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liangyi

Um pipeline completo de desenvolvimento de produtos que orquestra debates de especialistas, revisão cega, geração de documentos e registro de decisões humanas. Integra 4 habilidades modulares em um fluxo da ideia ao desenvolvimento, ideal para iniciar um novo produto do zero.

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Atualizado há 6 meses

Ver no GitHub ↗ Licença: MIT

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/plugin marketplace add wenboxia/liangyi

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Liangyi — Adversarial Product Workflow v1.0

Liangyi (两仪) /lyahng-ee/ — "The Two Polarities."
From the I Ching (易经·系辞): "太极生两仪" — *The Supreme Ultimate generates
the Two Polarities.* In Chinese cosmology, a single unified origin (Taiji)
gives rise to two opposing forces (yin and yang), whose productive tension
shapes all further complexity.
This workflow is built on the same structure: a single product idea generates
two opposing expert views, whose structural disagreement you synthesize into
your final decision. The tension is the feature.

Full orchestrator for the Liangyi workflow. Combines 4 modular skills into a complete flow.

This skill bundles all reference files in references/. No additional
configuration is needed to get started.
Model compatibility: This skill runs in Claude Code (Model A = Claude).
For the independent challenger (Model B), Gemini is recommended, but any
capable LLM works — GPT, Qwen, Doubao, DeepSeek, etc. The key is using a
different provider so training biases don't overlap. Throughout this
document, "Gemini" is used as shorthand for "your chosen Model B."

Quick start

/liangyi

Individual phases can also be run:

/liangyi phase0    # Idea refinement
/liangyi phase1    # Expert brainstorm
/liangyi phase2    # Cross-validation
/liangyi phase3    # Document generation
/liangyi phase4    # Development kickoff

First-time setup (optional):

/liangyi setup

For standalone use of any module, install them individually:

  • /expert-debate — just the brainstorm
  • /blind-review — just the review
  • /doc-generator — just the documents
  • /decision-log — just decision tracking

Setup (first-time configuration)

When invoked with setup, guide the user through:

  1. Model B selection: "Which LLM will you use as Model B? (Gemini recommended,

GPT/Qwen/Doubao/DeepSeek also work)"

  • Save choice to references/config.md
  1. Skills inventory: Read references/skills-registry.md and ask:
  2. "These are the default recommended Skills. Do you want to add, replace, or

remove any? (Enter to keep defaults)"

  • If user has changes, update references/skills-registry.md
  1. Quick validation: Confirm the setup by listing:
  2. Model A: Claude (fixed)
  3. Model B: [user's choice]
  4. Active Skills: [list from registry]
  5. "Ready to go. Run /liangyi to start."

Setup is optional — the workflow runs with defaults if skipped.

Phase 0: Idea capture

  1. Ask the user for their raw idea (voice transcript, rough notes, anything)
  2. Generate a Gemini prompt for language refinement:
You are a prompt refinement specialist. I have a rough product idea description.
Please:
1. Remove filler words, repetition, and verbal tics
2. Only refine the language — do NOT change my intent or direction
3. Preserve all specific details and constraints I mentioned
4. Output a clean, structured description (under 200 words)

My raw idea: [paste raw idea]

Auto-copy to clipboard:

cat prompts/gemini-phase0.md | pbcopy 2>/dev/null || cat prompts/gemini-phase0.md | xclip -selection clipboard 2>/dev/null || echo "Prompt saved — please copy manually"
  1. After user provides Gemini's refined version, review it together — confirm

nothing was lost in refinement

  1. Save to idea-raw.md

Phase 1: Adversarial expert brainstorm

Uses the same logic as the standalone expert-debate skill.

1.0 Select expert role pair

Read references/expert-roles.md for recommendations. Help user pick two roles with structural tension. Log to decision-log.md:

### [PROCESS] Decision: Expert role assignment
**Role A (Claude):** [name + why]
**Role B (Gemini):** [name + why]
**Tension point:** [where they conflict]
**What AI can't weigh:** [why this pair matters for *this specific* project]

1A: Generate proposal as Role A

Full instructions in expert-debate skill. Key points:

  • Adopt persona completely, preserve professional biases
  • Mark conflicts with generic advice explicitly
  • Generate complete proposal with 5 components

1B: Generate Gemini prompt for Role B

Create prompt for a separate, clean Gemini session. Auto-copy to clipboard:

cat prompts/gemini-phase1.md | pbcopy 2>/dev/null || cat prompts/gemini-phase1.md | xclip -selection clipboard 2>/dev/null || echo "Prompt saved — please copy manually"

Tell the user: "Prompt copied. Open a new Gemini tab (label it 'Role B') and paste."

1C: Human comparison + decision log

Compare divergence points. Log each decision with the [PRODUCT] tag. Every entry must answer: "Why did I choose this? What couldn't AI weigh?"

Phase 2: Cross-validation & fusion

Uses the same logic as the standalone blind-review skill.

2A: Claude critique (investor role)

Drop Role A persona. Adopt skeptical investor. Critique the merged proposal:

  • 3 weakest points + hardening suggestions
  • Most likely cause of death
  • Output improved version

2B: Single-blind Gemini review

Generate prompt for a brand new Gemini session (label it "Blind Review"). Contains ONLY the improved proposal — no original idea, no background.

Auto-copy to clipboard. Tell user which tab to use.

After receiving Gemini's response:

  • Compare one-sentence summary vs original intent
  • Classify divergence: None / Minor / Major
  • Log as [REVIEW] type (AI-assisted finding, not human decision)

2C: Informed re-review (optional)

If 2B found major divergence, after revision → prompt for the original Gemini session (the one with context) to verify fix didn't drift.

2D: Devil's advocate (conditional)

Trigger: Claude + Gemini agree with no substantive disagreement. Complexity gate: Only for core architecture / strategic direction decisions. Skip for UI tweaks, copy changes, or well-scoped small features.

If triggered: hostile critic persona, find assumptions that will fail, identify competitor attack vectors, specify minimum validation requirements.

Output: idea.md (finalized)

Phase 3: Document generation

Uses the same logic as the standalone doc-generator skill.

Read references/doc-templates.md for templates.

3.1: PRD (prd.md)

Generate full PRD from idea.md.

3.1.1: User perspective validation

AI-simulated user reads the PRD. Results logged as [REVIEW] type — clearly labeled as AI simulation, not human decision.

If simulated user's understanding diverges from intent → flag before proceeding.

3.2: Tech spec (tech-spec.md)

Including Skills dispatch table referencing references/skills-registry.md.

3.3: CLAUDE.md

Code conventions, Git workflow, testing, agent boundaries.

3.4: Dev guide (dev-guide.md)

Step-by-step with Skill callouts and human decision point markers.

Phase 4: Development kickoff

  1. Load CLAUDE.md and tech-spec.md into Claude Code
  2. Follow dev-guide.md phase by phase
  3. Log significant decisions with /decision-log
  4. For technical dilemmas, use Best Minds instant summon:

"Who in the world knows [domain] best? Answer as that person. Give me a recommendation with conviction, not 'both have pros and cons.'"

Output files

| File | Purpose | Reader | |------|---------|--------| | idea.md | Final product concept | You | | prd.md | Product requirements | You + Claude Code | | tech-spec.md | Technical architecture | Claude Code | | CLAUDE.md | Dev conventions | Claude Code | | dev-guide.md | Operator manual | You | | decision-log.md | Decision + review record | You / portfolio | | prompts/*.md | Ready-to-paste Gemini prompts | You → Gemini |

Session management

This workflow uses multiple Gemini sessions. Label your browser tabs:

| Tab | Contains | Used in | |-----|----------|---------| | "Role B" | Phase 1 Gemini (knows your idea) | Phase 1B, optionally 2C | | "Blind Review" | Phase 2 Gemini (no context) | Phase 2B only |

Never paste into the wrong tab — it defeats the s

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