PRD-Led Context Engineering

Memory as Infrastructure — an operating system for building products with AI agents

Your AI partner is brilliant in one session and amnesiac by the next. This repository is the fix: a fork-ready methodology that turns documentation into a knowledge graph humans and AI navigate together — so the 50th session is smarter than the 1st.

Quick Start · The Idea · The Lifecycle · The Skills · Live Demo Views

⭐ If this changes how you build with AI, star the repo — stars put this method in front of the next team drowning in context drift.

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The Problem: AI forgets. Teams drift.

Every era of software solved memory its own way — and broke it its own way:

• Waterfall had Static Memory. Write everything down upfront: certainty, at the cost of change.
• Agile moved fast and created Fragmented Memory. Knowledge scattered across tickets, wikis, and chats; shared understanding evaporated.
• AI-assisted building adds a third failure: Amnesiac Memory. The model that architected your system yesterday has never heard of it today.

The common mistake is treating these as tooling problems. They are memory problems.

PRD-Led Context Engineering builds Shared Memory: it treats AI as a team member, not a tool, and keeps documentation synchronized with code so humans and AI navigate the same truth.

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The Idea: Memory as Infrastructure

This methodology comes from two converging experiences.

Leading human teams — alignment always followed the same pattern: rally around a single Source-of-Truth artifact and the team moves as one. Without it, even great talent drifts.

Partnering with AI — sometimes the model performs at a senior level, sometimes it hallucinates. The variable was never the model's intelligence. It was the Context Density provided: rich, structured context in; senior-level output out.

The convergence: documentation is not an afterthought. Documentation is the infrastructure of shared memory.

The Golden Rule: If it isn't part of the memory infrastructure, it isn't true.

So every durable decision gets a Unique ID (UJ-101, BR-004, API-045) in a Source-of-Truth file. That ID is a memory node with weight: when the AI references BR-004, it isn't guessing — it's retrieving a specific, validated decision you encoded. The linked network of IDs across files is the Knowledge Graph, and it lives in plain markdown, in your repo, under version control.

The 4 Pillars

1. Just-in-Time Context — IDs let you load only what a task needs. No repo-dumps, no noise, fewer hallucinations.
2. The Documentation Ecosystem — PRD ↔ EPICs ↔ SoT connected by links, skills, and hooks. When logic changes, both human and AI know.
3. Context Validation — context is measured like code. Too sparse, the AI drifts; too dense, it chokes. The repo scores itself (see Readiness Scoring).
4. Progressive Documentation — update in place, never copy. No PRD_v2.md, ever. One document, many versions, single current reality.

The Cognitive Shift

This changes how work is measured, not just how it's tooled:

Traditional Agile	PRD-Led Context Engineering	The Shift
Sprints	Context Windows	We don't time-box based on dates; we scope-box based on cognitive capacity.
User Stories	Prompts	We don't write descriptions; we engineer prompts that deterministically load context.
Tribal Knowledge	Source of Truth	If it isn't in the Knowledge Graph (SoT/), it doesn't exist.
Standups	Documentation Hooks	No status meetings. Event-driven hooks handle context loading, gate checks, and memory handoffs.
Project Management	Context Governance	We don't task-manage people. The system gates execution until context is verified valid.

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What's in the box

Everything below ships in this repo, works offline, and forks in one click:

Feature	What it gives you
🧠 The Knowledge Graph	14 SoT files, 21 ID types, zero databases — durable memory in markdown
📈 The Progressive PRD	A gated v0.1 → v1.0 lifecycle that stops AI from one-shotting your architecture
🛠 47 Skills	Stage playbooks from problem framing to crossing the chasm — Dunford, Hormozi, Moore, Torres built in
📊 Readiness Scoring	The repo computes whether you're ready to advance — and what to fix first
🫀 The Development Graph	@implements tags bridge code to specs; drift surfaces as a verdict, not a surprise
📰 The Human Review Layer	Every SoT file rendered as a styled, hyperlinked page its reviewer actually wants to read
🤖 The Agent Squad	Four role agents with persistent memory, coordinated through files instead of meetings

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🧠 Feature: A Knowledge Graph in plain markdown

The pitch: long-term product memory with no database, no SaaS, no lock-in — just files with discipline.

The architecture is 3 + 1 + SoT + Temp, designed to manage Context Density for both human cognitive load and AI context windows:

1. Executive Functions — orient attention. Files load stable→volatile to maximize prompt-cache hits:
   • README.md — the Dashboard (where am I? what is active?)
   • PRD.md — the Strategy (why and what)
   • CLAUDE.md — the Physics (how the AI must behave)
2. Focus Memory — epics/: the only variable state. An EPIC frames one problem as one context window.
3. Long-Term Memory — SoT/SoT.*.md: the immutable facts. Business Rules (BR-), User Journeys (UJ-), API Contracts (API-), and 18 more ID types. Nothing duplicated; everything referenced by ID.
4. Short-Term Memory — temp/: the scratch pad. Files attach to the active EPIC and get harvested to SoT before the EPIC closes.

Just-in-Time Context: instead of dumping documentation into the context window, reference specific IDs (UJ-101, API-002). Fewer input tokens, deeper understanding.

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📈 Feature: The Progressive PRD

The pitch: the "One-Shot" — asking AI to build the whole app at once — produces generic code and rapid drift. The Progressive PRD makes that impossible by design.

PRD.md is a gated workflow, not a document. The AI focuses on one stage at a time, and no stage advances until its Definition of Done is met:

Version	Name	Focus	Definition of Done (DoD)
v0.1	Spark	Problem & Outcomes	Problem defined, Outcomes measurable, Open Questions list.
v0.2	Market Definition	Segments & ICP	Segments sized, "Not For" defined, Business Rules (BR-) created.
v0.3	Commercial Model	Value & Pricing	Competitors profiled, Pricing model, Monetization rules.
v0.4	User Journeys	Personas & Flows	Core journeys mapped (UJ-), Dependencies (API-) noted.
v0.5	Red Team Review	Risks & Feasibility	Risks (Market/Tech) identified, Mitigations linked to tests.
v0.6	Architecture	Technical Strategy	Stack selected, API contracts (API-) drafted, ARC- conformance rules, Cost guardrails.
v0.7	Build Execution	Implementation Loop	Code tested (TEST-), SoT updated, code traced to specs (Development Graph), Epic loop execution.
v0.8	Release & Deployment	Operational Readiness	Runbooks (RUN-), Monitoring (MON-, MON-DRIFT-), Rollback plan, Changelog system, MOPS handoff.
v0.9	Launch	Go-to-Market	Positioning (Dunford), Offer (Hormozi), Channels (ORB), Launch metrics (KPI-), Feedback channels (CFD-), Tactical playbooks (AEO, alternatives, outreach, HN/Reddit).
v1.0	Growth	Market Adoption	Adoption stage (ADO-STAGE-), Beachhead (ADO-BEACHHEAD-), Whole product (ADO-WHOLE-), References (ADO-REF-), Continuous discovery, Case studies, Testimonials.

Why gates work: constrained focus prevents the AI from guessing the architecture before it understands the users; deep focus produces meaningful IDs; the result is not just a working product but a desirable one.

The paradox that makes it practical: gates provide focus; the ecosystem provides agility. Because documentation is modular and interlocked, you can revisit any stage just-in-time — customer feedback during Build doesn't restart the plan, it updates the BR- rules and lets hooks propagate the change.

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🛠 Feature: 47 skills, one for every decision

The pitch: the lifecycle isn't advice — it's executable. Every stage ships with skills that know what to consume, what IDs to produce, and which gate they feed.

• 41 stage skills (prd-v01-* → prd-v10-*): problem framing, competitive landscape, pricing, persona definition, journey mapping, risk discovery, architecture design, epic scoping, test planning, release planning, GTM strategy, case studies…
• 6 methodology operators (ghm-*): gate checks, SoT building, ID registration, insight harvesting, status sync.
• Named frameworks, encoded: April Dunford positioning, Alex Hormozi offer construction, Owned/Rented/Borrowed channel allocation, Geoffrey Moore chasm crossing, Teresa Torres continuous discovery, Rob Fitzpatrick Mom Test interviews.
• Three depth modes — quick (founder gut-check, <15 min), standard (default), deep (investor-ready, with assumption logs) — so the method scales from solo founder to team.

Every skill emits Consumes / Produces sections in SoT IDs, which is what keeps the knowledge graph connected as you move through stages.

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📊 Feature: A repo that scores its own readiness

The pitch: before advancing a stage or starting an EPIC, the repo already knows whether you're ready — and why not.

Readiness is a three-layer graph over the artifacts you already author:

1. SoT files — scored on entry count, depth, cross-reference density, and orphan rate. A placeholder file scores 0.
2. EPICs — inherit the readiness of every SoT file they reference. Dangling refs and unresolved assumptions surface as unmet criteria, each citing the file that caused it.
3. PRD stage — answers "can we advance v0.X → v0.Y?" from gate criteria plus the layers below.

All three write to one file — status/readiness.json — with causal links intact: an EPIC's unmet criterion points at its caused_by SoT file; the top blockers are ranked by downstream impact. The highest-leverage fix is rarely the lowest-scoring file — it's the lowest-scoring file blocking the most EPICs. The system tells you which.

python scripts/readiness.py run        # compute all layers + print report
python scripts/readiness.py status     # print last-computed report
python scripts/readiness.py run --json # machine-readable output for hooks/CI

Exit codes 0/1/2 map to PASS / WARN / BLOCK (thresholds: warn=70, block=50, overridable per item). The ghm-gate-check skill delegates here for stage-advancement decisions.

🫀 Feature: Code that traces back to specs

Once building starts (v0.7), the code itself joins the knowledge graph. An AST pass extracts code nodes into status/devgraph.json; the @implements / @verifies tags you write under rule 04 become bridge edges linking each code unit to the spec it realizes. Readiness then measures reality, not just spec health:

• implementation_coverage — which scoped specs actually have implementing code
• architecture_conformance — do the ARC- rules still hold in the as-built system (drift = a violate verdict, not a surprise in review)

Untagged code shows up as an orphan node — a context leak you can see. The same devgraph.json powers the HeartBeat visualizer: a live pulse of built / unbuilt / drifted. See docs/DEVELOPMENT_GRAPH.md.

Deeper reading: .claude/rules/07-readiness-protocol.md · docs/READINESS_PROTOCOL.md

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📰 Feature: The Human Review Layer

The pitch: markdown SoT files are optimized for agents and diffs. Humans reviewing a gate deserve a better reading surface — so every SoT file ships with a styled, hyperlinked HTML view in the format its natural reviewer already expects. Start at SoT/html/index.html (opens from file://, no build step, no JS).

The contract: markdown stays authoritative; the HTML is a render. Entry anchors equal unique IDs (SoT.BUSINESS_RULES.html#BR-001), and every cross-reference is a hyperlink — a reviewer walks the knowledge graph by clicking, the same way an agent walks it by ID.

The Atlas (index.html) — registry of every view, ID anatomy, graph patterns	User Journeys — trigger → steps → value moment, the way design reviews read flows
API Contracts — Swagger-style reference with method plates and status codes	Data Model — ER-style entity cards with keys and a relationship map
Customer Feedback — quote-first insight cards with decision stamps	Adoption — Moore lifecycle curve with the chasm and a "you are here" marker

Each of the 13 pages serves a different reviewer: policy register for BR-, ADRs + topology diagram for TECH-/ARC-, Storybook-style specimens for DES-, Given/When/Then cards for TEST-, an ops console for DEP-/RUN-/MON-/SEC-, a retro playbook for LL-, a vendor context map for INT-. The full schema-per-ID-type and persona-per-view rationale lives in SoT/html/README.md.

Screenshots are generated — when the pages change, refresh them with python3 SoT/html/screenshot.py (Playwright + Chromium; see SoT/html/README.md for setup) and commit the regenerated PNGs with the change.

Next direction (concept): these pages render SoT outward for review. A proposed third artifact class — deliverables — would add an input mode where a human contributes judgment (rank, select, acknowledge) and the page emits paste-ready SoT markdown. See docs/DELIVERABLES_CONCEPT.md.

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🤖 Feature: An agent squad with persistent memory

The pitch: four role agents that remember, coordinate through files, and get smarter every EPIC — no standups required.

• horizon (Strategy, v0.1–v0.5) · studio (Design, v0.3–v0.6) · devlab (Build, v0.6–v0.8) · metro (Ops, v0.9–v1.0)
• Memory that persists: each agent accumulates Feedback, Patterns, Decisions, and Handoff Notes in its MEMORY.md. A SubagentStop hook actively extracts memories from the conversation. During EPIC harvest, cross-EPIC insights are promoted to SoT/SoT.LESSONS_LEARNED.md as durable LL- entries.
• Event-driven hooks instead of meetings: SessionStart injects read order, UserPromptSubmit checks context density, PreToolUse verifies an active EPIC before code writes, Stop reminds on SoT cascade updates. Behavior is standardized by HOOK_CONTRACT.md.
• Multi-agent EPICs without the telephone game: a Synthesis Checkpoint forces the coordinator to produce self-contained worker prompts before implementation begins — workers never see degraded second-hand context.
• File-based standups: the Squad Status section below shows agent activity and EPIC state at a glance, updated by ghm-status-sync.

Repository structure

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├── README.md               # Dashboard, structure, and status
├── PRD.md                  # Product definition (Progressive PRD)
├── CLAUDE.md               # The agent's operating instructions
├── epics/                  # Active Context Windows (Tasks)
├── SoT/                     # Shared Memory Store (SoT.* files + html/ review layer)
├── temp/                    # Scratch Pad for explorations and audits
└── .claude/                 # Methodology runtime (skills, hooks, agents)
    ├── skills/              # 41 stage skills (prd-v*) + 6 operators (ghm-*)
    ├── hooks/               # Session/user/stop hooks + subagent memory hooks
    ├── agents/              # Role agents with persistent MEMORY.md
    ├── domain-profile.yaml  # ID registry + skill taxonomy
    └── settings.json        # Hook wiring and execution config

Agent Note: .claude/ can be replaced with .gemini/, .codex/, or any other agent structure, but the skills, hooks, and agent model here were built with Anthropic's documentation model in mind.

Fork Note: this README.md explains the methodology. When you fork for a product, copy README_template.md to README.md and customize it.

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🚀 Quick Start

# 1. Fork this repo for your product, then:
cp README_template.md README.md     # your product dashboard replaces this page

# 2. Open the repo in Claude Code — hooks load the read order automatically.

# 3. Start the lifecycle at the beginning:
#    "Let's frame the problem"  →  triggers prd-v01-problem-framing
#    The skill produces CFD- evidence IDs and fills PRD.md v0.1.

# 4. Advance only through gates:
python scripts/readiness.py run      # are we ready for v0.2?

From there, the method drives itself: each stage's skills consume the previous stage's IDs, the readiness score tells you when to advance, and the knowledge graph grows with every decision. No subscriptions, no servers, no lock-in — fork and go.

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How to use this repo today

The methodology is fork-native: everything runs from files in your repo, with no services to stand up.

1. Fork it per product — this repo is the template; each product gets its own copy with its own PRD, SoT graph, and EPICs (Quick Start above).
2. Or self-install into an existing repo — run the self-install path instead of forking the whole repo (see below).
3. Work through the gates — let the stage skills drive: each one tells you what it consumes, what IDs it produces, and which gate it feeds. Run python scripts/readiness.py run before advancing.
4. Keep the graph honest — decisions land in SoT/ with IDs before or during the change, never after. Review them as humans through SoT/html/.
5. Harvest every EPIC — temp/ notes and agent memories get promoted to durable LL- entries at EPIC close, so the next session starts smarter.

Adopt into an existing repo (self-install — prototype)

You don't have to fork. The self-install path drops the framework into a fresh or existing repo without clobbering product content — the subscription-native pattern borrowed from ZQadus/Xantham-system-blueprint: ship a blueprint a fresh Claude Code session executes, so all cost lands on your Pro/Max plan, not the metered API.

# Deterministic CLI (from a clone of this repo):
bash install.sh --target /path/to/your/repo --profile product --dry-run   # preview
bash install.sh --target /path/to/your/repo --profile product             # install

Or paste the one-line bootstrap from BLUEPRINT.md into a fresh Claude Code session and let the ghm-self-install wizard drive it. Both paths read .claude/install-manifest.yaml (framework vs. product file classes), are idempotent, and merge into an existing .claude/settings.json rather than overwriting it.

Still on the roadmap

The self-install path is the foundation — fuller distribution is next:

• MCP server — the knowledge graph as a queryable service: look up any ID, traverse cross-references, and pull readiness scores from any MCP-capable agent, without loading files into context.
• Packaged Claude Code plugin — the skills, hooks, and agent squad as a marketplace one-click, plus Xantham-style hardening (Docker audit sandbox, signed CHECKSUMS.sha256).

Watch the repo to catch these when they land. The fork and self-install paths both work end-to-end today.

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Squad Status

Agent Activity

Agent	Role	Last Active	Current EPIC	Status
horizon	Strategy (v0.1-v0.5)	—	—	idle
studio	Design (v0.3-v0.6)	—	—	idle
devlab	Build (v0.6-v0.8)	—	—	idle
metro	Ops (v0.9-v1.0)	—	—	idle

EPIC Status

EPIC	State	Lead	Last Updated
(no active EPICs)	—	—	—

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Contributing

Thank you for helping refine PRD-Led Context Engineering. This repository is not just a codebase; it is a living system of Memory as Infrastructure.

Core Philosophy

Before contributing, read:

1. README.md — this page: the methodology and dashboard.
2. CLAUDE.md — the Agent Operating Instructions.

The goal is always Context Density: exactly the right information, at exactly the right time, for humans and AI alike.

Ways to Contribute

Refine the methodology

• Templates: improve SoT/ templates, the HTML review layer, or epics/EPIC_TEMPLATE.md.
• Skills & workflows: sharpen a stage skill, suggest automation hooks, or improve Source-of-Truth management.
• Documentation: clarify the rules of the road in this README.

Report friction

• A lifecycle gate that slows you down without adding value? Tell us.
• The AI struggling to find context? Report it as a Context Leak.

Getting Started

1. Fork & branch for your feature or fix.
2. Follow the lifecycle — even meta-changes respect the spirit of the gated workflow.
3. Traceability — durable new concepts get an ID (BR-XXX, UJ-XXX).

Contribution Standards

• Terminology: "PRD-Led Context Engineering", "Source of Truth", and "EPICs", consistent with this README.
• Links: relative links to files (e.g., [Link](README.md)), never absolute paths.
• Tone: professional, prescriptive, rigorous.

Questions?

Open a GitHub Issue — and if the method earned it, leave a star on the way out. ⭐