agentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
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
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/agentic-engineering/SKILL.mdinside 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.
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