agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

25 stars

Best use case

agentic-engineering is best used when you need a repeatable AI agent workflow instead of a one-off prompt.

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

Teams using agentic-engineering should expect a more consistent output, faster repeated execution, less prompt rewriting.

When to use this skill

You want a reusable workflow that can be run more than once with consistent structure.

When not to use this skill

You only need a quick one-off answer and do not need a reusable workflow.
You cannot install or maintain the underlying files, dependencies, or repository context.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/agentic-engineering/SKILL.md --create-dirs "https://raw.githubusercontent.com/ComeOnOliver/skillshub/main/skills/affaan-m/everything-claude-code/agentic-engineering/SKILL.md"

Manual Installation

Download SKILL.md from GitHub
Place it in .claude/skills/agentic-engineering/SKILL.md inside your project
Restart your AI agent — it will auto-discover the skill

How agentic-engineering Compares

Feature / Agent	agentic-engineering	Standard Approach
Platform Support	Not specified	Limited / Varies
Context Awareness	High	Baseline
Installation Complexity	Unknown	N/A

Frequently Asked Questions

What does this skill do?

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Agentic Engineering

Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.

## Operating Principles

1. Define completion criteria before execution.
2. Decompose work into agent-sized units.
3. Route model tiers by task complexity.
4. Measure with evals and regression checks.

## Eval-First Loop

1. Define capability eval and regression eval.
2. Run baseline and capture failure signatures.
3. Execute implementation.
4. Re-run evals and compare deltas.

## Task Decomposition

Apply the 15-minute unit rule:
- each unit should be independently verifiable
- each unit should have a single dominant risk
- each unit should expose a clear done condition

## Model Routing

- Haiku: classification, boilerplate transforms, narrow edits
- Sonnet: implementation and refactors
- Opus: architecture, root-cause analysis, multi-file invariants

## Session Strategy

- Continue session for closely-coupled units.
- Start fresh session after major phase transitions.
- Compact after milestone completion, not during active debugging.

## Review Focus for AI-Generated Code

Prioritize:
- invariants and edge cases
- error boundaries
- security and auth assumptions
- hidden coupling and rollout risk

Do not waste review cycles on style-only disagreements when automated format/lint already enforce style.

## Cost Discipline

Track per task:
- model
- token estimate
- retries
- wall-clock time
- success/failure

Escalate model tier only when lower tier fails with a clear reasoning gap.

Related Skills

engineering-features-for-machine-learning

from ComeOnOliver/skillshub

This skill empowers Claude to perform feature engineering tasks for machine learning. It creates, selects, and transforms features to improve model performance. Use this skill when the user requests feature creation, feature selection, feature transformation, or any request that involves improving the features used in a machine learning model. Trigger terms include "feature engineering", "feature selection", "feature transformation", "create features", "select features", "transform features", "improve model performance", and similar phrases related to feature manipulation.

feature-engineering-helper

from ComeOnOliver/skillshub

Feature Engineering Helper - Auto-activating skill for ML Training. Triggers on: feature engineering helper, feature engineering helper Part of the ML Training skill category.

conducting-chaos-engineering

from ComeOnOliver/skillshub

This skill enables Claude to design and execute chaos engineering experiments to test system resilience. It is used when the user requests help with failure injection, latency simulation, resource exhaustion testing, or resilience validation. The skill is triggered by discussions of chaos experiments (GameDays), failure injection strategies, resilience testing, and validation of recovery mechanisms like circuit breakers and retry logic. It leverages tools like Chaos Mesh, Gremlin, Toxiproxy, and AWS FIS to simulate real-world failures and assess system behavior.

ROS 2 Engineering Skills

from ComeOnOliver/skillshub

A progressive-disclosure skill for ROS 2 development — from first workspace to

using-dbt-for-analytics-engineering

from ComeOnOliver/skillshub

Builds and modifies dbt models, writes SQL transformations using ref() and source(), creates tests, and validates results with dbt show. Use when doing any dbt work - building or modifying models, debugging errors, exploring unfamiliar data sources, writing tests, or evaluating impact of changes.