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 XCode-NLP/KodaSkills --skill koda-zensicalgit clone --depth 1 https://github.com/XCode-NLP/KodaSkillsWrote 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/xcode-nlp/kodaskills/koda-zensical)<a href="https://agentmods.dev/skills/xcode-nlp/kodaskills/koda-zensical"><img src="https://agentmods.dev/badge/skills/xcode-nlp/kodaskills/koda-zensical.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 5.1 | $0.00049 | $0.03946 |
| Opus 5 | $0.00024 | $0.01973 |
| Sonnet 5 | $0.00010 | $0.00789 |
| Haiku 4.5 | $0.00005 | $0.00395 |
Grade A, and why
koda-zensical 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 — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Документирование Zensical
Принципы написания документации
Язык и стиль
- Пиши простым и понятным языком
- Избегай просторечий и сложных технических терминов без объяснения
- Используй активный залог
- Обращайся к пользователю документации на "вы"
- Поддерживай единый стиль во всех документах
- Не злоупотребляй emoji
Примеры
- Приводи примеры конфигурации
- Показывай скриншоты для важных шагов
- Добавляй таблицы для сравнения опций
Исправление и избегание ошибок
- Синтаксические и грамматические ошибки должны исправляться в соответствии с правиламии и нормами естественного языка
- При изменении структуры документации:
- в новый документ следует добавить ссылки на другие релевантные документы или якоря
- ссылки на перемещённый или удалённый документ/якорь следует обновить в каждом существующем документе
- следует проверять результаты сборки на наличие ошибок и предупреждений компилятора
Синтаксис файлов
В основе документации лежит расширенный markdown.
Ниже описаны правила оформления и синтаксиса, которые отличаются от стандартного markdown и github-flavoured markdown.
Расширения синтаксиса предоставляются связкой:
zensical(документация: https://zensical.org/docs/)pymdown-extensions(документация: https://facelessuser.github.io/pymdown-extensions)
Ниже только необходимые и достаточные правила для:
- качественной документации;
- хорошего человеческого восприятия;
- корректного формирования документации без ошибок.
Применение этих подходов необязательно, но требования к каждому требуется соблюдать строго.
Если приведены ссылки на документацию, можешь использовать их для получения актуальной информации. Там так же могут быть описаные дополнительные приёмы для работы с документами.
Frontmatter
Каждый документ должен начинаться с этого блока метаданных.
После frontmatter должна быть пустая строка.
Внутри должен быть валидный yaml. Часто используются следующие опциональные параметры (* — желательны):
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 · 405 lines · 49 tokens per session scan A e41378f65600
koda-zensical is a skill published in the GitHub repository XCode-NLP/KodaSkills (19 stars, last pushed 1mo ago), licensed MIT. It adds 49 tokens to every session and 3,946 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 2026-08-30.
Other skills, from other repositories
claude-md-improver
Audit and improve CLAUDE.md files in repositories. Use when user asks to check, audit, update, improve, or fix CLAUDE.md files. Scans for all CLAUDE.md files, evaluates quality against templates, outputs quality report, then makes targeted updates. Also use when the user mentions "CLAUDE.md maintenance" or "project…
agent-platform-rag-engine-management
Manage and query Agent Platform RAG Engine Corpora and retrieve grounded contexts using the Google GenAI SDK. Use when listing RAG corpora or files, inspecting a corpus, retrieving contexts, or generating content grounded in a RAG corpus. Do not use for standard database queries (use SQL/Spanner skills), Google…
gke-workload-security
Audits, configures, and hardens workload-level security controls for Google Kubernetes Engine (GKE) applications and namespaces. Covers running cluster security audits (auditcluster.sh), configuring Workload Identity Federation (impersonation, KSA/GSA binding, and pod setup), enforcing Network Policies (default-deny…
gke-reliability
Improves GKE workload reliability, using PDBs, health probes, and topology spread constraints. Use when configuring GKE workload reliability, setting up PDBs, or configuring GKE health probes (liveness, readiness, startup). Don't use for disaster recovery setup or full cluster backups (use gke-backup-dr instead).
agent-platform-model-registry
Agent Platform Model Registry Management. Use when you need to upload, list, describe, update, or delete machine learning models (and their versions) in the Agent Platform Model Registry. Don't use for model training, model deployment to endpoints, or managing non-Agent Platform models.
google-cloud-solution-agentic-analytics-spark-knowledge-catalog
Discovers requirements and generates guidance to design and deploy a governed, secure agentic-analytics solution for data that's distributed across Google Cloud, other cloud providers, or on-premises. Data that's outside Google Cloud (such as data from Databricks, Snowflake, Salesforce, SAP, or Oracle systems) is…