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Agent skill

Agentic Engineering

by affaan-m in affaan-m/ECC

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

MITAuto-check passedAI & LLM Engineering

Install Agentic Engineering

skills CLI
$ npx skills add affaan-m/ECC --skill agentic-engineering -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install affaan-m/ECC agentic-engineering --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/affaan-m/ECC.git skills-src && mkdir -p .claude/skills && cp -r skills-src/.kiro/skills/agentic-engineering .claude/skills/agentic-engineering && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
agentic-engineering
GitHub stars
275k
Token cost
~986 tokens
SKILL.md length
354 words
Files
1
Skills in repo
645
Repo updated
First seen
Licence
MIT

At a glance

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

  • Works in 4 steps: Define completion criteria before… → Decompose work into agent-sized units. → Route model tiers by task complexity. → …
  • AI agents perform most implementation work and humans enforce quality and risk controls
  • SKILL.md covers Operating Principles, Eval-First Loop, Task Decomposition and Model Routing, plus 5 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md

What it does

Agentic Engineering is an agent skill from affaan-m/ECC. 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.

Its SKILL.md is about 990 tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in AI & LLM Engineering, covering Model routing and gateways. The repository describes itself as: The agent harness performance optimization system. Skills, instincts, memory, security, and research-first development for Claude Code, Codex, Opencode, Cursor and beyond. The licence is MIT.

When your agent uses it

  • AI agents perform most implementation work and humans enforce quality and risk controls
  • Tasks that involve Model routing and gateways

Example prompts

  • “/agentic-engineering”

Workflow steps

4 steps, taken from the first numbered list in SKILL.md.

  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.

What it can do on your machine

Read from SKILL.md and the folder at commit ef648e0. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Agentic Engineering loads about 986 tokens when it runs. Until then it costs about 55 tokens; SKILL.md has 354 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~55
When it runs · the whole SKILL.md, loaded when a task matches
~986

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from affaan-m/ECC at commit ef648e0, republished under its MIT licence (© affaan-m). 354 words, ~986 tokens.

Download SKILL.mdSave it as .claude/skills/agentic-engineering/SKILL.md (or your agent's skills folder).
name
agentic-engineering
description
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.
metadata.origin
ECC

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.

Example workflow:

1. Write test that captures desired behavior (eval)
2. Run test → capture baseline failures
3. Implement feature
4. Re-run test → verify improvements
5. Check for regressions in other tests

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

Good decomposition:

Task: Add user authentication
├─ Unit 1: Add password hashing (15 min, security risk)
├─ Unit 2: Create login endpoint (15 min, API contract risk)
├─ Unit 3: Add session management (15 min, state risk)
└─ Unit 4: Protect routes with middleware (15 min, auth logic risk)

Bad decomposition:

Task: Add user authentication (2 hours, multiple risks)

Model Routing

Choose model tier based on task complexity:

  • Haiku: Classification, boilerplate transforms, narrow edits

    • Example: Rename variable, add type annotation, format code

  • Sonnet: Implementation and refactors

    • Example: Implement feature, refactor module, write tests

  • Opus: Architecture, root-cause analysis, multi-file invariants

    • Example: Design system, debug complex issue, review architecture

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

Session Strategy

  • Continue session for closely-coupled units

    • Example: Implementing related functions in same module

  • Start fresh session after major phase transitions

    • Example: Moving from implementation to testing

  • Compact after milestone completion, not during active debugging

    • Example: After feature complete, before starting next feature
Show full SKILL.md (138 more words)Show less

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.

Review checklist:

  • Edge cases handled (null, empty, boundary values)
  • Error handling comprehensive
  • Security assumptions validated
  • No hidden coupling between modules
  • Rollout risk assessed (breaking changes, migrations)

Cost Discipline

Track per task:

  • Model tier used
  • Token estimate
  • Retries needed
  • Wall-clock time
  • Success/failure outcome

Example tracking:

Task: Implement user login
Model: Sonnet
Tokens: ~5k input, ~2k output
Retries: 1 (initial implementation had auth bug)
Time: 8 minutes
Outcome: Success

When to Use This Skill

  • Managing AI-driven development workflows
  • Planning agent task decomposition
  • Optimizing model tier selection
  • Implementing eval-first development
  • Reviewing AI-generated code
  • Tracking development costs

Integration with Other Skills

  • tdd-workflow: Combine with eval-first loop for test-driven development
  • verification-loop: Use for continuous validation during implementation
  • search-first: Apply before implementation to find existing solutions
  • coding-standards: Reference during code review phase

© affaan-m, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in .kiro/skills/agentic-engineering of affaan-m/ECC.

Open the folder on GitHubat commit ef648e0

Compare with similar skills

Agentic Engineering next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Agentic Engineering compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Agentic Engineering this skillaffaan-m/ECC275k—~986Automated safety check: PassMIT
Shogun Bloom Configyohey-w/multi-agent-shogun1.4k—~3.1kAutomated safety check: PassMIT
Codemie Analyticscodemie-ai/codemie-code294—~7.5kAutomated safety check: PassApache-2.0
Model Routernidhi-singh02/agent-router110—~1.2kAutomated safety check: PassMIT
Codex Model Routing Teamzjp1997720/codex-model-routing-team158—~736Automated safety check: PassMIT
Add Modelget-convex/convex-evals129—~1.5kAutomated safety check: NotesApache-2.0

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Categories

Questions about Agentic Engineering

What does Agentic Engineering do?

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Agentic Engineering is an agent skill from affaan-m/ECC. Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing.

When should I use Agentic Engineering?

Agentic Engineering fits situations like: AI agents perform most implementation work and humans enforce quality and risk controls; tasks that involve Model routing and gateways.

How do I install Agentic Engineering in Claude Code?

Run `npx skills add affaan-m/ECC --skill agentic-engineering -a claude-code`. Or copy the skill folder (.kiro/skills/agentic-engineering in affaan-m/ECC) into .claude/skills/agentic-engineering in your project. Claude Code loads it when a task matches its description.

How do I install Agentic Engineering in Codex?

Run `npx skills add affaan-m/ECC --skill agentic-engineering -a codex`. Or copy the skill folder (.kiro/skills/agentic-engineering in affaan-m/ECC) into .agents/skills/agentic-engineering in your project. Codex loads it when a task matches its description.

Can I use Agentic Engineering in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add affaan-m/ECC --skill agentic-engineering -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/agentic-engineering, .gemini/skills/agentic-engineering, .github/skills/agentic-engineering and .opencode/skills/agentic-engineering in your project.

What does Agentic Engineering need to run?

SKILL.md names no scripts, command-line tools or credentials: Agentic Engineering is instructions for the agent only.

Does Agentic Engineering access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Agentic Engineering safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Agentic Engineering use?

Agentic Engineering is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Agentic Engineering use?

About 986 tokens (SKILL.md is roughly 3.9k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Agentic Engineering?

Skills that share tags, products or a category with Agentic Engineering: Shogun Bloom Config (yohey-w/multi-agent-shogun, 1.4k stars), Codemie Analytics (codemie-ai/codemie-code, 294 stars), Model Router (nidhi-singh02/agent-router, 110 stars) and Codex Model Routing Team (zjp1997720/codex-model-routing-team, 158 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Agentic Engineering?

affaan-m (a GitHub user) maintains it in affaan-m/ECC, which has 275,023 GitHub stars. The repository holds 645 skills in this directory. The repository was last updated on October 5, 2026.

Source: affaan-m/ECC on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.