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/ilkruglov/developing-ai-agents-skill/developing-ai-agentsnpx skills add ilkruglov/developing-ai-agents-skill --skill developing-ai-agentsgit clone --depth 1 https://github.com/ilkruglov/developing-ai-agents-skillWrote 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/ilkruglov/developing-ai-agents-skill/developing-ai-agents)<a href="https://agentmods.dev/skills/ilkruglov/developing-ai-agents-skill/developing-ai-agents"><img src="https://agentmods.dev/badge/skills/ilkruglov/developing-ai-agents-skill/developing-ai-agents.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 | $0.00059 | $0.04986 |
| Opus 5 | $0.00030 | $0.02493 |
| Sonnet 5 | $0.00012 | $0.00997 |
| Haiku 4.5 | $0.00006 | $0.00499 |
Grade A, and why
developing-ai-agents 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 5d 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 — 202 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Разработка AI-агентов
Проектируй агента как проверяемую систему, а не как «промпт плюс мощная модель». Опирайся на русское издание книги Bojie Li «AI-агенты изнутри: принципы проектирования и инженерная практика» и на факты из текущего проекта.
Рабочий контракт
- Сначала изучи реальные требования, код, конфигурацию, трассы, метрики и ограничения. Не называй гипотезу причиной без доказательств.
- Разделяй:
- evidence — что подтверждено кодом, логом, тестом или измерением;
- inference — наиболее вероятное объяснение;
- unknown — что ещё нужно измерить.
- Начинай ответ с решения или диагноза. После него дай минимальную архитектуру, порядок реализации, проверки и риски.
- Для review или diagnosis оставайся read-only, пока пользователь явно не попросит изменить систему. Для реализации соблюдай инструкции репозитория и TDD.
- Книжные принципы считай устойчивыми инженерными эвристиками. Актуальность моделей, SDK, API, протоколов, цен, лимитов и поддержки провайдеров проверяй по первичным текущим источникам.
- По умолчанию укладывай архитектурный ответ в 1200 слов. Расширяй его только по явному запросу; если деталей больше, приоритизируй решения, contracts и проверки, а не полный checklist.
С чего начать: маршрут по задаче
| Запрос выглядит как | Начни с | Добери при необходимости |
|---|---|---|
| спроектировать агента с нуля | playbooks/design-agent.md | templates/agent-design.md, patterns.md |
| разобрать trace, найти причину сбоя | playbooks/diagnose-trace.md | templates/trace-diagnosis.md, antipatterns.md |
| review существующей агентной системы | playbooks/harness-review.md | templates/harness-spec.md |
| построить или починить evals | playbooks/build-evals.md | templates/eval-plan.md |
| память, RAG, самоулучшение | playbooks/memory-design.md | templates/memory-policy.md |
| голос, realtime, мультимодальность | playbooks/realtime-latency.md | chapters/ch09 |
| один агент или несколько | playbooks/multi-agent-choice.md | chapters/ch10 |
| контракт инструмента, права, песочница | templates/tool-contract.md | chapters/ch04 |
| симптом известен, причина нет | antipatterns.md | playbook по нужной области |
| что говорит книга по теме | source-map.md | нужный конспект главы |
What ships with it
44 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.
- agents/openai.yaml 325 B
- references/antipatterns.md 24 KB
- references/chapters/ch00-introduction.md 15 KB
- references/chapters/ch01-agent-foundations.md 22 KB
- references/chapters/ch02-context-engineering.md 20 KB
- references/chapters/ch03-memory-and-knowledge.md 16 KB
- references/chapters/ch04-tools.md 18 KB
- references/chapters/ch05-coding-agents.md 18 KB
- references/chapters/ch06-evaluation.md 17 KB
- references/chapters/ch07-post-training.md 15 KB
- references/chapters/ch08-self-evolution.md 16 KB
- references/chapters/ch09-realtime-multimodal.md 15 KB
- references/chapters/ch10-multi-agent.md 15 KB
- references/chapters/ch11-afterword.md 7.7 KB
- references/cheatsheet.md 9.9 KB
- references/glossary.md 12 KB
- references/patterns.md 33 KB
- references/playbooks/build-evals.md 8.6 KB
- references/playbooks/design-agent.md 10 KB
- references/playbooks/diagnose-trace.md 8.7 KB
- references/playbooks/harness-review.md 7.8 KB
- references/playbooks/memory-design.md 9.8 KB
- references/playbooks/multi-agent-choice.md 9.1 KB
- references/playbooks/realtime-latency.md 9.4 KB
- references/source-book/afterword.md 26 KB
- references/source-book/chapter1.md 148 KB
- references/source-book/chapter10.md 228 KB
- references/source-book/chapter2.md 287 KB
- references/source-book/chapter3.md 244 KB
- references/source-book/chapter4.md 219 KB
- references/source-book/chapter5.md 260 KB
- references/source-book/chapter6.md 239 KB
- references/source-book/chapter7.md 325 KB
- references/source-book/chapter8.md 160 KB
- references/source-book/chapter9.md 214 KB
- references/source-book/introduction.md 48 KB
- references/source-map.lock.json 32 KB
- references/source-map.md 10 KB
- references/templates/agent-design.md 10 KB
- references/templates/eval-plan.md 6.6 KB
- references/templates/harness-spec.md 6.8 KB
- references/templates/memory-policy.md 8.3 KB
- references/templates/tool-contract.md 7.4 KB
- references/templates/trace-diagnosis.md 8.1 KB
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.
- 5d ago First seen · 202 lines · 59 tokens per session scan A 09179b63662b
developing-ai-agents is a skill published in the GitHub repository ilkruglov/developing-ai-agents-skill (10 stars, last pushed 1mo ago), licensed Apache-2.0. It adds 59 tokens to every session and 4,986 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.
Other skills, from other repositories
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Define metrics from Kayba insights, implement them as Python measurement code, run against traces, and iterate until the metrics are clean and meaningful. Trigger when the user says "run stage 3", "define metrics", "build metrics", "compute baselines", or when invoked by the kayba-pipeline orchestrator. Requires…
kayba-stage-5-action-plan
Triage each insight into discard/code-fix/prompt-fix and produce a prioritized action plan with specific recommendations. Trigger when the user says "run stage 5", "make action plan", "triage skills", or when invoked by the kayba-pipeline orchestrator. Requires eval outputs from stages 1-4.
kayba-stage-4-rubric
Organize computed metrics into a tiered evaluation rubric with leading, lagging, and quality indicators. Trigger when the user says "run stage 4", "build rubric", "tier metrics", or when invoked by the kayba-pipeline orchestrator. Requires eval/baselinemetrics.json and eval/computebaselines.py to exist.
kayba-stage-6-hitl
Human-In-The-Loop gate that presents the action plan with full context, collects an informed approval/modification/rejection decision, and records the outcome. Trigger when the user says "run stage 6", "HITL review", "approve action plan", or when invoked by the kayba-pipeline orchestrator. Requires eval/actionplan.md…
kayba-stage-2-domain-context
Gather domain context about the repository and agent — system prompt, tool definitions, domain docs, and behavior patterns from traces. Trigger when the user says "run stage 2", "gather context", "domain context", or when invoked by the kayba-pipeline orchestrator.
kayba-stage-7-fixer
Implement the approved fixes from the action plan and log all changes. Trigger when the user says "run stage 7", "implement fixes", "apply action plan", or when invoked by the kayba-pipeline orchestrator. Requires eval/actionplan.md to exist.