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 commands/terrene-foundation/metis/aigit clone --depth 1 https://github.com/terrene-foundation/metisWrote 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/metis/ai)<a href="https://agentmods.dev/commands/terrene-foundation/metis/ai"><img src="https://agentmods.dev/badge/commands/terrene-foundation/metis/ai.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 | $0.00000 | $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 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.
This is a copy
100% identical to ai — 0 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
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.
- 5d ago First seen · 80 lines · 0 tokens per session scan A c7833cf0c93e
ai is a command published in the GitHub repository terrene-foundation/metis (2 stars, last pushed 4mo 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. It is 100% identical to ai, differing in 0 lines, and is treated as a copy.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.