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/vpeetla-ai/react-agent-pattern/agentic-engineeringnpx skills add vpeetla-ai/react-agent-pattern --skill agentic-engineeringgit clone --depth 1 https://github.com/vpeetla-ai/react-agent-patternWhat 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 | $0.00046 | $0.00323 |
| Opus 5 | $0.00023 | $0.00161 |
| Sonnet 5 | $0.00009 | $0.00065 |
| Haiku 4.5 | $0.00005 | $0.00032 |
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 yesterday.
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.
This is a copy
100% identical to agentic-engineering — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
What it actually says
Agentic Engineering
Not vibe coding — preserve professional quality while moving faster.
Before writing code
- State assumptions and success criteria (tests, API response, UI behavior)
- Identify smallest change that satisfies the request
- Read surrounding files — match naming, types, error handling
While coding
- Simplicity first — no speculative abstractions or "while I'm here" refactors
- Surgical diffs — touch only files required for the task
- Match conventions — see target repo's pyproject.toml, eslint, existing patterns
Before claiming done
- Run relevant tests (
pytest -q,npm test, typecheck) - For agent graphs: verify routing + at least one happy-path trace
- Summarize what changed and what was not changed (scope boundary)
Red flags — stop and ask
- Request touches governance + orchestration in one PR (split layers)
- Side effect without gateway/HITL (publish, push, notify)
- No test strategy for new behavior
Reference
Karpathy: From Vibe Coding to Agentic Engineering
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.
- yesterday First seen · 40 lines · 46 tokens per session scan A eacbd34bf3d7
agentic-engineering is a skill published in the GitHub repository vpeetla-ai/react-agent-pattern (2 stars, last pushed 1mo ago), licensed MIT. It adds 46 tokens to every session and 323 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to agentic-engineering, differing in 0 lines, and is treated as a copy.
Other skills, from other repositories
deploy-vercel-render
Deploy vpeetla-ai demos: Vercel static/Next.js frontends, Render FastAPI backends, env vars, free tier gotchas. Use when shipping demos, fixing deploy failures, or adding render.yaml / vercel.json.
langgraph-orchestration
Build or modify LangGraph StateGraph agents in vpeetla-ai repos: typed state, nodes, conditional edges, MemorySaver, interruptbefore HITL. Use when adding orchestrators, coding loops, or multi-agent graphs.
portfolio-adr
Write ADRs, case studies, and portfolio copy for ai-architecture-portfolio and venkat-ai-portfolio. Use when documenting decisions, updating ecosystem pages, or syncing GitHub profile README with live demos.
tdd-agent-loops
Test-driven development for agent systems: red-green-refactor on graphs, mocked LLM fixtures, pytest-asyncio, trace assertions. Use when adding agent nodes, fixing loop bugs, or building pattern repos.
aegis-gateway
Integrate AegisAI gateway before tool side effects (notify, publish, deploy). Use when adding Slack/Telegram/WhatsApp notify, content publish, or any irreversible external action in VAP, AegisLoop, or ai-content-factory.
hitl-side-effects
Add human-in-the-loop gates for irreversible actions: LangGraph interruptbefore, AegisAI approval queue, UI approve/resume endpoints. Use when shipping, publishing, notifying, or merging agent-generated changes.