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 skills add shennawardana23/skillme --skill agentic-engineeringgit clone --depth 1 https://github.com/shennawardana23/skillmeWrote 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/shennawardana23/skillme/agentic-engineering)<a href="https://agentmods.dev/skills/shennawardana23/skillme/agentic-engineering"><img src="https://agentmods.dev/badge/skills/shennawardana23/skillme/agentic-engineering/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/shennawardana23/skillme/agentic-engineering"><img src="https://agentmods.dev/badge/skills/shennawardana23/skillme/agentic-engineering.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00091 | $0.01446 |
| Opus 5 | $0.00046 | $0.00723 |
| Sonnet 5 | $0.00018 | $0.00289 |
| Haiku 4.5 | $0.00009 | $0.00145 |
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 8d 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.
How it starts
The opening of the file, as written. The whole thing — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Agentic Engineering
The engineering practice for a single unit of work when an AI agent does
most of the typing and a human sets the goal, the constraints, and the
acceptance bar. This is a per-task discipline — how you shape and check
one piece of work — not the team-wide process changes (see
ai-first-engineering for that).
Define done before you start
Write the completion criteria before the agent starts implementing, not after reviewing what it produced. If you can't state a concrete pass/fail condition, you don't yet know what "done" means well enough to hand the task off — decompose or clarify further first.
- A capability check: what new behavior must exist and how would you demonstrate it (a test, a manual repro, a script).
- A regression check: what existing behavior must not change.
- An explicit non-goal, if the task is easy to over-scope (e.g. "do not touch the payment retry logic in this pass").
Task decomposition: the 15-minute unit
Break work into units small enough that each one is independently verifiable. A good unit:
- has a single dominant risk — one thing that's actually hard about it, not three unrelated hard things bundled together
- has a clear done condition you can check without re-reading the whole diff
- can be verified on its own — you shouldn't need three other in-flight units to know whether this one is correct
If you can't articulate the unit's dominant risk in one sentence, it's still two units pretending to be one — split it.
Eval-first loop
- Define the capability eval (what should now work) and the regression eval (what must keep working) before implementation starts.
- Run both against the current code to get a baseline — capture what already fails and why, so you're not surprised by pre-existing gaps.
- Hand off the implementation.
- Re-run both evals and diff against the baseline. A capability eval that now passes plus a regression eval with no new failures is the actual completion signal — not the agent's own claim that it's done.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 138 lines · 91 tokens per session scan A 2fb8b88f1e7d
agentic-engineering is a skill published in the GitHub repository shennawardana23/skillme (2 stars, last pushed 11d ago), licensed Apache-2.0. It adds 91 tokens to every session and 1,446 once invoked, about $0.0005 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.
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