This source-evidence page is currently authored and reviewed in English.

Category operating system

LaunchpadOS

A versioned product-management source toolkit

A versioned product-management source toolkit with 336 SKILL.md files in the reviewed snapshot, plus orchestration, evaluation, examples, APIs, MCP and platform-specific surfaces. It is an inspectable working environment, not evidence that every method statement or generated artefact is automatically correct.

查看源代码记录项目组合图

业务 ↔ 技术桥梁 · Category operating system

Turn product judgement into an operating record without outsourcing authority to a model.

Makes full-lifecycle product judgement versioned, inspectable, and executable.

这是对项目组合与源代码的解读，并非客户、部署、认证或成果声明。

  1. 01

    业务压力

    Customer, portfolio, economic, delivery, and technical decisions are dispersed across documents and tools, so their original evidence and invalidation logic disappear.

  2. 02

    产品决策

    Define which product decisions matter, which evidence advances a gate, who owns the call, and what must cause the decision to reopen.

  3. 03

    技术记录

    Versioned skills, the Operated Product Decision Graph, Release Case boundaries, CLI and MCP surfaces, workflow state, evaluations, and audit records.

  4. 04

    证据回流

    Changed assumptions, technical limits, release evidence, and field signals return to scope, economics, sequencing, and portfolio commitment.

源码证据

源码实际能够证明什么

A versioned product-management source toolkit with 336 SKILL.md files in the reviewed snapshot, plus orchestration, evaluation, examples, APIs, MCP and platform-specific surfaces. It is an inspectable working environment, not evidence that every method statement or generated artefact is automatically correct.

详细技术快照以英文维护。

成熟度
Source toolkit under active development
证据审查
源码快照截至

产品与架构决策

  • Compile reusable product practices into explicit skills, rubrics, and workflow stages.
  • Keep artefact generation, quality evaluation, memory, and trust records as separate concerns.
  • Treat Physical AI release readiness as an assurance verdict with human gates, not an automated deployment permission.

可核验的证据

  • 331 core skill files plus five agent and retrieval packs: 336 SKILL.md files at the reviewed commit.
  • Python CLI, API and MCP surfaces with workflow, evidence, audit and quality-test suites.
  • A content-addressed HC-OPDG-001 0.1.0 candidate plus reference artefacts and selected evaluation fixtures.

声明边界

  • Skill inventory and structural depth are source scope, not proof of equal factual authority across every method or model.
  • Platform and tool surfaces require path-specific configuration and verification; source presence does not establish identical behavior or external-system access.
  • The OPDG bundle remains a candidate with licence and owned-evidence status pending; generated product, safety, legal and release decisions require accountable human review.

代码仓库证据不等同于客户、部署、认证或成果证据。

The Problem

The Problem

Why product teams can't keep up

  1. PM Bandwidth Bottleneck

    Product managers can lose substantial time recreating PRDs, stories, roadmaps, and planning material instead of testing assumptions and aligning decisions.

  2. Inconsistent Quality

    Artefact quality varies when teams lack shared frameworks, rubrics, and reusable examples. Important reasoning can remain implicit and difficult to review.

  3. Tool Fragmentation

    Product context is often distributed across planning, design, engineering, and communication tools. Each hand-off creates a risk that assumptions and evidence lose their traceability.

  4. Scaling Without Hiring

    Growing teams need reusable product discipline without assuming that generated artefacts can replace accountable product leadership.

Capabilities

Core Capabilities

10 pillars of AI-powered product management

仅为源代码范围：条目可能描述代码、已记录适配器或已配置工作流，并不表示生产服务正在运行。

  1. 01

    Expert Skill Library

    331 core skills across product, methodology, team, pipeline and Physical AI families, plus five agent and retrieval packs. The structural audit checks required sections and files; it does not establish that every method statement is current or equally authoritative.

  2. 02

    Virtual Product Team

    The source represents 10 virtual role prompts — analyst, architect, developer, UX designer, copywriter, tester, security, DevOps, scrum master, and business analyst. A role prompt is not a credentialed specialist and its output requires accountable review.

  3. 03

    Innovation Pipeline

    A repository-defined five-stage pipeline labelled Discovery → Insights → Architecture → Experiment → Deploy, with PM approval gates and fast, standard and careful modes.

  4. 04

    Workflow Engine

    Six source-defined skill chains cover discovery-to-PRD, GTM launch, churn-to-retention, strategy-to-roadmap, sprint cycle and new feature. Context passing is implemented in source; output coherence and factual quality still require path-specific evaluation.

  5. 05

    Agent Orchestration

    The PM Company OS source sequences model-assisted roles, PM approval gates and versioned artefacts. It supports drafting and review; it does not autonomously build, ship or approve a product, and improvement claims require measured evaluation.

  6. 06

    PM Tool Integration

    The source includes manifests and adapter surfaces for Notion, Linear, Jira, GitHub, Figma, Miro, Slack and Confluence. Availability, read/write scope and approval behaviour vary by adapter and require credentials, configuration and path-specific verification.

  7. 07

    Company Knowledge RAG

    The source includes ingestion and retrieval paths for approved playbooks, transcripts and research, with evidence-quality and staleness fields. Retrieval does not establish source correctness, permission, completeness or answer accuracy.

  8. 08

    Quality Assurance

    Source-controlled quality checks, selected evaluation rubrics, and benchmark artefacts make parts of the output reviewable. Coverage and model-judge reliability remain bounded.

  9. 09

    Multi-Platform Native

    The repository contains platform-specific surfaces for a Claude Code plugin, Claude Desktop MCP, ChatGPT actions, Mistral agent prompts and a custom HTTP API. Each path has its own setup and must be verified independently; source presence is not a promise of identical behaviour.

  10. 10

    Deployment Design

    The repository contains container, orchestration, cloud-template, audit, and service-level design surfaces. Templates are not evidence of a production deployment, security posture, or compliance status.

Tech Stack

Built With

所列技术仅标识仓库依赖项或适配器表面，并不证明存在活动集成或部署。

Backend
Python 3.12+, FastAPI, Click CLI, Redis caching, Pydantic v2, litellm multi-provider abstraction
AI / LLM
Provider-adapter and routing source for Anthropic, OpenAI, Mistral and local-model paths, plus retrieval components; model availability and behaviour depend on configuration and current provider terms
Frontend
React 18, Vite, Tailwind CSS, skill browser UI, brief input, output panel, history panel
Integrations
Notion (pages), Linear (issues), Jira (tickets), GitHub (releases), Figma (specs), Miro (boards), Slack (digests), Confluence (wikis)
Infrastructure
Docker Compose (5 services), Kubernetes Helm, AWS ECS Fargate, GCP Cloud Run, nginx reverse proxy, API key scoping
Quality & Evaluation
Pytest structural skill audit, selected rubric and evaluation fixtures, benchmark harnesses, and property-based tests; factual method authority requires separate source assurance

Pricing

Get Started with LaunchpadOS

Contact me to evaluate where LaunchpadOS can support your product practice, what still requires human product leadership, and which integrations would need separate verification.

Request a Demo

Inspect the dated skill inventory, virtual-role and workflow source, and the innovation pipeline implementation. The demo separates what is present in source from what is configured, tested and ready for accountable use.

Contact Sales

Physical AI Product Leadership Mission

Use the toolkit inside an accountable product mission.

Hyperion can use selected LaunchpadOS source patterns while leading one evidence-backed product milestone. The commercial mandate remains the Physical AI Product Leadership Mission; LaunchpadOS is supporting R&D, not a fourth offer or a promise of autonomous delivery.

Explore the Product Leadership Mission
AI 洞察

The AI Dispatch

从演示走向可靠运营。订阅 Physical AI 新读物。

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