Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add vanja-emichi/a0_agent_skills --skill context-engineeringgit clone --depth 1 https://github.com/vanja-emichi/a0_agent_skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vanja-emichi/a0_agent_skills/context-engineering)<a href="https://agentmods.dev/skills/vanja-emichi/a0_agent_skills/context-engineering"><img src="https://agentmods.dev/badge/skills/vanja-emichi/a0_agent_skills/context-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vanja-emichi/a0_agent_skills/context-engineering"><img src="https://agentmods.dev/badge/skills/vanja-emichi/a0_agent_skills/context-engineering.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00052 | $0.02478 |
| Opus 5.5 | $0.00021 | $0.00991 |
| Sonnet 5.5 | $0.00010 | $0.00496 |
| Haiku 4.5 | $0.00005 | $0.00248 |
Grade A, and why
context-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 321 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Overview
Agent performance is determined by the quality of context provided, not just the quality of the model. Context engineering is the practice of structuring project information, task instructions, and session state so agents can do their best work. This applies whether you're writing a project guidance file, structuring a long conversation, or coordinating a multi-step development task.
When to Use
- Setting up a new project for agent-assisted development
- Agent is making mistakes that suggest missing context (wrong conventions, wrong tech stack)
- Writing or updating a project guidance file (CLAUDE.md, AGENTS.md, etc.)
- Planning a complex multi-step development session
- Agent keeps losing track of decisions made earlier in a session
The Context Hierarchy
Context sources (highest to lowest priority):
├── 1. Immediate message — what you just said
├── 2. Conversation history — this session's decisions
├── 3. Project guidance file — persistent project conventions
├── 4. Loaded skills — active behavioral protocols
└── 5. Training data — model's built-in knowledge
The first source wins when there's a conflict. Use this to override defaults: put critical constraints in the immediate message or project guidance file.
The Project Guidance File
Every project that uses agent-assisted development should have a guidance file. In Agent Zero, this is typically read via text_editor:read at the start of a session. Common names: CLAUDE.md, AGENTS.md, .cursorrules, GEMINI.md.
Guidance File Template
# Project: [Name]
## What This Is
One paragraph: what the project does, who it's for, and what problem it solves.
## Tech Stack
- **Runtime**: Node.js 20 + TypeScript 5
- **Framework**: Next.js 15 App Router
- **Database**: PostgreSQL + Prisma
- **Testing**: Vitest + Testing Library + Playwright
- **Styling**: Tailwind CSS
- **Deployment**: Vercel
## Development Commands
```bash
npm run dev # Start dev server (localhost:3000)
npm test # Run unit tests
npm run test:e2e # Run Playwright E2E tests
npm run build # Production build
npm run type-check # TypeScript check
npm run lint # ESLint
Code Conventions
- Components: Functional components, colocated tests in
ComponentName.test.tsx - API routes: In
app/api/, useroute.tshandlers - Imports: Use
@/alias for src root - Error handling: Throw domain errors (
NotFoundError,ValidationError), catch in route handlers - State: React Query for server state,
useStatefor local UI state
Project Structure
app/
(auth)/ # Auth routes
api/ # API route handlers
tasks/ # Task feature routes
src/
components/ # Shared UI components
lib/ # Utilities, DB client, auth helpers
types/ # Shared TypeScript types
prisma/
schema.prisma # Database schema
Key Decisions
- Use Prisma (not raw SQL) for all DB access. For complex queries:
prisma.$queryRaw. - All API validation uses Zod schemas defined in
src/lib/validation/ - Authentication via NextAuth.js with JWT sessions
- Feature flags managed via
src/lib/flags.ts
What Not to Do
- Don't use
anyin TypeScript (useunknown+ type guards) - Don't bypass Zod validation at API boundaries
- Don't commit
.envfiles (use.env.exampleas template) - Don't use
useEffectfor data fetching (use React Query)
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 321 lines · 52 tokens per session scan A 532f166991a2
context-engineering is a skill published in the GitHub repository vanja-emichi/a0_agent_skills (5 stars, last pushed 2mo ago), licensed MIT. It adds 52 tokens to every session and 2,478 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
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