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 kirill-sviridov/agent-dev-skills --skill prompt-engineeringgit clone --depth 1 https://github.com/kirill-sviridov/agent-dev-skillsWrote 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/kirill-sviridov/agent-dev-skills/prompt-engineering)<a href="https://agentmods.dev/skills/kirill-sviridov/agent-dev-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/kirill-sviridov/agent-dev-skills/prompt-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/kirill-sviridov/agent-dev-skills/prompt-engineering"><img src="https://agentmods.dev/badge/skills/kirill-sviridov/agent-dev-skills/prompt-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 | $0.00067 | $0.03920 |
| Opus 5 | $0.00034 | $0.01960 |
| Sonnet 5 | $0.00013 | $0.00784 |
| Haiku 4.5 | $0.00007 | $0.00392 |
Grade A, and why
prompt-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 10d 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 — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Prompt Engineering — a working reference
Sources: the DeepLearning.AI course "ChatGPT Prompt Engineering for Developers" (the base) + Anthropic's guides "Prompting best practices" / "Effective context engineering" + current OpenAI GPT-5.x prompting guides (5.5, April 2026: "re-baseline your prompts for the new model, don't drag along your old prescriptive stack") + Anthropic's essay "Building Effective Agents" + practice. Updated as I learn.
The core intuition: the model doesn't read minds (whatever isn't stated explicitly, it will either invent or NOT do) and the model only "thinks" in what it prints (there's no internal scratchpad — the reasoning tokens are the thinking).
Reference files alongside this one (read them as needed, not always): PATTERNS.md — reasoning models, reasoning patterns (ReAct, Reflexion, CoVe…), formatting (CAPS/markdown/emoji), curing a bloated prompt; EVAL-JUDGE.md — evals, the iterative loop, LLM-as-judge, DSPy; TOOLS.md — tooling (Console, Promptfoo, Langfuse…).
Principle 1 — clear, specific instructions ("clear" ≠ "short")
- Delimiters / XML tags. Any extraneous text (input data, examples, context documents) goes in delimiters:
```,""",< >,:::, XML tags (preferred for complex prompts). Why: (a) the model can tell where the instruction ends and the data begins; (b) protection against prompt injection — text inside a block is treated as data, not as a command. Use consistent, meaningful tag names (<instructions>,<context>,<input>,<example>); nest them for hierarchy (<documents><document index="1">…). - Numbered steps when order or completeness of steps matters.
- Structured output. If the result feeds code — specify the format explicitly. See "Modern standards → Structured Outputs."
- Condition checks. If an assumption might not hold, have the model check and say so ("if there are no steps — reply 'No steps found'") instead of inventing.
- Context helps. Explain who/what the result is for — the model calibrates tone, level of detail, and vocabulary.
- Don't leave the model freedom where you have requirements. Edge cases, what counts as what — spell them out.
What ships with it
3 files 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.
- 10d ago First seen · 159 lines · 67 tokens per session scan A b2650d1b08a7
prompt-engineering is a skill published in the GitHub repository kirill-sviridov/agent-dev-skills (2 stars, last pushed 2mo ago), licensed MIT. It adds 67 tokens to every session and 3,920 once invoked, about $0.0003 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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