learnship

learnship

Agentic engineering done right. Learn as you build. Build with intent.
A context engineering and spec-driven development system for Claude Code, Windsurf, Cursor, OpenCode, Gemini CLI, and Codex CLI.

Install · 5 Commands · Phase Loop · How It Works · All Workflows · Full Docs · Changelog

⚡ Install

npx learnship

Works on Mac, Windows, and Linux. Requires Node.js ≥ 22 and Git. The installer auto-detects your platform.

npx learnship --global   # all projects
npx learnship --local    # this project only
npx learnship --all --global  # all 6 platforms at once

Then open your AI agent and type:

/ls

That's it. /ls tells you where you are, what to do next, and offers to run it.

Platform-specific install + marketplace options

Platform	Install command	Invoke commands as
Windsurf	npx learnship --windsurf --global	/ls, /new-project
Claude Code	npx learnship --claude --global	/learnship:ls, /learnship:new-project
Cursor	/add-plugin learnship (marketplace)	@learnship rules load automatically
OpenCode	npx learnship --opencode --global	/learnship-ls, /learnship-new-project
Gemini CLI	npx learnship --gemini --global	/learnship:ls, /learnship:new-project
Codex CLI	npx learnship --codex --global	$learnship-ls, $learnship-new-project

Via marketplace (no terminal required):

# Claude Code — community marketplace
/plugin marketplace add FavioVazquez/learnship-marketplace
/plugin install learnship@learnship-marketplace

# Cursor — after marketplace approval
/add-plugin learnship

# Gemini CLI — native extension
gemini extensions install https://github.com/FavioVazquez/learnship

Custom install directory:

npx learnship --claude --global --target /path/to/custom/dir

--target overrides the default platform directory. Works with install and uninstall on all 6 platforms.

learnship is published to npm — npx learnship pulls the latest release directly. No github: prefix, no clone needed. The same bin/install.js runs regardless of install method.

🗺️ The 5 Commands

learnship has 57 workflows. You need five. Everything else surfaces naturally from /ls.

Command	What it does	When to use
/ls	Show status, recent work, and next step (and offer to run it)	Start every session here
/next	Read state and immediately run the right next workflow	When you just want to keep moving
/new-project	Full init: questions → research → requirements → roadmap	Starting a new project
/quick "..."	One-off task with atomic commits, no planning ceremony	Small fixes, experiments
/help	All 57 workflows organized by category	Discovering capabilities

Tip: /ls works for both new and returning users. No project? It explains learnship and offers /new-project. Returning? It shows your progress and suggests exactly what to do next.

🔄 The Phase Loop

Every feature ships through a 7-step loop:

flowchart LR
    DP["/discuss-phase N<br/>Capture decisions"]
    PP["/plan-phase N<br/>Research + plans"]
    EP["/execute-phase N<br/>Build + commit"]
    VW["/verify-work N<br/>UAT + diagnose"]
    RV["/review<br/>Multi-persona review"]
    SH["/ship<br/>Test → PR"]
    CP["/compound<br/>Capture knowledge"]

    DP --> PP --> EP --> VW
    VW --> RV --> SH --> CP
    CP -->|"next phase"| DP
    VW -->|"all done"| DONE["✓ /complete-milestone"]

Step	Command	What happens
1. Discuss	/discuss-phase N	You and the agent align on implementation decisions before any code
2. Plan	/plan-phase N	Agent researches the domain, creates executable plans, verifies them
3. Execute	/execute-phase N	Plans run in dependency order, one atomic commit per task
4. Verify	/verify-work N	You do UAT; agent diagnoses any gaps and creates fix plans
5. Review	/review	Multi-persona code review through 6 lenses (v2.0)
6. Ship	/ship	Test → lint → commit → push → PR (v2.0)
7. Compound	/compound	Capture what you learned as searchable documentation (v2.0)

Just starting? /ls or /next will route you into the right step automatically.

🏗️ How It Works

Three integrated layers that reinforce each other:

Layer	What it does
Workflow Engine	Spec-driven phases → context-engineered plans → wave-ordered execution → verified delivery
Learning Partner	Neuroscience-backed checkpoints at every phase transition: retrieval, reflection, spacing, struggle
Design System	21 impeccable steering commands for production-grade UI: /audit, /critique, /polish, and more

🌐 Platform Support

Each platform gets the best experience it supports:

Feature	Windsurf	Claude Code	OpenCode	Gemini CLI	Codex CLI
Slash commands	✓	✓	✓	✓	$skills
Real parallel subagents	—	✓	✓	✓	✓
Parallel wave execution	—	✓ opt-in	✓ opt-in	✓	✓ opt-in
Agent personas (17)	model_decision rules	Task() subagents	Task() subagents	Task() subagents	Task() subagents
Interactive questions	ask_user_question	AskUserQuestion	question	ask_user	request_user_input
Session hooks	—	✓	—	✓	—
Skills (native @invoke)	✓	—	—	—	—
Skills (context files)	✓	✓	✓	✓	✓

Parallel subagents: On Claude Code, OpenCode, and Codex, execute-phase can spawn a dedicated executor per plan within a wave, each with its own 200k context budget. Enable with "parallelization": { "enabled": true } in .planning/config.json. Up to 5 concurrent agents per wave by default. All platforms default to sequential (always safe).

What is learnship?

learnship is an agent harness — the scaffolding that makes your AI coding agent actually reliable across real projects.

Every serious AI coding tool converges on the same architecture: a simple execution loop wraps the model, and the harness decides what information reaches the model, when, and how. The model is interchangeable. The harness is the product.

learnship gives you that harness as a portable, open-source layer that adds:

• Persistent memory. /new-project generates an AGENTS.md loaded automatically every session. No more repeating yourself.
• Structured process. A repeatable phase loop with spec-driven plans, wave-ordered execution, and UAT-driven verification.
• Knowledge compounding. /compound captures solved problems. /review runs multi-persona code review. /ship runs the full delivery pipeline.
• Security & recovery. /secure-phase for STRIDE verification. /forensics for post-mortem. /undo for safe revert.
• Session intelligence. Hooks, context profiles, interactive questions, agent delegation. (v2.2 details →)
• Built-in learning. Neuroscience-backed checkpoints at every phase transition so you understand what you shipped.

What problem does it solve?

If you've used AI coding assistants for more than a few sessions, you've hit this wall:

The agent forgets everything. Each session starts from scratch. Decisions get repeated. Code quality drifts. You ship fast but understand less.

This is a harness problem, not a model problem. The same model scores 42% with one scaffold and 78% with another. Same model. The only variable is the harness.

learnship solves this with progressive disclosure — context revealed incrementally, not dumped upfront. The right files, decisions, and phase context reach the agent exactly when needed.

Without learnship	With learnship
Context resets every session	AGENTS.md loaded automatically every conversation
Ad-hoc prompts, unpredictable results	Spec-driven plans, verifiable deliverables
Architectural decisions get forgotten	DECISIONS.md tracked and honored by the agent
Everything dumped into context at once	Phase-scoped context: only what this step needs
You ship code you don't fully understand	Learning checkpoints build real understanding at every step
UI looks generic, AI-generated	impeccable design system prevents AI aesthetic slop

Who is it for?

Anyone who wants to build and ship real products with AI agents — founders, designers, researchers, makers, not just developers.

It's the right tool if:

• You're building a real project and want the AI to stay aligned across sessions
• You're learning while building and want to actually understand what gets shipped
• You care about code quality and UI quality beyond "it works"
• You want parallel agent execution on Claude Code, OpenCode, or Gemini CLI
• You've felt the frustration of context loss: repeating yourself while the agent forgets

It's probably overkill for one-off scripts. Use /quick for that.

📚 Documentation

faviovazquez.github.io/learnship

• Getting Started: install, first project, the 5 commands
• Platform Guide: Windsurf, Claude Code, Cursor, OpenCode, Gemini CLI, Codex CLI
• Core Concepts: phase loop, context engineering, planning artifacts
• Skills: 11 @agentic-learning actions + 21 impeccable design commands
• Workflow Reference: all 57 workflows
• Configuration: full schema, speed presets, parallelization

🆕 What's New

What's new in v2.3

v2.3 adds 5 new agent personas, Windsurf-native persona adoption via model_decision rules, and inline <persona_context> blocks across all 18 persona-aware workflows:

5 new agent personas: project-researcher (domain ecosystem research for /new-project), research-synthesizer (synthesizes 4 research files into SUMMARY.md), roadmapper (creates phased roadmaps from requirements), phase-researcher (focused research for /plan-phase and /research-phase), doc-verifier (verifies docs match live code). Total agent pool: 17 specialist personas.

Windsurf model_decision rules: Agent personas are now installed as .windsurf/rules/learnship-{name}.md with trigger: model_decision frontmatter. Windsurf's Cascade sees the rule description in every system prompt and reads the full persona when context is relevant — the native equivalent of Claude Code's subagent spawning.

Inline <persona_context> blocks: All 18 workflows that reference agent personas now include inline persona instructions directly in the workflow text. This works on every platform — no special tool needed. Belt-and-suspenders with @./agents/ file references and platform-native mechanisms.

Codex sandbox map: All 17 agent personas now have per-agent sandbox modes (read-only for checkers/auditors, workspace-write for executors/planners).

Published agents synced: The agents/ directory now contains all 17 agents with proper frontmatter (name:, description:, tools:, color:) — in sync with the source learnship/agents/ directory.

What's new in v2.2

v2.2 adds session intelligence, structured interactivity, and research templates:

Session hooks (Claude Code + Gemini CLI): 4 hooks installed via settings.json — statusline showing context usage, context monitor that warns before context runs out, prompt guard that scans .planning/ writes for injection patterns, and session state that injects project orientation at startup.

Context profiles: Set "context": "dev" (default), "research", or "review" in config.json to control agent output style. Switch with /settings.

Interactive questions: 14 workflows present decisions via your platform's native structured question tool — clickable cards on Claude Code, dropdowns on Windsurf, etc. install.js rewrites the tool name per platform automatically.

Agent persona delegation: 18 workflows use inline <persona_context> blocks and @./agents/ references for sequential persona adoption, with Task() subagent spawning when parallelization is enabled.

Research templates: 5 structured fill-in-the-blanks templates (STACK.md, FEATURES.md, ARCHITECTURE.md, PITFALLS.md, SUMMARY.md) that prevent the AI from skipping file writes.

Upgrade safety: SHA-256 file manifest after every install. Locally modified files detected and backed up before overwriting. Run /reapply-patches to restore customizations.

What's new in v2.1

v2.1 adds 8 new workflows, 5 new references, 3 new templates, and 2 new agents:

Category	New workflows
Security	/secure-phase — per-phase STRIDE threat verification
Documentation	/docs-update — generate and verify project docs against codebase
Recovery	/forensics — post-mortem investigation · /undo — safe git revert
Session	/note — zero-friction capture · /session-report — stakeholder summaries
Learning	/extract-learnings — decisions, lessons, patterns, surprises · /milestone-summary — team onboarding

Enhanced: /discuss-phase (scope guardrails + domain probes), /execute-phase (--wave flag + context scaling), /quick (--research --validate --full composable flags), /ideate (--explore Socratic mode).

Optional per-phase: /secure-phase N (STRIDE security), /extract-learnings N (meta-knowledge). Recovery: /forensics (post-mortem), /undo (safe revert).

Agentic Engineering vs Vibe Coding

	Vibe coding	Agentic engineering
Context	Resets every session	Engineered into every agent call
Decisions	Implicit, forgotten	Tracked in DECISIONS.md, honored by the agent
Plans	Ad-hoc prompts	Spec-driven, verifiable, wave-ordered
Outcome	Code you shipped	Code you shipped and understand

🧠 Context Engineering

Every agent invocation is loaded with structured context. Nothing is guessed:

flowchart LR
    subgraph CONTEXT["Loaded into every agent call"]
        A["AGENTS.md<br/>Project soul + current phase"]
        B["REQUIREMENTS.md<br/>What we're building"]
        C["DECISIONS.md<br/>Every architectural choice"]
        D["Phase CONTEXT.md<br/>Implementation preferences"]
    end
    CONTEXT --> AGENT["AI Agent"]
    AGENT --> P["Executable PLAN.md"]
    AGENT --> S["Commits + SUMMARY.md"]

🗂️ AGENTS.md: Persistent Project Memory

/new-project generates an AGENTS.md at your project root. On Windsurf, Claude Code, and Cursor it loads automatically every session. On other platforms, workflows reference it explicitly. Either way: the agent always knows the project, current phase, tech stack, and past decisions.

AGENTS.md                   ← your AI agent reads this every conversation
├── Soul & Principles        # Pair-programmer framing, 10 working principles
├── Platform Context         # Points to .planning/, explains the phase loop
├── Current Phase            # Updated automatically by workflows
├── Project Structure        # Filled during new-project from your answers
├── Tech Stack               # Filled from research results
└── Regressions              # Updated by /debug when bugs are fixed

📖 Workflow Reference: Advanced

These are all 57 workflows. Most users discover them naturally from /ls. Scan this when you want to know if a specific capability exists.

Core Workflow

Workflow	Purpose	When to use
/new-project	Full init: questions → research → requirements → roadmap	Start of any new project
/discuss-phase [N]	Capture implementation decisions before planning	Before every phase
/plan-phase [N]	Research + create + verify plans	After discussing a phase
/execute-phase [N]	Wave-ordered execution of all plans	After planning
/verify-work [N]	Manual UAT with auto-diagnosis and fix planning	After execution
/complete-milestone	Archive milestone, tag release, prepare next	All phases verified
/audit-milestone	Pre-release: requirement coverage, stub detection	Before completing milestone
/new-milestone [name]	Start next version cycle	After completing a milestone

Navigation

Workflow	Purpose	When to use
/ls	Status + next step + offer to run it	Start every session here
/next	Auto-pilot: reads state and runs the right workflow	When you just want to keep moving
/progress	Same as /ls: status overview with smart routing	"Where am I?"
/resume-work	Restore full context from last session	Starting a new session
/pause-work	Save handoff file mid-phase	Stopping mid-phase
/quick [description]	Ad-hoc task with full guarantees	Bug fixes, small features
/help	Show all available workflows	Quick command reference

Phase Management

Workflow	Purpose	When to use
/add-phase	Append new phase to roadmap	Scope grows after planning
/insert-phase [N]	Insert urgent work between phases	Urgent fix mid-milestone
/remove-phase [N]	Remove future phase and renumber	Descoping a feature
/research-phase [N]	Deep research only, no plans yet	Complex/unfamiliar domain
/list-phase-assumptions [N]	Preview intended approach before planning	Validate direction
/plan-milestone-gaps	Create phases for audit gaps	After audit finds missing items

Brownfield, Discovery & Debugging

Workflow	Purpose	When to use
/map-codebase	Analyze existing codebase	Before /new-project on existing code
/discovery-phase [N]	Map unfamiliar code area before planning	Entering complex/unfamiliar territory
/debug [description]	Systematic triage → diagnose → fix	When something breaks
/diagnose-issues [N]	Batch-diagnose all UAT issues by root cause	After verify-work finds multiple issues
/execute-plan [N] [id]	Run a single plan in isolation	Re-running a failed plan
/add-todo [description]	Capture an idea without breaking flow	Think of something mid-session
/check-todos	Review and act on captured todos	Reviewing accumulated ideas
/add-tests	Generate test coverage post-execution	After executing a phase
/validate-phase [N]	Retroactive test coverage audit	After hotfixes or legacy phases

Decision Intelligence

Workflow	Purpose	When to use
/decision-log [description]	Capture decision with context and alternatives	After any significant architectural choice
/knowledge-base	Aggregate all decisions and lessons into one file	Before starting a new milestone
/knowledge-base search [query]	Search the project knowledge base	When you need to recall why something was built a certain way

Milestone Intelligence

Workflow	Purpose	When to use
/discuss-milestone [version]	Capture goals, anti-goals before planning	Before /new-milestone
/milestone-retrospective	5-question retrospective + spaced review	After /complete-milestone
/transition	Write full handoff document for new session/collaborator	Before handing off or long break

Compounding & Quality (v2.0)

Workflow	Purpose	When to use
/compound	Capture solved problem as searchable documentation	After /debug, /verify-work, or any aha moment
/review	Multi-persona code review (6 lenses)	After /verify-work, before shipping
/challenge	Stress-test scope through product + engineering lenses	Before committing to a milestone or large feature
/ship	Test → lint → commit → push → PR	After review, ready to deploy
/ideate	Codebase-grounded idea generation	Before /discuss-milestone, between milestones
/guard	Safety mode: protect sensitive directories	Working on auth, payments, migrations
/sync-docs	Detect stale documentation	Before /complete-milestone, after refactors

Maintenance

Workflow	Purpose	When to use
/settings	Interactive config editor	Change mode, toggle agents
/set-profile [quality|balanced|budget]	One-step model profile switch	Quick cost/quality adjustment
/health	Project health check	Stale files, missing artifacts
/cleanup	Archive old artifacts	End of milestone
/update	Update the platform itself	Check for new workflows
/reapply-patches	Restore local edits after update	After /update if you had local changes

⚙️ Configuration

Project settings live in .planning/config.json. Set during /new-project or edit with /settings.

Full Schema

{
  "mode": "interactive",
  "granularity": "standard",
  "model_profile": "balanced",
  "learning_mode": "auto",
  "context": "dev",
  "test_first": false,
  "planning": {
    "commit_docs": true,
    "commit_mode": "auto",
    "search_gitignored": false
  },
  "workflow": {
    "research": true,
    "plan_check": true,
    "verifier": true,
    "validation": true,
    "review": true,
    "solutions_search": true,
    "security_enforcement": true,
    "discuss_mode": "discuss",
    "tdd_mode": false
  },
  "parallelization": {
    "enabled": false,
    "plan_level": true,
    "task_level": false,
    "max_concurrent_agents": 5,
    "min_plans_for_parallel": 2
  },
  "gates": {
    "confirm_project": true,
    "confirm_phases": true,
    "confirm_roadmap": true,
    "confirm_plan": true,
    "execute_next_plan": true,
    "issues_review": true,
    "confirm_transition": true
  },
  "safety": {
    "always_confirm_destructive": true,
    "always_confirm_external_services": true
  },
  "review": {
    "auto_after_verify": false
  },
  "ship": {
    "auto_test": true,
    "conventional_commits": true,
    "pr_template": true
  },
  "hooks": {
    "context_warnings": true
  },
  "git": {
    "branching_strategy": "none",
    "phase_branch_template": "phase-{phase}-{slug}",
    "milestone_branch_template": "{milestone}-{slug}"
  }
}

Core Settings

Setting	Options	Default	What it controls
mode	auto, interactive	auto	auto auto-approves steps; interactive confirms at each decision
granularity	coarse, standard, fine	standard	Phase size: 3-5 / 5-8 / 8-12 phases
model_profile	quality, balanced, budget	balanced	Agent model tier (see table below)
learning_mode	auto, manual	auto	auto offers learning at checkpoints; manual requires explicit invocation
context	dev, research, review	dev	Output profile: dev (concise), research (detailed), review (audit-focused)
parallelization.enabled	true, false	false	Parallel subagents per plan on supported platforms
test_first	true, false	false	TDD mode: write failing test first, verify red, implement, verify green
planning.commit_mode	auto, manual	auto	auto commits after each workflow step; manual skips all git commits

Workflow Toggles

Setting	Default	What it controls
workflow.research	true	Domain research before planning each phase
workflow.plan_check	true	Plan verification loop (up to 3 iterations)
workflow.verifier	true	Post-execution verification against phase goals
workflow.validation	true	Test coverage mapping during plan-phase
workflow.review	true	Enable /review suggestions after /verify-work (v2.0)
workflow.solutions_search	true	Search .planning/solutions/ during /plan-phase (v2.0)

Review & Ship Settings

Setting	Default	What it controls
review.auto_after_verify	false	Auto-run /review after /verify-work passes
ship.auto_test	true	Run test suite before shipping
ship.conventional_commits	true	Use conventional commit format
ship.pr_template	true	Auto-generate PR description

Git Branching

branching_strategy	Creates branch	Best for
none	Never	Solo dev, simple projects
phase	At each execute-phase	Code review per phase
milestone	At first execute-phase	Release branches, PR per version

Model Profiles

Agent	quality	balanced	budget
Planner	large	large	medium
Executor	large	medium	medium
Phase Researcher	large	medium	small
Debugger	large	medium	medium
Verifier	medium	medium	small
Plan Checker	medium	medium	small
Solution Writer	medium	medium	small
Code Reviewer	large	medium	medium
Challenger	large	medium	medium
Ideation Agent	large	medium	small

Platform note: Tiers map to the best available model on your platform: large = Claude Opus 4.6 / Gemini 3.1 Pro / GPT-5.4, medium = Claude Sonnet 4.6 / Gemini 3.1 Flash / GPT-5.4-mini, small = Claude Haiku 4.5 / Gemini 3.1 Flash-Lite / GPT-5.4-nano. Windsurf, Cursor, and OpenCode use the platform default model — tiers signal intended task complexity.

Speed vs. Quality Presets

Scenario	mode	granularity	model_profile	Research	Plan Check	Verifier
Prototyping	auto	coarse	budget	off	off	off
Normal dev	auto	standard	balanced	on	on	on
Production	interactive	fine	quality	on	on	on

🧩 Learning Partner

The learning partner is woven into the platform, not bolted on. It fires at natural workflow transitions to build genuine understanding, not just fluent answers.

How it fires

learning_mode: "auto"    → offered automatically at checkpoints (default)
learning_mode: "manual"  → only when you explicitly invoke @agentic-learning

All 11 actions

Action	Trigger	What it does
@agentic-learning learn [topic]	Any time	Active retrieval: explain before seeing, then fill gaps
@agentic-learning quiz [topic]	Any time	3-5 questions, one at a time, formative feedback
@agentic-learning reflect	After execute-phase	Three-question structured reflection: learned / goal / gaps
@agentic-learning space	After verify-work	Schedule concepts for spaced review → writes docs/revisit.md
@agentic-learning brainstorm [topic]	After new-project	Collaborative design dialogue before any code
@agentic-learning struggle [topic]	During quick	Hint ladder: try first, reveal only when needed
@agentic-learning either-or	After discuss-phase	Decision journal: paths considered, choice, rationale
@agentic-learning explain-first	Any time	Oracy exercise: you explain, agent gives structured feedback
@agentic-learning explain [topic]	Any time	Project comprehension log → writes docs/project-knowledge.md
@agentic-learning interleave	Any time	Mixed retrieval across multiple topics
@agentic-learning cognitive-load [topic]	After plan-phase	Decompose overwhelming scope into working-memory steps

Core principle: Fluent answers from an AI are not the same as learning. Every action makes you do the cognitive work, with support rather than shortcuts.

Skills across platforms

Platform	How agentic-learning works
Windsurf	Native skill: invoke with @agentic-learning learn, @agentic-learning quiz, etc.
Claude Code, OpenCode, Gemini CLI, Codex CLI	Installed as a context file in learnship/skills/agentic-learning/. The AI reads and applies the techniques automatically. Reference it explicitly with use the agentic-learning skill or just work normally and it activates at checkpoints.

🎨 Design System

The impeccable skill suite is always active as project context for any UI work. It provides design direction, anti-patterns, and 21 steering commands that prevent generic AI aesthetics. Based on @pbakaus/impeccable.

Commands

Command	What it does
/teach-impeccable	One-time setup: gathers project design context and saves persistent guidelines
/audit	Comprehensive audit: accessibility, performance, theming, responsive design
/critique	UX critique: visual hierarchy, information architecture, emotional resonance
/polish	Final quality pass: alignment, spacing, consistency before shipping
/normalize	Normalize design to match your design system for consistency
/colorize	Add strategic color to monochromatic or flat interfaces
/animate	Add purposeful animations and micro-interactions
/bolder	Amplify safe or boring designs for more visual impact
/quieter	Tone down overly aggressive designs to reduce intensity and gain refinement
/distill	Strip to essence: remove complexity, clarify what matters
/clarify	Improve UX copy, error messages, microcopy, labels
/optimize	Performance: loading speed, rendering, animations, bundle size
/harden	Resilience: error handling, i18n, text overflow, edge cases
/delight	Add moments of joy and personality that make interfaces memorable
/extract	Extract reusable components and design tokens into your design system
/adapt	Adapt designs across screen sizes, devices, and contexts
/onboard	Design onboarding flows, empty states, first-time user experiences

The AI Slop Test: If you showed the interface to someone and said "AI made this", would they believe you immediately? If yes, that's the problem. Use /critique to find out.

learnship integration

Automatic UI standards during execute-phase: When a phase involves UI work, learnship detects it automatically and activates @impeccable frontend-design principles before any code is written. You'll see a banner announcing it. The agent then applies typography, color, layout, and component standards across every task in the phase — not as a post-hoc review but as an active constraint during execution.

Post-action milestone recommendation: After any impeccable action produces recommendations, the agent suggests running /new-milestone to create a dedicated "UI Polish" milestone. This turns impeccable findings into versioned, traceable phases with plans and commits — so improvements don't disappear into chat history. Applying directly is always an option too.

Skills across platforms

Platform	How impeccable works
Windsurf	Native skills: invoke each command directly with /audit, /polish, /critique, etc.
Claude Code, OpenCode, Gemini CLI, Codex CLI	Installed as context files in learnship/skills/impeccable/. The AI reads design principles and anti-patterns automatically. Reference commands explicitly with run the /audit impeccable skill or just ask for UI work and it applies the standards.

💡 Usage Examples

New greenfield project

/new-project              # Answer questions, configure, approve roadmap
/discuss-phase 1          # Lock in your implementation preferences
/plan-phase 1             # Research + plan + verify
/execute-phase 1          # Wave-ordered execution
/verify-work 1            # Manual UAT
/review                   # v2.0: multi-persona code review
/ship                     # v2.0: test → commit → push → PR
/compound                 # v2.0: capture what you learned
                          # Repeat for each phase
/sync-docs                # v2.0: detect stale documentation
/audit-milestone          # Check everything shipped
/complete-milestone       # Archive, tag, done

Existing codebase (brownfield)

/map-codebase             # Structured codebase analysis
/new-project              # Questions focus on what you're ADDING
# Normal phase workflow from here

Quick bug fix

/quick "Fix login button not responding on mobile Safari"

Quick with discussion + verification

/quick --discuss --full "Add dark mode toggle"

Resuming after a break

/ls                       # See where you left off (offers to run next step)
# or
/next                     # Just pick up and go: auto-pilot
# or
/resume-work              # Full context restoration

Scope change mid-milestone

/add-phase                # Append new phase to roadmap
/insert-phase 3           # Insert urgent work between phases 3 and 4
/remove-phase 7           # Descope phase 7 and renumber

Preparing for release

/audit-milestone          # Check requirement coverage, detect stubs
/plan-milestone-gaps      # If audit found gaps, create phases to close them
/complete-milestone       # Archive, tag, done

Debugging something broken

/debug "Login flow fails after password reset"

🧭 Decision Intelligence

Every project accumulates decisions: architecture choices, library picks, scope trade-offs. The platform tracks them in a structured register so future sessions understand why the project is built the way it is.

.planning/DECISIONS.md is the decision register:

## DEC-001: Use Zustand over Redux
Date: 2026-03-01 | Phase: 2 | Type: library
Context: Needed client-side state for dashboard filters
Options: Zustand (simple, no boilerplate), Redux (complex, overkill for scope)
Choice: Zustand
Rationale: 3x less boilerplate, sufficient for current data flow complexity
Consequences: Locks React as UI framework; migration would require state rewrite
Status: active

Populated automatically by:

• discuss-phase surfaces prior decisions before each phase discussion
• plan-phase reads decisions before creating plans (never contradicts active ones)
• debug puts architectural lessons from bugs into the register
• decision-log manually captures any decision from any conversation

Queried by:

• audit-milestone checks decisions were honored in implementation
• knowledge-base aggregates all decisions into a searchable KNOWLEDGE.md

📁 Planning Artifacts

Every project creates a structured .planning/ directory:

.planning/
├── config.json               # Workflow settings
├── PROJECT.md                # Vision, requirements, key decisions
├── REQUIREMENTS.md           # v1 requirements with REQ-IDs
├── ROADMAP.md                # Phase breakdown with status tracking
├── STATE.md                  # Current position, decisions, blockers
├── DECISIONS.md              # Cross-phase decision register
├── KNOWLEDGE.md              # Aggregated lessons (from knowledge-base)
├── research/                 # Domain research from new-project
│   ├── STACK.md
│   ├── FEATURES.md
│   ├── ARCHITECTURE.md
│   ├── PITFALLS.md
│   └── SUMMARY.md
├── codebase/                 # Brownfield mapping (from map-codebase)
│   ├── STACK.md
│   ├── ARCHITECTURE.md
│   ├── CONVENTIONS.md
│   └── CONCERNS.md
├── todos/
│   ├── pending/              # Captured ideas awaiting work
│   └── done/                 # Completed todos
├── solutions/               # Knowledge compounding (from /compound) (v2.0)
│   ├── auth/                # Solutions by category
│   ├── performance/
│   └── ...
├── debug/                    # Active debug sessions
│   └── resolved/             # Archived debug sessions
├── quick/
│   └── 001-slug/             # Quick task artifacts
│       ├── 001-PLAN.md
│       ├── 001-SUMMARY.md
│       └── 001-VERIFICATION.md (if --full)
└── phases/
    └── 01-phase-name/
        ├── 01-CONTEXT.md     # Your implementation preferences
        ├── 01-DISCOVERY.md   # Unfamiliar area mapping (from discovery-phase)
        ├── 01-RESEARCH.md    # Ecosystem research findings
        ├── 01-VALIDATION.md  # Test coverage contract (from /validate-phase)
        ├── 01-01-PLAN.md     # Executable plan (wave 1)
        ├── 01-02-PLAN.md     # Executable plan (wave 1, independent)
        ├── 01-01-SUMMARY.md  # Execution outcomes
        ├── 01-UAT.md         # User acceptance test results
        └── 01-VERIFICATION.md # Post-execution verification

🔧 Troubleshooting

"Project already initialized"

/new-project found .planning/PROJECT.md already exists. If you want to start over, delete .planning/ first. To continue, use /progress or /resume-work.

Context degradation during long sessions

Start each major workflow with a fresh context. The platform is designed around fresh context windows; every agent gets a clean slate. Use /resume-work or /progress to restore state after clearing.

Plans seem wrong or misaligned

Run /discuss-phase [N] before planning. Most plan quality issues come from unresolved gray areas. Run /list-phase-assumptions [N] to see the intended approach before committing to a plan.

Execution produces stubs or incomplete code

Plans with more than 3 tasks are too large for reliable single-context execution. Re-plan with smaller scope: /plan-phase [N] with finer granularity.

Lost track of where you are

Run /ls. It reads all state files, shows your progress, and offers to run the next step.

Need to change something after execution

Use /quick for targeted fixes, or /verify-work to systematically identify and fix issues through UAT. Do not re-run /execute-phase on a phase that already has summaries.

Costs running too high

Switch to budget profile via /settings. Disable research and plan-check for familiar domains. Use granularity: "coarse" for fewer, broader phases.

Working on a private/sensitive project

Set commit_docs: false during /new-project or via /settings. Add .planning/ to .gitignore. Planning artifacts stay local.

Something broke and I don't know why

Run /debug "description of what's broken". It runs triage → root cause diagnosis → fix planning with a persistent debug session.

Phase passed UAT but has known gaps

Run /audit-milestone to surface all gaps, then /plan-milestone-gaps to create fix phases before release.

🚑 Recovery Quick Reference

Problem	Solution
Lost context / new session	/ls or /next
Phase went wrong	git revert the phase commits, re-plan
Need to change scope	/add-phase, /insert-phase, or /remove-phase
Milestone audit found gaps	/plan-milestone-gaps
Something broke	/debug "description"
Quick targeted fix	/quick
Plans don't match your vision	/discuss-phase [N] then re-plan
Costs running high	/settings → budget profile, toggle agents off

📂 Repository Structure

learnship/
├── .windsurf/
│   ├── workflows/          # 57 workflows as slash commands
│   ├── rules/              # 17 model_decision rules (agent personas for Windsurf)
│   └── skills/
│       ├── agentic-learning/   # Learning partner (SKILL.md + references), native on Windsurf + Claude Code
│       └── impeccable/         # Design suite: 21 skills, native on Windsurf + Claude Code
│           ├── frontend-design/ #   Base skill + 7 reference files (typography, color, motion…)
│           ├── audit/           #   /audit
│           ├── critique/        #   /critique
│           ├── polish/          #   /polish
│           └── …14 more/        #   /colorize /animate /bolder /quieter /distill /clarify…
│                               # → on OpenCode/Gemini/Codex: both skills copied to learnship/skills/ as context files
├── commands/               # 57 Claude Code-style slash command wrappers
│   └── learnship/          # /learnship:ls, /learnship:new-project, etc.
├── learnship/              # Payload installed into the target platform config dir
│   ├── workflows/          # 57 workflow markdown files (the actual instructions)
│   ├── references/         # Reference docs (questioning, verification, git, design, learning)
│   └── templates/          # Document templates for .planning/ + AGENTS.md template
├── agents/                 # 17 agent personas (planner, researcher, project-researcher, research-synthesizer, phase-researcher, roadmapper, executor, verifier, debugger, plan-checker, solution-writer, code-reviewer, challenger, ideation-agent, security-auditor, doc-writer, doc-verifier)
├── assets/                 # Brand images (banner, explainers, diagrams)
├── bin/
│   └── install.js          # Multi-platform installer (Claude Code, OpenCode, Gemini CLI, Codex CLI, Windsurf)
├── tests/
│   └── run_all.sh               # 15 test suites, 1200+ checks across 6 platforms
├── SKILL.md                # Meta-skill: platform context loaded by Cascade / AI agents
├── install.sh              # Shell installer wrapper
├── package.json            # npm package (npx learnship)
├── CHANGELOG.md            # Version history
└── CONTRIBUTING.md         # How to extend the platform

🙏 Inspiration & Credits

learnship builds on ideas and work from these open-source projects:

• get-shit-done: spec-driven development with structured workflows and planning artifacts — no sprint ceremonies, just build
• agentic-learning: neuroscience-backed learning techniques woven into the development cycle
• impeccable: frontend design quality system with auditing and refinement actions
• compound-engineering: the philosophy that each unit of engineering work should make subsequent units easier — compounding knowledge through structured review and documentation
• superpowers: complete development workflow for coding agents with subagent-driven execution, TDD enforcement, and plan-based task dispatching
• gstack: builder-first engineering system with safety guards, shipping pipelines, multi-specialist review, and the "Boil the Lake" philosophy of AI-assisted completeness

learnship adapts, combines, and extends these into a unified, multi-platform system with integrated learning. All are used as inspiration and learnship is original work built on their shoulders.

License

MIT © Favio Vazquez