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 svngoku/coding-agents-skills --skill genai-tkgit clone --depth 1 https://github.com/svngoku/coding-agents-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/svngoku/coding-agents-skills/genai-tk)<a href="https://agentmods.dev/skills/svngoku/coding-agents-skills/genai-tk"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/genai-tk/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/svngoku/coding-agents-skills/genai-tk"><img src="https://agentmods.dev/badge/skills/svngoku/coding-agents-skills/genai-tk.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 | $0.00147 | $0.03181 |
| Opus 5.5 | $0.00059 | $0.01272 |
| Sonnet 5.5 | $0.00029 | $0.00636 |
| Haiku 4.5 | $0.00015 | $0.00318 |
Grade A, and why
genai-tk 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 — 228 lines — stays where its author put it; the contents beside it link to each section on GitHub.
genai-tk — GenAI & Agentic Toolkit
genai-tk is a Python 3.12+ toolkit where YAML config drives everything:
LLM providers, agent profiles, retrievers, workflows, and CLI commands are declared in config/*.yaml and
resolved at runtime through an Inversion-of-Control registry (ImportResolver). You extend it by adding YAML
entries that point at dotted Python paths, not by editing the library.
config/app_conf.yaml ─► global_config() ─► ImportResolver ─► factories / harness ─► LangChain objects
Verified against upstream commit
7fbd7a3(2026-10-12). The toolkit moves fast and several upstream docs lag the code (see "Upstream doc drift" below). When docs and code disagree, trust the code.
Quick Reference
| Topic | Reference |
|---|---|
Install, extras, cli init, full CLI command tree, CliTopCommand, cli skills, house conventions |
cli-and-init.md |
app_conf.yaml, :merge, :profiles, GENAITK_PROFILE, typed config access, IoC |
configuration.md |
get_llm / get_embeddings, model_id@provider, providers.yaml, reasoning effort, routing, decision models |
models.md |
| Agent profiles, harness layer, tools, middlewares, skills, sandbox, MCP, DeerFlow, CodeAct, trajectories | agents.md |
RetrieverFactory, ManagedRetriever, embeddings stores, chunkers, cli rag, RAG as a tool |
rag.md |
| YAML workflows, Prefect, ingest routing, markdownize converters | workflows.md |
BAML structured extraction: BamlStructuredProcessor, baml_invoke, cli baml |
baml-structured.md |
Core Workflow
1. Install and scaffold
genai-tk is not on PyPI. Install from git with uv, then scaffold:
uv init my-app && cd my-app
uv add "genai-tk @ git+https://github.com/tclatos/genai-tk@main"
uv run cli init --name "My Agent App" # scaffolds config/, skills/, justfile; runs uv sync
uv run cli init --extra harnessing --extra browser # also install DeepAgents/DeerFlow + Playwright
just run # uv run cli agent chat
What ships with it
14 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.
- eval.yaml 1019 B
- genai-tk-skill.skill 38 KB
- graders/check.py 19 KB runs code
- instructions/langchain-agent-profile.md 2.2 KB
- references/agents.md 19 KB
- references/baml-structured.md 12 KB
- references/cli-and-init.md 10 KB
- references/configuration.md 8.3 KB
- references/models.md 11 KB
- references/rag.md 9.9 KB
- references/workflows.md 8.8 KB
- rubrics/quality.md 2.2 KB
- solutions/reference-langchain-agent-profile/config/agents.yaml 937 B
- solutions/reference-langchain-agent-profile/use_agent.py 342 B runs code
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 · 228 lines · 147 tokens per session scan A 5ebe6c1ab3c2
genai-tk is a skill published in the GitHub repository svngoku/coding-agents-skills (12 stars, last pushed yesterday), licensed MIT. It adds 147 tokens to every session and 3,181 once invoked, about $0.0006 per session on Opus 5.5. 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 2026-10-14.
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