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Hook Development for Claude Code Plugins · brycewang-stanford | DecimalAI

Agent Toolingby brycewang-stanford

Hook Development for Claude Code Plugins

This skill should be used when the user asks to "create a hook", "add a PreToolUse/PostToolUse/Stop hook", "validate tool use", "implement prompt-based hooks", "use ${CLAUDE_PLUGIN_ROOT}", "set up event-driven automation", "block dangerous commands", or mentions hook events (PreToolUse, PostToolUse, Stop, SubagentStop, SessionStart, SessionEnd, UserPromptSubmit, PreCompact, Notification). Provides comprehensive guidance for creating and implementing Claude Code plugin hooks with focus on advance

SkillScore

SkillScore95/100

Test results 95

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

64%95%of test cases passed +32 pts

Without skill14 of 22

With skill21 of 22

8 fixed13 passed either way0 still failing1 broke

Measured on gemini-3.6-flash8/8/2026v1

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskIf no explicit timeout field is provided in a hook definition, what are the default timeout durations in seconds for command hooks versus prompt hooks?

Without the skillMissed

With the skillPassed

Same model, same task. Answers are capped and redacted.

Use this skill

Your agent runs on

Adds every file the skill has, pinned so you can update it. No account needed.

pip install "decimalai>=0.15"
decimalai skills add brycewang-stanford-hook-development
claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

The same install from Python, for a notebook or a setup script.

Install

pip install "decimalai>=0.15"

Add the skill

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

print("Added. Start Claude Code with the command below.")

Start Claude Code with it

claude --plugin-dir "$(git rev-parse --show-toplevel 2>/dev/null || pwd)/.agents"

Type these in a Claude Code session. No account or API key. It writes the skill and any files it bundles, and asks first when its safety rating isn't Passed.

Add the DecimalAI plugin, once

/plugin marketplace add decimal-labs/decimalai-claude-plugins
/plugin install decimalai@decimalai

Install this skill

/decimalai:install brycewang-stanford-hook-development

The plugin needs python3 and curl on your machine.

No Python or account. Claude Code picks it up in its next session.

Save it into your project

curl -fsSL --create-dirs -o .claude/skills/brycewang-stanford-hook-development/SKILL.md https://app.decimal.ai/s/brycewang-stanford-hook-development/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/brycewang-stanford-hook-development/SKILL.md

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Claude Code docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

The same install from Python, for a notebook or a setup script. agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[claude-agent-sdk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from claude_agent_sdk import ClaudeAgentOptions
from decimalai.claude_agent_sdk import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

async def main():
    question = "Hello!"
    async with support.run(question) as run:  # one user message, one run
        async for message in run.query(options=ClaudeAgentOptions(max_turns=6)):
            print(message)

# In a notebook or async code: await main()
asyncio.run(main())

Prefer its own loader? ClaudeAgentOptions(plugins=[skills_plugin()], skills="all") reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from claude_agent_sdk import ClaudeAgentOptions, query

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
options = ClaudeAgentOptions(system_prompt=f"You are a helpful agent.\n\n{skill}", max_turns=6)

async def main():
    async for message in query(prompt="Hello!", options=options):
        print(message)

# In a notebook or async code: await main()
asyncio.run(main())

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Claude Agent SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai-agents]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

Install

pip install "decimalai[openai-agents]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

from agents import Agent, Runner
from decimalai.openai_agents import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    name=support.name,
    instructions=support.instructions,  # your prompt, then the skill list
    tools=[*support.tools],             # your tools, then load_skill
)
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

Using SandboxAgent? capabilities=[Shell(), decimalai.openai_agents.sandbox_skills()] points its own loader at the same folder.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = Agent(name="support", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await Runner.run(agent, "Hello!")
result = Runner.run_sync(agent, "Hello!")

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

OpenAI Agents docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[openai]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

Install

pip install "decimalai[openai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

import json

from openai import OpenAI
from decimalai.openai import agent_config

client = OpenAI()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your tools, as Chat Completions takes them, and what answers each.
TOOLS = [{"type": "function", "function": {
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters": {"type": "object", "properties": {"order_id": {"type": "string"}},
                   "required": ["order_id"]},
}}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    # run.system is your prompt, then the skill list.
    messages = [{"role": "system", "content": run.system}, run.message]
    while True:
        reply = client.chat.completions.create(
            model="gpt-5.4-mini",
            messages=messages,
            tools=[*TOOLS, *run.tools],  # your tools, then load_skill
        ).choices[0].message
        messages.append(reply)
        if not reply.tool_calls:
            break
        for call in reply.tool_calls:
            # load_skill is answered by the run; your own tools by FUNCTIONS.
            function = FUNCTIONS.get(call.function.name)
            messages.append(run.tool_result(call) or {
                "role": "tool", "tool_call_id": call.id,
                "content": function(**json.loads(call.function.arguments)) if function
                else f"There is no tool named {call.function.name!r}.",
            })
print(reply.content)

# The Responses API takes run.response_tools instead of run.tools.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
reply = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[
        {"role": "system", "content": f"You are a helpful agent.\n\n{skill}"},
        {"role": "user", "content": "Hello!"},
    ],
).choices[0].message

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

OpenAI SDK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[anthropic]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

Install

pip install "decimalai[anthropic]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

import anthropic
from decimalai.anthropic import agent_config

client = anthropic.Anthropic()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

question = "Hello!"
with support.run(question) as run:  # one user message, one run
    answer = client.beta.messages.tool_runner(
        model="claude-haiku-5-5",
        max_tokens=4096,
        system=run.system,     # your prompt, then the skill list
        tools=[*run.tools],    # your tools, then load_skill
        messages=[{"role": "user", "content": question}],
    ).until_done()

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
reply = client.messages.create(
    model="claude-haiku-5-5",
    max_tokens=4096,
    system=f"You are a helpful agent.\n\n{skill}",
    messages=[{"role": "user", "content": "Hello!"}],
)

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Anthropic docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[google-genai]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

Install

pip install "decimalai[google-genai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

from google import genai
from google.genai import types
from decimalai.google_genai import agent_config

client = genai.Client()
# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")


def lookup_order(order_id: str) -> str:
    return f"Order {order_id} shipped yesterday."


# Your functions, as declarations for the model, and what answers each.
TOOLS = [{"function_declarations": [{
    "name": "lookup_order",
    "description": "Look up the status of an order.",
    "parameters_json_schema": {"type": "object", "properties": {"order_id": {"type": "string"}},
                               "required": ["order_id"]},
}]}]
FUNCTIONS = {"lookup_order": lookup_order}

question = "Where is order A1001?"
with support.run(question) as run:  # one user message, one run
    config = types.GenerateContentConfig(
        system_instruction=run.system,     # your prompt, then the skill list
        tools=[*TOOLS, *run.tool_params],  # your functions, then load_skill
    )
    contents = [{"role": "user", "parts": [{"text": run.message}]}]
    while True:
        response = client.models.generate_content(
            model="gemini-3.5-flash", contents=contents, config=config,
        )
        if not response.function_calls:
            break
        contents.append(response.candidates[0].content)
        parts = []
        for call in response.function_calls:
            # load_skill is answered by the run; your own functions by FUNCTIONS.
            # A loop of your own rather than automatic function calling, which
            # raises KeyError when the model asks for a function you did not pass.
            function = FUNCTIONS.get(call.name)
            parts.append(run.tool_result(call) or {"function_response": {
                "name": call.name, "id": call.id,
                "response": {"result": function(**(call.args or {})) if function
                             else f"There is no function named {call.name!r}."},
            }})
        contents.append({"role": "user", "parts": parts})
print(response.text)

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from google import genai
from google.genai import types

client = genai.Client()
skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
response = client.models.generate_content(
    model="gemini-3.5-flash",
    contents="Hello!",
    config=types.GenerateContentConfig(system_instruction=f"You are a helpful agent.\n\n{skill}"),
)

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Gemini API docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

Install

pip install "decimalai[langchain]" langchain-openai

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

from langchain.agents import create_agent
from decimalai.langchain import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = create_agent(
    "openai:gpt-5.4-mini",
    tools=[],                         # your tools; the middleware adds load_skill
    middleware=[support.middleware],  # your prompt, the skill list and load_skill
)
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = create_agent("openai:gpt-5.4-mini", tools=[], system_prompt=f"You are a helpful agent.\n\n{skill}")
result = agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

LangChain docs

CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

The same install from Python, for a notebook or a setup script. CrewAI's own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.crewai
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                               # your own CrewAI LLM
    skills=decimalai.crewai.skills(),      # the installed folder
    tools=decimalai.crewai.skill_tools(),  # reads a skill's files, runs its scripts
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

No DecimalAI package. CrewAI's own loader reads the folder, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from crewai import Agent, Crew, Task

agent = Agent(
    role="Support", goal="Help the customer", backstory="A support agent.",
    llm=llm,                         # your own CrewAI LLM
    skills=[Path(".agents/skills")],
)
task = Task(description="Hello!", expected_output="an answer", agent=agent)
Crew(agents=[agent], tasks=[task]).kickoff()

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

CrewAI docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[adk]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

Install

pip install "decimalai[adk]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.adk import DecimalAIPlugin, agent_config
from google.adk.agents import LlmAgent
from google.adk.apps import App

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = LlmAgent(
    name="support",          # ADK needs a Python identifier
    model="gemini-3.5-flash",
    instruction=support.instruction,  # your prompt, then the skill list
    tools=[*support.tools],           # your tools, then load_skill
)
# DecimalAIPlugin makes each invocation of runners built from this App one trace.
app = App(name="support", root_agent=agent, plugins=[DecimalAIPlugin()])

Prefer ADK's own loader? SkillToolset(skills=decimalai.adk.skills(), ...) reads the same folder. Use one or the other: a skill delivered both ways reaches the prompt twice.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from google.adk.agents import LlmAgent

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = LlmAgent(
    name="support",
    model="gemini-3.5-flash",
    instruction=lambda context: f"You are a helpful agent.\n\n{skill}",
)

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Google ADK docs

agent_config() hands your agent the skill. With no DecimalAI key it reads your project's files and sends nothing. Needs SDK 0.27 or newer.

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

Install

pip install "decimalai[pydantic-ai]"

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

from pydantic_ai import Agent
from decimalai.pydantic_ai import agent_config

# With no DECIMAL_API_KEY this reads the skills your project lists, from disk.
support = agent_config("support", fallback="You are a helpful agent.")

agent = Agent(
    "openai:gpt-4o-mini",
    name=support.name,
    tools=[],                 # your tools; the capability adds load_skill
    capabilities=[support],   # your prompt, the skill list and load_skill
)
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

On the Pydantic AI harness, decimalai.pydantic_ai_harness.skills(project) uses its own loader instead.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = Agent("openai:gpt-4o-mini", instructions=f"You are a helpful agent.\n\n{skill}")
# In a notebook or async code: result = await agent.run("Hello!")
result = agent.run_sync("Hello!")

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Pydantic AI docs

Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

The same install from Python, for a notebook or a setup script. Deep Agents' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" deepagents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

import decimalai.deepagents
from deepagents import create_deep_agent

agent = create_deep_agent(model=model, **decimalai.deepagents.skills())
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

The default backend reads files but has no shell. To run a skill's scripts, pass skills(backend=LocalShellBackend(...)).

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = create_deep_agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent.invoke({"messages": [{"role": "user", "content": "Hello!"}]})

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Deep Agents docs

Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

The same install from Python, for a notebook or a setup script. Its own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" agent-framework

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

import asyncio

from agent_framework import Agent
from decimalai.agent_framework import skills

agent = Agent(client=client, instructions="You are a helpful agent.", context_providers=[skills()])
asyncio.run(agent.run("Hello!"))

It caches skills by default, so restart the process after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = Agent(client=client, instructions=f"You are a helpful agent.\n\n{skill}")
asyncio.run(agent.run("Hello!"))

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add brycewang-stanford-hook-development

Add it to your agent

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

The same install from Python, for a notebook or a setup script. Strands' own skill loader reads the installed folder. No DecimalAI account needed.

Install

pip install "decimalai>=0.16" strands-agents

Add it to your agent

from decimalai import skillpkg

# Writes .agents/skills and .decimal/skills.toml. Safe to run twice.
result = skillpkg.add("brycewang-stanford-hook-development")
if result.exit_code:
    raise SystemExit(result.problems)

from decimalai.strands import skills
from strands import Agent
from strands.vended_tools import file_editor, shell

agent = Agent(
    model=model,                # always pass one: the default is Amazon Bedrock
    system_prompt="You are a helpful agent.",
    tools=[file_editor, shell],
    plugins=[skills()],
)
agent("Hello!")

Strands loads an agent's skills once, so build the agent again after decimalai skills update.

No DecimalAI package. The whole skill goes in every prompt, and nothing pins or updates it.

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/brycewang-stanford-hook-development/SKILL.md").read_text()
agent = Agent(model=model, system_prompt=f"You are a helpful agent.\n\n{skill}")
agent("Hello!")

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Strands docs

Adds every file the skill has, pinned so you can update it. Cursor, Copilot, Cline and Warp read this folder by themselves.

pip install "decimalai>=0.15"
decimalai skills add brycewang-stanford-hook-development

No Python or account. Cursor, Copilot, Cline and Warp read this folder by themselves.

Save it into your project

curl -fsSL --create-dirs -o .agents/skills/brycewang-stanford-hook-development/SKILL.md https://app.decimal.ai/s/brycewang-stanford-hook-development/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/brycewang-stanford-hook-development/SKILL.md

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Cursor and other editors docs

Fetch the file and give its text to your model as instructions. No account or package needed.

Get the file

curl -fsSL https://app.decimal.ai/s/brycewang-stanford-hook-development/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/brycewang-stanford-hook-development.json

This gets SKILL.md only. The skill's 7 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v1, tested 8/8/2026
Source: brycewang-stanford/Auto-Empirical-Research-Skills ↗
License: NOASSERTION

Manage it in DecimalAI

Saved prompts and skills, pinned versions, and traces of this skill in your runs. DecimalAI does not run your agent.

Claude Code reads files from your project, so DecimalAI config does not apply there.

On Claude Agent SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI Agents: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On OpenAI SDK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Anthropic: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Gemini API: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On LangChain: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On CrewAI: set DECIMAL_API_KEY and add decimalai.init(crewai=True) to record runs and skill use. The skill version stays in your repo.

On Google ADK: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Pydantic AI: save the skill to an agent here, set DECIMAL_API_KEY, and the same code uses it with no command to run. Changes you save reach the next run with no redeploy.

On Deep Agents: set DECIMAL_API_KEY and add decimalai.init(langchain=True) to record runs and skill use. The skill version stays in your repo.

On Microsoft Agent Framework: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

On Strands: set DECIMAL_API_KEY and add decimalai.init(otel=True, agent_name="support") to record runs and skill use. The skill version stays in your repo.

Editors read files from your project, so DecimalAI config does not apply there.

Anywhere else: record a use with decimalai.log_skill_activation() inside a trace.

Use this skill

SKILL.md

Hook Development for Claude Code Plugins

Overview

Hooks are event-driven automation scripts that execute in response to Claude Code events. Use hooks to validate operations, enforce policies, add context, and integrate external tools into workflows.

Key capabilities:

Validate tool calls before execution (PreToolUse)
React to tool results (PostToolUse)
Enforce completion standards (Stop, SubagentStop)
Load project context (SessionStart)
Automate workflows across the development lifecycle

Hook Types

Prompt-Based Hooks (Recommended)

Use LLM-driven decision making for context-aware validation:

{
  "type": "prompt",
  "prompt": "Evaluate if this tool use is appropriate: $TOOL_INPUT",
  "timeout": 30
}

Supported events: Stop, SubagentStop, UserPromptSubmit, PreToolUse

Benefits:

Context-aware decisions based on natural language reasoning
Flexible evaluation logic without bash scripting
Better edge case handling
Easier to maintain and extend

Command Hooks

Execute bash commands for deterministic checks:

{
  "type": "command",
  "command": "bash ${CLAUDE_PLUGIN_ROOT}/scripts/validate.sh",
  "timeout": 60
}

Use for:

Fast deterministic validations
File system operations
External tool integrations
Performance-critical checks

Hook Configuration Formats

Plugin hooks.json Format

For plugin hooks in hooks/hooks.json, use wrapper format:

{
  "description": "Brief explanation of hooks (optional)",
  "hooks": {
    "PreToolUse": [...],
    "Stop": [...],
    "SessionStart": [...]
  }
}

Key points:

description field is optional
hooks field is required wrapper containing actual hook events
This is the plugin-specific format

Example:

{
  "description": "Validation hooks for code quality",
  "hooks": {
    "PreToolUse": [
      {
        "matcher": "Write",
        "hooks": [
          {
            "type": "command",
            "command": "${CLAUDE_PLUGIN_ROOT}/hooks/validate.sh"
          }
        ]
      }
    ]
  }
}

Settings Format (Direct)

For user settings in .claude/settings.json, use direct format:

{
  "PreToolUse": [...],
  "Stop": [...],
  "SessionStart": [...]
}

Key points:

No wrapper - events directly at top level
No description field
This is the settings format

Important: The examples below show the hook event structure that goes inside either format. For plugin hooks.json, wrap these in {"hooks": {...}}.

Hook Events

PreToolUse

Execute before any tool runs. Use to approve, deny, or modify tool calls.

Example (prompt-based):

{
  "PreToolUse": [
    {
      "matcher": "Write|Edit",
      "hooks": [
        {
          "type": "prompt",
          "prompt": "Validate file write safety. Check: system paths, credentials, path traversal, sensitive content. Return 'approve' or 'deny'."
        }
      ]
    }
  ]
}

Output for PreToolUse:

{
  "hookSpecificOutput": {
    "permissionDecision": "allow|deny|ask",
    "updatedInput": {"field": "modified_value"}
  },
  "systemMessage": "Explanation for Claude"
}

PostToolUse

Execute after tool completes. Use to react to results, provide feedback, or log.

Example:

{
  "PostToolUse": [
    {
      "matcher": "Edit",
      "hooks": [
        {
          "type": "prompt",
          "prompt": "Analyze edit result for potential issues: syntax errors, security vulnerabilities, breaking changes. Provide feedback."
        }
      ]
    }
  ]
}

Output behavior:

Exit 0: stdout shown in transcript
Exit 2: stderr fed back to Claude
systemMessage included in context

Stop

Execute when main agent considers stopping. Use to validate completeness.

Example:

{
  "Stop": [
    {
      "matcher": "*",
      "hooks": [
        {
          "type": "prompt",
          "prompt": "Verify task completion: tests run, build succeeded, questions answered. Return 'approve' to stop or 'block' with reason to continue."
        }
      ]
    }
  ]
}

Decision output:

{
  "decision": "approve|block",
  "reason": "Explanation",
  "systemMessage": "Additional context"
}

SubagentStop

Execute when subagent considers stopping. Use to ensure subagent completed its task.

Similar to Stop hook, but for subagents.

UserPromptSubmit

Execute when user submits a prompt. Use to add context, validate, or block prompts.

Example:

{
  "UserPromptSubmit": [
    {
      "matcher": "*",
      "hooks": [
        {
          "type": "prompt",
          "prompt": "Check if prompt requires security guidance. If discussing auth, permissions, or API security, return relevant warnings."
        }
      ]
    }
  ]
}

SessionStart

Execute when Claude Code session begins. Use to load context and set environment.

Example:

{
  "SessionStart": [
    {
      "matcher": "*",
      "hooks": [
        {
          "type": "command",
          "command": "bash ${CLAUDE_PLUGIN_ROOT}/scripts/load-context.sh"
        }
      ]
    }
  ]
}

Special capability: Persist environment variables using $CLAUDE_ENV_FILE:

echo "export PROJECT_TYPE=nodejs" >> "$CLAUDE_ENV_FILE"

See examples/load-context.sh for complete example.

SessionEnd

Execute when session ends. Use for cleanup, logging, and state preservation.

PreCompact

Execute before context compaction. Use to add critical information to preserve.

Notification

Execute when Claude sends notifications. Use to react to user notifications.

Hook Output Format

Standard Output (All Hooks)

{
  "continue": true,
  "suppressOutput": false,
  "systemMessage": "Message for Claude"
}

continue: If false, halt processing (default true)
suppressOutput: Hide output from transcript (default false)
systemMessage: Message shown to Claude

Exit Codes

0 - Success (stdout shown in transcript)
2 - Blocking error (stderr fed back to Claude)
Other - Non-blocking error

Hook Input Format

All hooks receive JSON via stdin with common fields:

{
  "session_id": "abc123",
  "transcript_path": "/path/to/transcript.txt",
  "cwd": "/current/working/dir",
  "permission_mode": "ask|allow",
  "hook_event_name": "PreToolUse"
}

Event-specific fields:

PreToolUse/PostToolUse: tool_name, tool_input, tool_result
UserPromptSubmit: user_prompt
Stop/SubagentStop: reason

Access fields in prompts using $TOOL_INPUT, $TOOL_RESULT, $USER_PROMPT, etc.

Environment Variables

Available in all command hooks:

$CLAUDE_PROJECT_DIR - Project root path
$CLAUDE_PLUGIN_ROOT - Plugin directory (use for portable paths)
$CLAUDE_ENV_FILE - SessionStart only: persist env vars here
$CLAUDE_CODE_REMOTE - Set if running in remote context

Always use ${CLAUDE_PLUGIN_ROOT} in hook commands for portability:

{
  "type": "command",
  "command": "bash ${CLAUDE_PLUGIN_ROOT}/scripts/validate.sh"
}

Plugin Hook Configuration

In plugins, define hooks in hooks/hooks.json:

{
  "PreToolUse": [
    {
      "matcher": "Write|Edit",
      "hooks": [
        {
          "type": "prompt",
          "prompt": "Validate file write safety"
        }
      ]
    }
  ],
  "Stop": [
    {
      "matcher": "*",
      "hooks": [
        {
          "type": "prompt",
          "prompt": "Verify task completion"
        }
      ]
    }
  ],
  "SessionStart": [
    {
      "matcher": "*",
      "hooks": [
        {
          "type": "command",
          "command": "bash ${CLAUDE_PLUGIN_ROOT}/scripts/load-context.sh",
          "timeout": 10
        }
      ]
    }
  ]
}

Plugin hooks merge with user's hooks and run in parallel.

Matchers

Tool Name Matching

Exact match:

"matcher": "Write"

Multiple tools:

"matcher": "Read|Write|Edit"

Wildcard (all tools):

"matcher": "*"

Regex patterns:

"matcher": "mcp__.*__delete.*"  // All MCP delete tools

Note: Matchers are case-sensitive.

Common Patterns

// All MCP tools
"matcher": "mcp__.*"

// Specific plugin's MCP tools
"matcher": "mcp__plugin_asana_.*"

// All file operations
"matcher": "Read|Write|Edit"

// Bash commands only
"matcher": "Bash"

Security Best Practices

Input Validation

Always validate inputs in command hooks:

#!/bin/bash
set -euo pipefail

input=$(cat)
tool_name=$(echo "$input" | jq -r '.tool_name')

# Validate tool name format
if [[ ! "$tool_name" =~ ^[a-zA-Z0-9_]+$ ]]; then
  echo '{"decision": "deny", "reason": "Invalid tool name"}' >&2
  exit 2
fi

Path Safety

Check for path traversal and sensitive files:

file_path=$(echo "$input" | jq -r '.tool_input.file_path')

# Deny path traversal
if [[ "$file_path" == *".."* ]]; then
  echo '{"decision": "deny", "reason": "Path traversal detected"}' >&2
  exit 2
fi

# Deny sensitive files
if [[ "$file_path" == *".env"* ]]; then
  echo '{"decision": "deny", "reason": "Sensitive file"}' >&2
  exit 2
fi

See examples/validate-write.sh and examples/validate-bash.sh for complete examples.

Quote All Variables

# GOOD: Quoted
echo "$file_path"
cd "$CLAUDE_PROJECT_DIR"

# BAD: Unquoted (injection risk)
echo $file_path
cd $CLAUDE_PROJECT_DIR

Set Appropriate Timeouts

{
  "type": "command",
  "command": "bash script.sh",
  "timeout": 10
}

Defaults: Command hooks (60s), Prompt hooks (30s)

Performance Considerations

Parallel Execution

All matching hooks run in parallel:

{
  "PreToolUse": [
    {
      "matcher": "Write",
      "hooks": [
        {"type": "command", "command": "check1.sh"},  // Parallel
        {"type": "command", "command": "check2.sh"},  // Parallel
        {"type": "prompt", "prompt": "Validate..."}   // Parallel
      ]
    }
  ]
}

Design implications:

Hooks don't see each other's output
Non-deterministic ordering
Design for independence

Optimization

Use command hooks for quick deterministic checks
Use prompt hooks for complex reasoning
Cache validation results in temp files
Minimize I/O in hot paths

Temporarily Active Hooks

Create hooks that activate conditionally by checking for a flag file or configuration:

Pattern: Flag file activation

#!/bin/bash
# Only active when flag file exists
FLAG_FILE="$CLAUDE_PROJECT_DIR/.enable-strict-validation"

if [ ! -f "$FLAG_FILE" ]; then
  # Flag not present, skip validation
  exit 0
fi

# Flag present, run validation
input=$(cat)
# ... validation logic ...

Pattern: Configuration-based activation

#!/bin/bash
# Check configuration for activation
CONFIG_FILE="$CLAUDE_PROJECT_DIR/.claude/plugin-config.json"

if [ -f "$CONFIG_FILE" ]; then
  enabled=$(jq -r '.strictMode // false' "$CONFIG_FILE")
  if [ "$enabled" != "true" ]; then
    exit 0  # Not enabled, skip
  fi
fi

# Enabled, run hook logic
input=$(cat)
# ... hook logic ...

Use cases:

Enable strict validation only when needed
Temporary debugging hooks
Project-specific hook behavior
Feature flags for hooks

Best practice: Document activation mechanism in plugin README so users know how to enable/disable temporary hooks.

Hook Lifecycle and Limitations

Hooks Load at Session Start

Important: Hooks are loaded when Claude Code session starts. Changes to hook configuration require restarting Claude Code.

Cannot hot-swap hooks:

Editing hooks/hooks.json won't affect current session
Adding new hook scripts won't be recognized
Changing hook commands/prompts won't update
Must restart Claude Code: exit and run claude again

To test hook changes:

Edit hook configuration or scripts
Exit Claude Code session
Restart: claude or cc
New hook configuration loads
Test hooks with claude --debug

Hook Validation at Startup

Hooks are validated when Claude Code starts:

Invalid JSON in hooks.json causes loading failure
Missing scripts cause warnings
Syntax errors reported in debug mode

Use /hooks command to review loaded hooks in current session.

Debugging Hooks

Enable Debug Mode

claude --debug

Look for hook registration, execution logs, input/output JSON, and timing information.

Test Hook Scripts

Test command hooks directly:

echo '{"tool_name": "Write", "tool_input": {"file_path": "/test"}}' | \
  bash ${CLAUDE_PLUGIN_ROOT}/scripts/validate.sh

echo "Exit code: $?"

Validate JSON Output

Ensure hooks output valid JSON:

output=$(./your-hook.sh < test-input.json)
echo "$output" | jq .

Quick Reference

Hook Events Summary

Event	When	Use For
PreToolUse	Before tool	Validation, modification
PostToolUse	After tool	Feedback, logging
UserPromptSubmit	User input	Context, validation
Stop	Agent stopping	Completeness check
SubagentStop	Subagent done	Task validation
SessionStart	Session begins	Context loading
SessionEnd	Session ends	Cleanup, logging
PreCompact	Before compact	Preserve context
Notification	User notified	Logging, reactions

Best Practices

DO:

✅ Use prompt-based hooks for complex logic
✅ Use ${CLAUDE_PLUGIN_ROOT} for portability
✅ Validate all inputs in command hooks
✅ Quote all bash variables
✅ Set appropriate timeouts
✅ Return structured JSON output
✅ Test hooks thoroughly

DON'T:

❌ Use hardcoded paths
❌ Trust user input without validation
❌ Create long-running hooks
❌ Rely on hook execution order
❌ Modify global state unpredictably
❌ Log sensitive information

Additional Resources

Reference Files

For detailed patterns and advanced techniques, consult:

references/patterns.md - Common hook patterns (8+ proven patterns)
references/migration.md - Migrating from basic to advanced hooks
references/advanced.md - Advanced use cases and techniques

Example Hook Scripts

Working examples in examples/:

validate-write.sh - File write validation example
validate-bash.sh - Bash command validation example
load-context.sh - SessionStart context loading example

Utility Scripts

Development tools in scripts/:

validate-hook-schema.sh - Validate hooks.json structure and syntax
test-hook.sh - Test hooks with sample input before deployment
hook-linter.sh - Check hook scripts for common issues and best practices

External Resources

Official Docs: https://docs.claude.com/en/docs/claude-code/hooks
Examples: See security-guidance plugin in marketplace
Testing: Use claude --debug for detailed logs
Validation: Use jq to validate hook JSON output

Implementation Workflow

To implement hooks in a plugin:

Identify events to hook into (PreToolUse, Stop, SessionStart, etc.)
Decide between prompt-based (flexible) or command (deterministic) hooks
Write hook configuration in hooks/hooks.json
For command hooks, create hook scripts
Use ${CLAUDE_PLUGIN_ROOT} for all file references
Validate configuration with scripts/validate-hook-schema.sh hooks/hooks.json
Test hooks with scripts/test-hook.sh before deployment
Test in Claude Code with claude --debug
Document hooks in plugin README

Focus on prompt-based hooks for most use cases. Reserve command hooks for performance-critical or deterministic checks.

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Imported

brycewang-stanford / Auto-Empirical-Research-Skills

skills/33-Galaxy-Dawn-claude-scholar/skills/hook-development/SKILL.md

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License NOASSERTIONImported 8/2/2026Version v1Bundled 7 files · scripts

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Hook Development for Claude Code Plugins

Overview

Hook Types

Prompt-Based Hooks (Recommended)

Command Hooks

Hook Configuration Formats

Plugin hooks.json Format

Settings Format (Direct)

Hook Events

PreToolUse

PostToolUse

Stop

SubagentStop

UserPromptSubmit

SessionStart

SessionEnd

PreCompact

Notification

Hook Output Format

Standard Output (All Hooks)

Exit Codes

Hook Input Format

Environment Variables

Plugin Hook Configuration

Matchers

Tool Name Matching

Common Patterns

Security Best Practices

Input Validation

Path Safety

Quote All Variables

Set Appropriate Timeouts

Performance Considerations

Parallel Execution

Optimization

Temporarily Active Hooks

Hook Lifecycle and Limitations

Hooks Load at Session Start

Hook Validation at Startup

Debugging Hooks

Enable Debug Mode

Test Hook Scripts

Validate JSON Output

Quick Reference

Hook Events Summary

Best Practices

Additional Resources

Reference Files

Example Hook Scripts

Utility Scripts

External Resources

Implementation Workflow

SkillSafety

Source

Other skills for the same job

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