Agentic Engineering
SkillAI & modelsYour AI can plan and run engineering tasks from start to finish with this skill. It breaks big work into smaller steps, checks results using evals, and picks models with cost in mind. Use it for engineering work your AI will carry out end to end.
Available today. Use it from your connected AI after setup.
No other account needed.
Add the skill, then give your AI an engineering task to plan or run. It will break the work down, check progress with evals, and keep model costs in mind as it goes.
Then ask your AI: use the Agentic Engineering skill
What your AI can do with it
- Plan engineering tasks before any work begins
- Break projects into smaller, doable steps
- Check results with evals before calling work done
- Choose models for each task based on cost
- Carry engineering work through from start to finish
What this skill tells your AI
The instructions your AI receives, as published by affaan-m/ecc in skills/agentic-engineering/SKILL.md and read by ahel’s review.
Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
Operating Principles
- Define completion criteria before execution.
- Decompose work into agent-sized units.
- Route model tiers by task complexity.
- Measure with evals and regression checks.
Eval-First Loop
- Define capability eval and regression eval.
- Run baseline and capture failure signatures.
- Execute implementation.
- 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.
Signals
- GitHub stars
- 256k
- Forks
- 38k
- Last commit
- Sep 2026
ahel recommends instead
Advanced
- Catalog kind
- skill
- Gateway key
agentic-engineering- Source
- github.com/affaan-m/ecc