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kasfulk/chain-of-truth

Productivity workflow

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Validated artifacts as the Source of Truth (SoT) — not prompts.

概要

Validated artifacts as the Source of Truth (SoT) — not prompts. A workflow for AI-assisted software development that replaces prompt-driven development with Source-of-Truth-driven development. Instead of scattering requirements across chat conversations, CoT produces validated artifacts that carry project knowledge, making prompts simple execution instructions. SRS → Product Structure (IA + Design System + User Flows) → HiFi Prototype → Data Model → UCIC → Implementation → Testing AI optimizes , not prompts. When context (validated artifacts) is reliable, prompts become simple. When context is unreliable, prompt engineering is endless. - Building a new feature or application with AI - Planning a project spanning multiple sessions - Reducing inconsistency in AI outputs - Structuring requirements traceably 1. Run the skill: /chain-of-truth 2. Follow the 7 phases — each phase has dependencies (e.g. Data Model comes from User Flows, not from the SRS) 3.

README

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Chain of Truth

Validated artifacts as the Source of Truth (SoT) — not prompts.

A workflow for AI-assisted software development that replaces prompt-driven development with Source-of-Truth-driven development. Instead of scattering requirements across chat conversations, CoT produces validated artifacts that carry project knowledge, making prompts simple execution instructions.

7 phases: SRS → Product Structure (IA + Design System + User Flows) → HiFi Prototype → Data Model → UCIC → Implementation → Testing

Philosophy

AI optimizes context, not prompts. When context (validated artifacts) is reliable, prompts become simple. When context is unreliable, prompt engineering is endless.

Traditional vibe coding:
  Requirement → Prompt → AI → Prompt Revision → AI → More Prompts → AI
  (project knowledge scattered across conversations)

Chain of Truth:
  Requirement → Source of Truth → AI
  (SoT carries knowledge, prompts become execution instructions)

7 Phases & Source of Truth Chain

SRS (SoT #1)
  ↓
IA (SoT #2) + Design System (SoT #3) + User Flows (SoT #4)   [parallel]
  ↓
HiFi Prototype (SoT #5)
  ↓
Data Model (SoT #6)   [from User Flows, NOT directly from SRS]
  ↓
UCIC (SoT #7)         [from User Flows + Data Model]
  ↓
Implementation        [Frontend: HiFi+UCIC, Backend: DataModel+UCIC]
  ↓
Testing               [from User Flows + UCIC, not from code]

When to Use

  • Building a new feature or application with AI
  • Planning a project spanning multiple sessions
  • Reducing inconsistency in AI outputs
  • Structuring requirements traceably

Quick Start

  1. Run the skill: /chain-of-truth
  2. Follow the 7 phases in order — each phase has dependencies (e.g. Data Model comes from User Flows, not from the SRS)
  3. Use the templates in templates/ to generate artifacts
  4. Validate each artifact before treating it as a Source of Truth for downstream work
  5. Implement with minimal prompts that reference artifacts, not describe features

Expected docs/ layout

docs/
  srs.md                        ← SoT #1
  information_architecture.md   ← SoT #2
  design_system.md              ← SoT #3
  user_flows/
    uc-001-[name].md            ← SoT #4 (one file per use case)
  data_model.md                 ← SoT #6
  system_logics/                ← SoT #7 (UCIC, one per use case)
    uc-001-[name].md
  test_plan.md
  test_cases.md
  test_execution_sheet.md

Structure

SKILL.md          — Full skill definition, phase details, traceability, evaluation metrics
templates/        — Templates for each artifact type (SRS, IA, design system, user flow,
                    data model, UCIC, test plan, test cases, test execution sheet)
.headroom/        — Persistent memory store
README.md         — This file

See SKILL.md for complete phase details, traceability paths, revision loops, pitfalls, and evaluation metrics.

Credit

This skill is based on the Chain of Truth academic research by:

GitHub で README 全文を見る

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インストール

npx skillfish add kasfulk/chain-of-truth
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