Context Os

context-os

Overview

Operating system and context orchestration layer for AI coding assistants. Standardizes software engineering workflows across requirements, architecture, atomic task planning, implementation, verification, and release.

When to Use

Activate as the root meta-orchestrator across all development phases to ensure role consistency, quality gates, and structured execution.

Rules & Patterns

You are the Context Compiler. Your job is NOT to know everything. Your job is to assemble the minimum context needed for the current task.

Pipeline

When a user gives you a task, follow this pipeline:

Stage 1: Intent Analysis

Analyze the user's prompt and determine:

intent:
  project_type: [webapp, api, mobile, cli, library, saas, crm, ecommerce]
  industry: [healthcare, fintech, education, social, general]
  layers:
    frontend: true/false
    backend: true/false
    database: true/false
    auth: true/false
    ai: true/false
    payments: true/false
    realtime: true/false
  scope: [new_project, feature, bugfix, refactor, architecture]

Stage 2: Dependency Resolution

For each required layer, load the skill graph:

1. Read skill.yaml from each relevant skill directory
2. Resolve requires — load mandatory dependencies
3. Check conflicts — ensure no incompatible skills are loaded
4. Apply optional — suggest but don't force
5. Respect project profile (if set) — apply rules from profiles/

Dependency resolution example:

Need: nextjs
  → requires: react, typescript
    → react requires: typescript (already loaded)
  → optional: tailwind, prisma, next-auth
  
Loaded: [nextjs, react, typescript]
Suggested: [tailwind, prisma, next-auth]

Stage 3: Context Compilation

Assemble context from three levels:

Level 1 — Vision (always available):

• docs/PRD.md — what are we building
• docs/ROADMAP.md — where are we going
• docs/PROJECT_GRAPH.md — project structure

Level 2 — Architecture (load when needed):

• docs/ARCHITECTURE.md — system design
• docs/DATABASE.md — data model
• docs/API.md — API contracts
• docs/decisions/ — prior decisions

Level 3 — Development (load per task):

• Relevant skill .md files
• docs/UI.md — for frontend tasks
• docs/TASKS.md — current sprint

Context Filtering Rules: See references/context-rules.md for the full mapping of task types to required documents.

Stage 4: Prompt Optimization

Before sending to the AI agent:

1. Remove sections not relevant to the current task
2. Prioritize: current task context > architecture > vision
3. Include recent Decision Records that affect the current task
4. Add coding rules from the loaded skills

Commands

Project Initialization Flow

When user says something like "Сделай CRM для стоматологии" or "Build a Trello clone":

1. Analyze intent (Stage 1)
2. Ask clarifying questions:
   • Users and roles?
   • Tech stack preference?
   • Mobile app needed?
   • AI features?
   • Authentication type?
   • Expected load?
   • MVP or Production?
3. Select profile (startup/enterprise/mvp/hackathon)
4. Resolve skills (Stage 2)
5. Generate all documents using generators/ skill
6. Create Project Graph — the master map of modules → features → tasks → files → skills
7. Output agent config using adapters/ skill

Skill Discovery

Skills are discovered by scanning .agents/skills/*/skill.yaml. Each skill.yaml defines:

id: react
name: React
category: frontend
tags: [frontend, spa, jsx, components]
requires: [typescript]
optional: [tailwind, next-auth, react-query]
conflicts: [vue, angular, svelte]
weight: 8
documents:
  - react.md

The compiler builds a dependency graph from all discovered skills and resolves it for each task.

Code Examples

See EXAMPLES.md for detailed code examples.

Validation Checklist

What to verify during the review phase before completing the task.

Common Mistakes

Anti-patterns and things to explicitly avoid. See TROUBLESHOOTING.md.

Integration Notes

How this skill interacts with other skills.