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 mamahoos/dot-files --skill context-engineeringgit clone --depth 1 https://github.com/mamahoos/dot-filesWrote 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/mamahoos/dot-files/context-engineering)<a href="https://agentmods.dev/skills/mamahoos/dot-files/context-engineering"><img src="https://agentmods.dev/badge/skills/mamahoos/dot-files/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/mamahoos/dot-files/context-engineering"><img src="https://agentmods.dev/badge/skills/mamahoos/dot-files/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 | $0.00043 | $0.03226 |
| Opus 5.5 | $0.00017 | $0.01290 |
| Sonnet 5.5 | $0.00009 | $0.00645 |
| Haiku 4.5 | $0.00004 | $0.00323 |
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.
This is a copy
97% identical to context-engineering — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 355 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Context Engineering
Overview
Feed agents the right information at the right time. Context is the single biggest lever for agent output quality — too little and the agent hallucinates, too much and it loses focus. Context engineering is the practice of deliberately curating what the agent sees, when it sees it, and how it's structured.
When to Use
- Starting a new coding session
- Agent output quality is declining (wrong patterns, hallucinated APIs, ignoring conventions)
- Switching between different parts of a codebase
- Setting up a new project for AI-assisted development
- The agent is not following project conventions
The Context Hierarchy
Structure context from most persistent to most transient:
┌─────────────────────────────────────┐
│ 1. Rules Files (CLAUDE.md, etc.) │ ← Always loaded, project-wide
├─────────────────────────────────────┤
│ 2. Spec / Architecture Docs │ ← Loaded per feature/session
├─────────────────────────────────────┤
│ 3. Relevant Source Files │ ← Loaded per task
├─────────────────────────────────────┤
│ 4. Error Output / Test Results │ ← Loaded per iteration
├─────────────────────────────────────┤
│ 5. Conversation History │ ← Accumulates, compacts
└─────────────────────────────────────┘
Level 1: Rules Files
Create a rules file that persists across sessions. This is the highest-leverage context you can provide.
CLAUDE.md (for Claude Code):
# Project: [Name]
## Tech Stack
- React 18, TypeScript 5, Vite, Tailwind CSS 4
- Node.js 22, Express, PostgreSQL, Prisma
## Commands
- Build: `npm run build`
- Test: `npm test`
- Lint: `npm run lint --fix`
- Dev: `npm run dev`
- Type check: `npx tsc --noEmit`
## Code Conventions
- Functional components with hooks (no class components)
- Named exports (no default exports)
- colocate tests next to source: `Button.tsx` → `Button.test.tsx`
- Use `cn()` utility for conditional classNames
- Error boundaries at route level
## Boundaries
- Never commit .env files or secrets
- Never add dependencies without checking bundle size impact
- Ask before modifying database schema
- Always run tests before committing
## Patterns
[One short example of a well-written component in your style]
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 Changed · +16 lines 32b0d8caa89b
- 1mo ago Changed · +48 lines 213459e37fde
- 1mo ago First seen · 291 lines · 43 tokens per session scan A 0ca5b2c90af9
context-engineering is a skill published in the GitHub repository mamahoos/dot-files (5 stars, last pushed yesterday), licensed MIT. It adds 43 tokens to every session and 3,226 once invoked, about $0.0002 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 97% identical to context-engineering, differing in 1 line, and is treated as a copy.
Other skills, from other repositories
Context Optimization
Techniques to maximize context window efficiency, reduce latency, and prevent 'lost in middle' issues through strategic masking and compaction.
handoff
Compact the current conversation into a handoff document for another agent to pick up.
golang-style
Go conventions specific to this author. Use before writing or editing any Go (.go) file.
gh-cli
GitHub CLI (gh) conventions and the inline PR review recipe. Use when working with GitHub repositories, PRs, issues, actions, gh api, or any GitHub operations from the command line.
plan-loop
Plan work with the user in a PLAN.md, then implement it step by step, with /clear once the context fills up. Use before creating a plan, when the user mentions a PLAN.md, or when resuming work after /clear.
self-review
Deliberate end-to-end review pass over a completed change, applying the laziness ladder. Invoke when the user asks for a review, or when orchestrating one. Not a routine step after every edit.