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.
git clone --depth 1 https://github.com/terrene-foundation/kailash-coc-claude-pyWrote 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/commands/terrene-foundation/kailash-coc-claude-py/ai)<a href="https://agentmods.dev/commands/terrene-foundation/kailash-coc-claude-py/ai"><img src="https://agentmods.dev/badge/commands/terrene-foundation/kailash-coc-claude-py/ai/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/commands/terrene-foundation/kailash-coc-claude-py/ai"><img src="https://agentmods.dev/badge/commands/terrene-foundation/kailash-coc-claude-py/ai.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 | $0.00000 | $0.00553 |
| Opus 5 | $0.00000 | $0.00277 |
| Sonnet 5 | $0.00000 | $0.00111 |
| Haiku 4.5 | $0.00000 | $0.00055 |
Grade A, and why
ai 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.
Copies of this mod
1 near-identical copy found in the catalogue:
- ai — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
/ai - Kaizen Quick Reference
Purpose
Load the Kaizen skill for production-ready AI agent implementation with signature-based programming and multi-agent coordination.
Step 0: Verify Project Uses Kailash Kaizen
Before loading Kaizen patterns, check that this project uses Kailash Kaizen:
- Look for
kailash-kaizenorkaizeninrequirements.txt,pyproject.toml - Look for
from kaizen/import kaizenin source files
If not found, inform the user: "This project doesn't appear to use Kailash Kaizen. These patterns may not apply. Continue anyway?"
Quick Reference
| Command | Action |
|---|---|
/ai |
Load Kaizen patterns and agent basics |
/ai agent |
Show Agent API patterns |
/ai signature |
Show signature-based programming |
/ai multi |
Show multi-agent coordination |
What You Get
- Unified Agent API (v1.0.0)
- Signature-based programming
- Multi-agent coordination
- BaseAgent architecture
- Autonomous execution modes
Quick Pattern
import os
from kaizen.api import Agent
# 2-line quickstart — model from .env, NEVER hardcoded
agent = Agent(model=os.environ["KAIZEN_MODEL"])
result = await agent.run("What is IRP?")
# Autonomous mode with memory
agent = Agent(
model=os.environ["KAIZEN_MODEL"],
execution_mode="autonomous", # TAOD loop
memory="session",
tool_access="constrained",
)
Key Concepts
| Concept | Description |
|---|---|
| Signatures | Define input/output contracts |
| Execution Modes | supervised, autonomous, hybrid |
| BaseAgent | Inherit for custom agents |
| AgentRegistry | Scale to 100+ agents |
| TAOD Loop | Think, Act, Observe, Decide |
Agent Teams
When working with Kaizen, deploy:
- kaizen-specialist — Signatures, multi-agent coordination, BaseAgent architecture
- testing-specialist — Agent testing patterns (NO MOCKING)
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 · 80 lines · 0 tokens per session scan A c7833cf0c93e
ai is a command published in the GitHub repository terrene-foundation/kailash-coc-claude-py (12 stars, last pushed 24d ago), licensed Apache-2.0. It costs nothing until one of its globs matches a file; then it loads 553 tokens. 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 2026-09-03.
Other commands, from other repositories
prompt
System instructions for writing effective prompts. Apply when generating commands, skills, agents, or any LLM instructions.
prompt-show
Display full details of a saved prompt by ID.
music-suno-prompt
Grounded Suno prompt synthesis from local knowledge corpus + persona canon + label canon. No vibes-prompting.
audit-prompt
Evaluate an existing prompt for clarity, effectiveness, and edge cases.
develop-image-prompt.eval
Generates a detailed image generation prompt from a document or content description. Good output: a prompt that is specific, visual, non-abstract, includes style/composition/lighting guidance, and is calibrated to the specified dimensions and style options.
dare-llm-integration
Integração segura e eficiente com LLMs (Gemini, Claude, OpenAI, Ollama) em projetos DARE.