agentic-engineering

A method for breaking work into small, clearly defined tasks that AI agents can carry out and pass between one another. It includes checks for judging results and explicit descriptions of each task's inputs and outputs.

In plain words
What is it for?
Use it to split large jobs before sending them to agents, design multi-agent workflows, investigate incomplete results, and set quality rules for agent-driven work.
Why use it?
It reduces failures caused by unclear instructions, hidden context, or poorly defined handoffs. It is written for the task-dispatch model used by Copilot CLI.

Skill for Claude CodeCodex

Install

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.

agentmods
npx agentmods add skills/drvoss/everything-copilot-cli/agentic-engineering
Any agent
npx skills add drvoss/everything-copilot-cli --skill agentic-engineering
Clone the repo
git clone --depth 1 https://github.com/drvoss/everything-copilot-cli

Made for: Claude Code, Codex.

Per session 40 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,134 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5 $0.00040 $0.02134
Opus 5 $0.00020 $0.01067
Sonnet 5 $0.00008 $0.00427
Haiku 4.5 $0.00004 $0.00213

Measured 2d ago against content hash 55aa67284a8d, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

agentic-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 2d 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.

skills/copilot-exclusive/agentic-engineering/SKILL.md · 231 lines

How it starts

The opening of the file, as written. The whole thing — 231 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Agentic Engineering

Design tasks so AI agents can execute them reliably. This is not about using Copilot features — it is about architecting work so agents succeed on the first attempt, fail loudly when they can't, and hand off cleanly to the next agent.

Why This is Copilot-Exclusive

The patterns here are specific to Copilot CLI's agent execution model: task() dispatch, read_agent / write_agent lifecycle, SQL session state, and background agents with mode: "background". They don't map directly to interactive coding sessions in other tools.

When to Use

  • Decomposing a large task before dispatching it to an agent or fleet
  • Designing a multi-agent workflow where context must transfer between agents
  • Debugging why an agent produced incorrect or incomplete output
  • Establishing quality standards for a new agentic workflow

When NOT to Use

Instead of agentic-engineering Use
You already have tasks and just need to plan them plan-mode-mastery
You need to assemble a specialist agent team team-planner
You need autonomous execution guardrails autopilot-patterns

Core Principles

1. The 15-Minute Task Unit

Rule: Each agent dispatch should complete in roughly 15 minutes of human-equivalent focused work. In practice: 1–3 files changed, 1 clear outcome, no more than one decision required.

Why: Agents fail when context exceeds what fits in a single focused pass. Long tasks require the agent to hold too much state, make too many decisions, and produce outputs that are hard to verify.

Signs a task is too large:

  • Description contains "and" more than twice
  • Requires reading more than 5 files to complete
  • Has more than one possible success state
  • Cannot be verified by a single test or check

Signs a task is too small:

  • It is just a file read or a lookup
  • A single edit call handles it entirely
  • No judgment is required

Decomposition pattern:

Large task: "Implement user authentication with JWT and refresh tokens"
↓ decompose
T-01: Add User schema + bcrypt password field (DB layer only)
T-02: Implement POST /auth/login endpoint (validate + sign JWT)
T-03: Implement POST /auth/refresh endpoint (validate refresh token)
T-04: Add auth middleware (extract + verify JWT on protected routes)
T-05: Integration tests for T-02, T-03, T-04

Read the full file on GitHub · 231 lines

Changes

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.

  1. 2d ago First seen · 231 lines · 40 tokens per session scan A 55aa67284a8d

Subscribe to this mod's changes

agentic-engineering is a skill published in the GitHub repository drvoss/everything-copilot-cli (45 stars, last pushed 5d ago), licensed MIT. It adds 40 tokens to every session and 2,134 once invoked, about $0.0002 per session on Opus 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 2026-08-30.

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