Best fit
- Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
affaan-m/ECC
agentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.
65Collecting
See how to use itView GitHub source
npx skills add https://github.com/affaan-m/ECC --skill "skills/agentic-engineering"Automated source guide
Source checked Jul 28, 2026·Refresh due Oct 26, 2026
Reorganized from the pinned upstream SKILL.md
Turn agentic-engineering's source instructions into a guide you can follow
According to the pinned SKILL.md from affaan-m/ECC: Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
npx skills add https://github.com/affaan-m/ECC --skill "skills/agentic-engineering"Bring this context
- A concrete task that matches the documented purpose of agentic-engineering.
- The files, examples, or context the task depends on.
- Your constraints, target environment, and definition of done.
Expected outputs
- A result that follows the pinned agentic-engineering instructions.
- A concise record of assumptions, inputs used, and unresolved questions.
- A final check against the source workflow and relevant permission signals.
Key source sections
Read agentic-engineering through these 5 source sections
Sections are extracted automatically from the pinned SKILL.md and link back to the source.
Review Focus for AI-Generated Code
Prioritize: - invariants and edge cases - error boundaries - security and auth assumptions - hidden coupling and rollout risk
invariants and edge caseserror boundariessecurity and auth assumptions
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.
Define completion criteria before execution.Decompose work into agent-sized units.Route model tiers by task complexity.
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.
Define capability eval and regression eval.Run baseline and capture failure signatures.Execute implementation.
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
each unit should be independently verifiableeach unit should have a single dominant riskeach unit should expose a clear done condition
Model Routing
Haiku: classification, boilerplate transforms, narrow edits
Haiku: classification, boilerplate transforms, narrow editsSonnet: implementation and refactorsOpus: architecture, root-cause analysis, multi-file invariants
SkillSignal prompt templates
Provide the task, context, and acceptance criteria
These prompts were written by SkillSignal from the source structure; they are not upstream text.
Task-start prompt
Confirm source fit, inputs, and outputs before acting.
Use agentic-engineering to help me with: [specific task]. Context: [files, data, or background]. Constraints: [environment, scope, and prohibited actions]. Before acting, check the pinned SKILL.md and explain which sections apply, what inputs are still missing, and what you will deliver.
Source-guided execution
Make the Agent explicitly follow the key extracted sections.
Apply the pinned agentic-engineering source to [task]. Pay particular attention to these source sections: “Review Focus for AI-Generated Code”, “Operating Principles”, “Eval-First Loop”, “Task Decomposition”, “Model Routing”. Preserve the important decision at each step. Mark facts not covered by the source as “needs confirmation” instead of inventing them. Then verify the result against my acceptance criteria: [criteria].
Result-review prompt
Check omissions, permissions, and source drift before delivery.
Review the current agentic-engineering result: (1) does it satisfy the original task; (2) were any applicable steps or limits in the pinned SKILL.md missed; (3) did it perform any unauthorized file, command, network, or data action; and (4) which conclusions remain unverified? List issues first, then fix only what the source or user authorization supports.
Output checklist
Verify each item before delivery
The task matches the purpose documented in the SKILL.md.
The source section “Review Focus for AI-Generated Code” has been checked.
The source section “Operating Principles” has been checked.
The source section “Eval-First Loop” has been checked.
The source section “Task Decomposition” has been checked.
Inputs, constraints, and acceptance criteria are explicit.
Unverified facts, compatibility, and outcome claims are clearly marked.
Any file, command, network, or data action has been reviewed.
Choose a different workflow
When another Skill is the better fit
agentic-engineering
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls.
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailagentic-engineering
評価ファースト実行、分解、コスト対応モデルルーティングを使用してエージェニックエンジニアとして動作します。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailagentic-engineering
作为代理工程师，采用评估优先执行、分解和成本感知模型路由进行操作。
A separate implementation from affaan-m/ECC; compare its source, maintenance signals, and permission requirements.
Open source detailFAQ
What does agentic-engineering do?
Use this skill for engineering workflows where AI agents perform most implementation work and humans enforce quality and risk controls.
How do I start using agentic-engineering?
The catalog detected this source-specific install command: npx skills add https://github.com/affaan-m/ECC --skill "skills/agentic-engineering". Inspect the command and pinned source before running it.
Which Agent platforms does it declare?
No dedicated Agent platform is declared in the pinned source record.
Repository stars
234,327
Repository forks
35,711
Quality
65/100
Source repository last pushed
Quality breakdown
Based on traceable docs and repository signals; stars are not treated as quality.
Documentation17/30
Specificity11/25
Maintenance20/20
Trust signals17/25
Compare before choosing
Related Agent Skills and source variants
These links are selected from shared tasks, functions, stacks, platforms, and same-name variants. Compare the source owner, documentation, permissions, and maintenance signals.
agentic-engineering by affaan-m
Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when AI agents perform most implementation work and humans enforce quality and risk controls.
agentic-engineering by affaan-m
評価ファースト実行、分解、コスト対応モデルルーティングを使用してエージェニックエンジニアとして動作します。
agentic-engineering by affaan-m
作为代理工程师，采用评估优先执行、分解和成本感知模型路由进行操作。
ab-testing by coreyhaines31
When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program
design-intelligence by event4u-app
Grounded design brief from the adopted corpus — style, WCAG-checked color tokens, typography, layout pattern, anti-patterns. Use on ui-design-brief or any which-style/palette/font/chart decision.
View original Skill.mdThis page is parsed directly from the repository SKILL.md without editorial rewriting. Collected: Jul 28, 2026 · about 1 min
Agentic Engineering
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.
- Source repo
- affaan-m/ECC
- Skill path
- skills/agentic-engineering/SKILL.md
- Commit SHA
- 4e973d3eaf92
- Repository license
- MIT
- Data collected