Unified mission engine + AI-agent skill system. Intercepts any project request (SaaS, app, API, CLI, agent) and decomposes into Goal → Mission → Task → Flow with quality gates and deliverable artifacts. Also installs and generates llms.txt / SKILL.md for 39+ AI agents with agent-native optimization from 32 system prompt profiles. Trigger on: project ideas, "je veux faire", "build me", "create a", SaaS, MVP, app, platform, skill setup, llms.txt, SKILL.md, agent docs, or any complex task needing structured decomposition before execution.
Resources
9Install
npx skillscat add creativityliberty/nexusskill Install via the SkillsCat registry.
NEXUS — Mission Engine + Agent Skill System
One system. Two modes. Every project, every agent.
USER: "Je veux faire un SaaS de X"
↓
╔═══════════════════════════════════════════╗
║ N E X U S ║
║ ║
║ MODE A: MISSION MODE B: SKILLS ║
║ ┌──────────────┐ ┌─────────────────┐ ║
║ │ INTERCEPT │ │ DETECT stack │ ║
║ │ DECOMPOSE │ │ FETCH docs │ ║
║ │ PLAN flows │ │ GENERATE skills │ ║
║ │ EXECUTE+gate │ │ OPTIMIZE /agent │ ║
║ │ DELIVER arts │ │ INSTALL 39 dirs │ ║
║ └──────┬───────┘ └────────┬────────┘ ║
║ └─────────┬───────────┘ ║
║ ▼ ║
║ ARTIFACTS + SKILLS ║
║ docs, code, specs, agent skills ║
╚═══════════════════════════════════════════╝Mode Selection
NEXUS auto-detects which mode to run:
| User Says | Mode | What Happens |
|---|---|---|
| "Build me a SaaS..." | MISSION | Full decomposition → execution → artifacts |
| "Install skills for my project" | SKILLS | Detect deps → fetch → install for agents |
| "Generate llms.txt for my API" | SKILLS | Analyze docs → generate llms.txt + SKILL.md |
| "Create a platform with agent tools" | BOTH | Mission plan + agent skills installed |
When in doubt, default to MISSION mode — it's more comprehensive and will
invoke SKILLS mode as a task within the mission when needed.
MODE A: MISSION ENGINE
A1. INTERCEPT — Capture Intent
Extract from user's message:
| Field | Extract | Default if Missing |
|---|---|---|
| Vision | What they want to build | — (must be stated) |
| Domain | Business/tech area | "Web application" |
| Users | Target audience | "General users" |
| Core Value | Key differentiator | First feature mentioned |
| Constraints | Stack, timeline, team | Next.js + Supabase, 6 weeks, solo dev |
Ask max 3 questions if critical info is missing. Otherwise, proceed with defaults.
A2. DECOMPOSE — Build Mission Hierarchy
GOAL (1)
└─ MISSION (1-3 phases)
└─ TASK (3-8 per mission)
└─ SUB-TASK (2-5 per task)
└─ FLOW (steps + gate)Goal definition:
goal:
id: G-001
title: "{Measurable goal}"
success_criteria: ["{Metric 1}", "{Metric 2}", "{Metric 3}"]
constraints:
timeline: "{N weeks}"
stack: "{Technologies}"
team: "{Team size}"Mission breakdown — each mission is a phase with a gate and artifacts:
missions:
- id: M-001
title: "Foundation & Architecture"
gate: "Architecture validated, scaffold running"
artifacts: [PRD, Architecture doc, Data model, Scaffold]
- id: M-002
title: "Core MVP"
gate: "Core features working end-to-end"
artifacts: [Auth, CRUD, UI, Database]
- id: M-003
title: "Polish & Launch"
gate: "Deployed, tested, documented"
artifacts: [Tests, Landing page, Deployment, Docs]Task decomposition — each task has sub-tasks and an artifact:
tasks:
- id: T-001
mission: M-001
title: "Define product requirements"
artifact: "docs/prd.md"
sub_tasks: ["User stories", "Data model", "User flows"]
delegate_to: "prd-builder" # if availableFlow patterns:
flows:
dev: [analyze, design, implement, test, review]
arch: [research, evaluate, decide, document, validate]
deploy: [build, test, stage, verify, deploy]Multi-flow — parallel execution for independent tasks:
multi_flow:
- parallel: [T-005, T-006, T-007] # Auth + CRUD + API in parallel
sync_gate: "All integrated and tested"A3. PLAN — Present Mission Briefing
Show the user the full plan before executing:
# Mission Briefing: {Goal}
## Phase 1: Foundation (Week 1-2)
Artifacts: 📄 PRD │ 📐 Architecture │ 📊 Data Model │ 🏗️ Scaffold
Tasks: T-001 → T-002 → T-003 → T-004
Gate: ✅ Architecture validated
## Phase 2: Core MVP (Week 3-4)
Artifacts: 🔐 Auth │ 📝 CRUD │ 🎨 UI │ 💾 Database
Tasks: T-005 ═╦═ T-006 ═╦═ T-007 → T-008
║ parallel ║
Gate: ✅ Core flow working end-to-end
## Phase 3: Launch (Week 5-6)
Artifacts: 💳 Payments │ 🧪 Tests │ 🌐 Landing │ 🚀 Deploy
Gate: ✅ Live and monitoredWait for user approval or modifications before executing.
A4. EXECUTE — Run With Quality Gates
Execute each task, producing artifacts. At each gate:
GATE: {Name}
├─ ✅ Artifacts produced?
├─ ✅ Quality criteria met?
├─ ✅ Dependencies satisfied?
└─ ✅ User validated?
→ PASS / PARTIAL / FAILExecution rules:
- Every task produces a tangible artifact (file, not just text)
- Delegate to specialized skills when available (see integration map below)
- Show progress after each task
- At Phase 1 completion → auto-trigger MODE B to install agent skills
A5. DELIVER — Artifacts Catalog
Standard output structure:
docs/prd.md, architecture.md, data-model.md, user-stories.md, adr/
src/ (project code)
tests/ (test suite)
deploy/ (Dockerfile, CI/CD)
.agents/skills/ (agent skills — from Mode B)Adaptive Complexity
| Complexity | Behavior |
|---|---|
| 1-3 (Simple) | Skip missions. Goal → 3-5 tasks → deliver |
| 4-6 (Medium) | 1-2 missions, sequential flow |
| 7-10 (Complex) | 3 missions, multi-flow, strict gates |
MODE B: AGENT SKILL SYSTEM
B1. DETECT — Project Stack + Agents
Scan for dependency manifests: package.json, requirements.txt, Cargo.toml,go.mod, Gemfile, composer.json, pubspec.yaml, build.gradle, *.csproj.
Scan for installed AI agents by checking home directories. Seereferences/agents-full-list.md for the complete 39-agent mapping.
B2. FETCH — Search & Download
Priority: llms.txt Hub registry → project /llms.txt → GitHub → generate from docs.
Use npx llmstxt-cli install <n> when available, or scripts/install_skills.sh.
B3. GENERATE — Produce llms.txt & SKILL.md
From project docs, generate:
llms.txt— concise index (llmstxt.org spec, seereferences/llmstxt-spec.md)llms-full.txt— complete docs (<500KB)SKILL.md— agent-ready with YAML frontmatter (description ≤1024 chars)
Use scripts/generate_llmstxt.py for automated generation.
B4. OPTIMIZE — Agent-Native Adaptation
Each agent reads skills differently. Adapt using profiles fromreferences/agent-native-profiles.md (derived from 32 system prompts):
| Agent | Style | Max Lines | Key Convention |
|---|---|---|---|
| Claude Code | Reasoning + examples | 500 | YAML frontmatter, explain WHY |
| Cursor | Dense imperative | 200 | Front-load patterns, code > prose |
| Windsurf | Workflow steps | 400 | Numbered steps, cascade pattern |
| Cline/Roo | Tool-use procedural | 400 | Tool call examples, decision trees |
| Copilot | Example-heavy | 300 | Code patterns, signatures |
| Universal | Clean markdown | 400 | No custom tags, portable |
Use scripts/optimize_for_agent.py --agent {name} for per-agent optimization.
B5. INSTALL — Multi-Agent Distribution
.agents/skills/{name}/SKILL.md ← canonical
.claude/skills/{name} → symlink
.cursor/skills/{name} → symlink
.windsurf/skills/{name} → symlink
... (39 agent directories)Update config files: CLAUDE.md, .cursorrules, .windsurfrules.
Skill Integration Map
NEXUS orchestrates all available skills:
| Need | Delegate To | Fallback |
|---|---|---|
| Full context | context-builder |
Inline |
| PRD | prd-builder |
context-builder |
| Architecture | agent-architecture-builder |
Inline |
| Multi-agent | numtema-universal-framework |
Inline |
| Workflows | numflow-llm |
Inline |
| Next.js scaffold | bun-nextjs-starter |
Manual |
| Agent skills | llmstxt-skill-installer |
Mode B inline |
| Deep reasoning | morphosys-cognitive-engine |
Standard |
| Prompts | vortex-meta-prompt-engineer |
Inline |
| Documents | docx, pptx, xlsx, pdf skills |
Markdown |
Scripts
| Script | Purpose |
|---|---|
scripts/install_skills.sh |
Standalone skill installer (no npm) |
scripts/generate_llmstxt.py |
Generate llms.txt / SKILL.md from docs |
scripts/optimize_for_agent.py |
Adapt SKILL.md per agent profile |
Key Principles
- Plan before code — Mission briefing comes first, always
- Every task → artifact — No task completes without a tangible output
- Gates prevent drift — Validate before advancing to next phase
- Agent-native skills — Optimize for each agent's internal conventions
- Delegate to specialists — Use other skills when they exist
- Adapt to complexity — Simple idea? Light plan. Complex SaaS? Full hierarchy.
- User controls everything — The plan is a proposal, not a mandate