checks.py
Warnings report; only graduated (block) checks fail CI or block a session.
See checks/README.md for graduation and exclusions, and
ADR 0012 for the architecture.
See CONTRIBUTING.md for prompt authoring standards, plugin development, and the ADR process.
The interrogation, premise-challenge, quality-loop, decision-classification, scope-walk, and process-learnings patterns (ADRs 0016–0017) were adapted from Garry Tan's gstack (MIT).
Apache 2.0. See LICENSE.