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 kumaran-is/claude-code-onboarding --skill agentic-ai-coding-standardgit clone --depth 1 https://github.com/kumaran-is/claude-code-onboardingWrote 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/kumaran-is/claude-code-onboarding/agentic-ai-coding-standard)<a href="https://agentmods.dev/skills/kumaran-is/claude-code-onboarding/agentic-ai-coding-standard"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/agentic-ai-coding-standard/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/kumaran-is/claude-code-onboarding/agentic-ai-coding-standard"><img src="https://agentmods.dev/badge/skills/kumaran-is/claude-code-onboarding/agentic-ai-coding-standard.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 | $0.00053 | $0.01245 |
| Opus 5 | $0.00026 | $0.00622 |
| Sonnet 5 | $0.00011 | $0.00249 |
| Haiku 4.5 | $0.00005 | $0.00125 |
Grade A, and why
agentic-ai-coding-standard 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 6d 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 — 112 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Iron Law: Always consult the agentic-ai-dev skill and its MCP sources before writing agent code; never generate LangGraph/LangChain patterns from memory.
Agentic AI Coding Standards
Mandatory coding standards for all Python agentic AI services using LangChain, LangGraph, and FastAPI.
Key Rules
| # | Rule | Standard |
|---|---|---|
| 1 | State typing | Always TypedDict; never dict[str, Any] |
| 2 | Message lists | Annotated[list[BaseMessage], add_messages] |
| 3 | Loop protection | iteration_count in state + max check in routing function |
| 4 | Tool functions | @tool + docstring + try/except + return strings |
| 5 | LLM instantiation | Factory function; never inline ChatAnthropic() in nodes |
| 6 | Temperature | 0 for factual; 0.7 only for creative tasks |
| 7 | Checkpointing | PostgresSaver in production; MemorySaver only in tests |
| 8 | Error handling | Log + return error state; never swallow exceptions |
| 9 | Naming | build_<name>_agent(), <verb>_node(), <Name>State |
| 10 | Config | pydantic-settings with fail-fast; no os.getenv() with silent defaults |
| 11 | Type hints | mypy --strict; Literal for routing return types |
| 12 | Async | async def for all I/O; ainvoke/astream in API routes |
| 13 | Logging | structlog with agent_name, thread_id, node_name context |
| 14 | Secrets | Never log API keys; redact PII before logging |
| 15 | Testing | Basic invoke + tool usage + iteration limit + error recovery |
| 16 | Cost | Track tokens; configure budget caps; use cheapest viable model |
| 17 | Imports | Group: stdlib → third-party → langchain/langgraph → local |
Import Ordering
# 1. Standard library
from __future__ import annotations
import json
from typing import Annotated, Literal
# 2. Third-party
from fastapi import APIRouter, Depends
from pydantic import BaseModel, Field
# 3. LangChain / LangGraph
from langchain_core.messages import AIMessage, BaseMessage, HumanMessage
from langchain_core.tools import tool
from langgraph.graph import END, StateGraph
from langgraph.graph.message import add_messages
# 4. Local
from ..core.config import settings
from ..core.logging import get_logger
What ships with it
1 file 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.
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.
- 6d ago First seen · 112 lines · 53 tokens per session scan A ab8bbde044fe
agentic-ai-coding-standard is a skill published in the GitHub repository kumaran-is/claude-code-onboarding (35 stars, last pushed 2mo ago), licensed MIT. It adds 53 tokens to every session and 1,245 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 2026-09-03.
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