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/multi-agent-system-pattern/agentic-engineeringnpx skills add vpeetla-ai/multi-agent-system-pattern --skill agentic-engineeringgit clone --depth 1 https://github.com/vpeetla-ai/multi-agent-system-patternWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/vpeetla-ai/multi-agent-system-pattern/agentic-engineering)<a href="https://agentmods.dev/skills/vpeetla-ai/multi-agent-system-pattern/agentic-engineering"><img src="https://agentmods.dev/badge/skills/vpeetla-ai/multi-agent-system-pattern/agentic-engineering.svg" alt="Measured on agentmods" height="20"></a>What 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 4d 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
1 near-identical copy found in the catalogue:
- agentic-engineering — 100% identical, 0 lines differ
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.
- 4d ago First seen · 40 lines · 46 tokens per session scan A eacbd34bf3d7
agentic-engineering is a skill published in the GitHub repository vpeetla-ai/multi-agent-system-pattern (2 stars, last pushed today), 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. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other skills, from other repositories
spec-code-review
Structured code review for bugs, regressions, tests, and standards. Report-only by default; apply fixes only when the current user or upstream caller explicitly requests review-and-fix. mode:agent is always report-only.
spec-compound-refresh
Refresh docs/solutions learnings against the current codebase. Use when auditing stale, overlapping, superseded, or drifted learnings; avoid general refactor, debugging, or code review unless docs/solutions is explicit.
spec-brainstorm
Explore vague or ambitious ideas into a right-sized requirements-only unified plan. Use when the user wants to brainstorm, think through scope, decide what to build, or needs collaborative product framing before planning. Also use when the user must scope work in territory they say they do not know ("I know nothing…
spec-debug
Diagnosis loop for bugs and failing behavior. Use for errors, stack traces, regressions, failed tests, issue-tracker bugs, stuck investigations after failed fixes, or asks to debug/fix a bug.
spec-commit-push-pr
Internal landing helper for public workflows that already hold explicit commit and landing authorization; commits scoped changes, pushes, and creates or updates a PR with a value-first description.
spec-lfg
Run the full hands-off engineering pipeline from planning through a green PR. Use only when the current user explicitly requests spec-lfg or selects an option that clearly states it will commit, push, open a PR, and watch CI.