Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/mturac/everything-openai-codex/agentic-engineeringnpx skills add mturac/everything-openai-codex --skill agentic-engineeringgit clone --depth 1 https://github.com/mturac/everything-openai-codexWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00040 | $0.00862 |
| Opus 5 | $0.00020 | $0.00431 |
| Sonnet 5 | $0.00008 | $0.00172 |
| Haiku 4.5 | $0.00004 | $0.00086 |
Grade A, and why
agentic-engineering scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
4 near-identical copies found in the catalogue:
- agentic-engineering — 100% identical, 2 lines differ
- agentic-engineering — 100% identical, 2 lines differ
- agentic-engineering — 100% identical, 2 lines differ
- agentic-engineering — 94% identical, 2 lines differ
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
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.
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
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 136 lines · 40 tokens per session scan A f8523715210a
agentic-engineering is a skill published in the GitHub repository mturac/everything-openai-codex (89 stars, last pushed 9d ago), licensed MIT. It adds 40 tokens to every session and 862 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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