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Postman Bundle · pipelex | DecimalAI

Testingby pipelex

Postman Bundle

Turn a Pipelex MTHDS bundle into an API request against any bundle-shaped pipelex-api route — push it as a ready-to-run query into the live "Pipelex FastAPI" Postman collection, emit a curl command, execute it directly, or just preview the body. Covers the whole bundle surface: run (/v1/execute, /v1/start), dry-run validate (/v1/validate — no inference, no cost), crate resolution (/v1/resolve), and typed codegen (/v1/codegen — ts-zod, python-pydantic, python-structures). Use this whenever the us

Never made a case worseMaintained

SkillScore

SkillScore82/100

Test results 82

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

18%82%of test cases passed +64 pts

Without skill4 of 22

With skill18 of 22

14 fixed4 passed either way4 still failing0 broke

Model 11/3/2026v3

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskFrom which directory in the Pipelex repository should `make bundle-run` or script invocations be executed?

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 pipelex-postman-bundle
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("pipelex-postman-bundle")
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 pipelex-postman-bundle

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/pipelex-postman-bundle/SKILL.md https://app.decimal.ai/s/pipelex-postman-bundle/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/pipelex-postman-bundle/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/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/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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("pipelex-postman-bundle")
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/pipelex-postman-bundle/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/pipelex-postman-bundle/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 1 other file is 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 pipelex-postman-bundle

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/pipelex-postman-bundle/SKILL.md https://app.decimal.ai/s/pipelex-postman-bundle/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/pipelex-postman-bundle/SKILL.md

This gets SKILL.md only. The skill's 1 other file is 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/pipelex-postman-bundle/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/pipelex-postman-bundle.json

This gets SKILL.md only. The skill's 1 other file is not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v3, tested 11/3/2026
Source: pipelex/pipelex ↗
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

Postman Bundle

Create runnable API requests for one Pipelex MTHDS bundle and push them into the live Pipelex FastAPI collection (or emit curl, or run them directly), so the user can exercise any bundle-shaped endpoint against a Pipelex API server. The query resolves the bundle the same way the CLI's pipelex run bundle <path> does.

This is a focused, one-bundle-at-a-time tool. It is not the full collection sync — for diffing the whole API surface against the code, that's the separate /update-postman command.

Endpoint cheat sheet

`--endpoint`	Route	Body beside `files`	Needs a pipe?	Needs `inputs`?	Inference?
`execute`	`POST /v1/execute`	`pipe_code`, `inputs`	yes	yes	YES — costs money
`start`	`POST /v1/start`	`pipe_code`, `inputs`, `callback_urls`	yes	yes	YES — costs money
`validate`	`POST /v1/validate`	`allow_signatures`, optional `render`	no	no	no (free dry-run)
`resolve`	`POST /v1/resolve`	nothing	no	no	no (free)
`codegen`	`POST /v1/codegen`	`kind`, `target`	no	no	no (free)

Every non-run endpoint answers with the /validate verdict discipline: a produced verdict is always a 200 discriminated on is_valid — the artifact on the valid arm, structured errors[] problem items on the invalid arm. Non-2xx is reserved for no-verdict conditions (request-shape 422, auth 401, server 5xx), rendered as RFC 9457 problem+json with a registry code.

What it produces in Postman

For the bundle the user names, it upserts requests into the collection under:

Bundles/
  <bundle>/            (named by the bundle's `domain`, or the filename)
    Execute (sync)             Start (async)             Validate (dry-run)
    Resolve (crate)            Codegen (types → <target>)

Which requests appear depends on --endpoint: the default both is execute + start; every other endpoint is pushed alone. Pushes merge by request name — pushing --endpoint codegen doesn't wipe an Execute/Start pair pushed earlier, and two codegen targets (distinct names) coexist. Requests use the collection variable {{base_url}} and inherit the collection's {{auth_token}} bearer auth (and the Start request's callback_urls is the {{callback_url}} variable), so the same query works against a local or hosted server by switching the Postman environment.

(The top folder used to be Run Bundle/ — if a stale folder by that name lingers in the collection, it can be deleted.)

The request body: the method as `files`

Every route carries the method the same way: files, the MTHDS standard's map of file entries. Each key is a file's tree path, its path from the method tree's root, and each value is the file's UTF-8 text, or {"base64": "..."} for a file whose bytes are not UTF-8, such as an image or an office template:

{
  "pipe_code": "greeting.greet",
  "inputs": { "...": "inputs.json copied verbatim" },
  "files": {
    "bundle.mthds": "domain = \"greeting\"\nmain_pipe = \"greet\"\n...",
    "templates/card.html": "<p>Hello, {{ text }}</p>\n",
    "assets/logo.png": { "base64": "iVBORw0KGgo..." }
  }
}

pipe_code and inputs ride execute/start only, allow_signatures and the optional render ride validate, and kind/target ride codegen; resolve sends files alone. The start body additionally carries callback_urls (see Callback URLs). files comes last in every body, so the fields you edit in Postman stay on top.

What travels is every file of the method's tree, listed by the mthds package's own lister, so it is judged by the same membership rule the runner and every MTHDS client apply (the standard's method-tree.md):

The root is the nearest directory at or above the bundle holding METHODS.toml, else the directory you point at. A .mthds file named alone with no METHODS.toml above it travels as the one-file tree of that file, nothing beside it.
What stays out: every path segment starting with a dot (.git/, .venv/, .env, .mthds/), __pycache__ and node_modules, virtual environments, nested repositories and nested packages, the tree's own .gitignored files and the manifest's [package] exclude patterns.
A run's inputs file never travels as a file. The standard keeps every file named inputs.json or inputs.toml, at any depth, out of every tree, and a runner applying that rule refuses such a key. The mthds lister pinned here still lists it, so the script drops it from files itself: the sibling inputs.json travels as inputs on execute/start and nowhere else.
Never by hash. The standard's third entry form, {"sha256", "size"}, names bytes the runner already holds in its blob store, which only an upload (POST /v1/blobs/missing, then PUT /v1/blobs/{sha256}) puts there. A Postman query or a curl command never uploads, so every file travels inline.
No retired fields. The skill never sends mthds_contents, mthds_sources, bundle_b64 or resources.

The listing stops at the runner's own limits on a tree a request sends (the MAX_METHOD_TREE_* limits of pipelex_api.limits), so pointing the skill at a large directory by mistake fails in moments. The script also refuses a main bundle the membership rule keeps out of the tree, such as a .gitignored one, since the pipe_code read from it would name a pipe the runner never receives.

The pipe_code the script infers is the main bundle's main_pipe qualified by that bundle's domain (greeting.greet, not greet): the whole tree travels, so a sibling bundle declaring a pipe of the same code would make a bare code ambiguous, which the runner refuses. An explicit --pipe is sent exactly as given.

Validate = the API's dry-run

POST /v1/validate is the API's dry-run: it parses, loads, and dry-runs every pipe with mock inputs and zero inference (no LLM calls, no cost), then returns the validated bundle blueprint, a graph_spec, and per-pipe input/output JSON-Schema pipe_io_contracts. It confirms the whole pipeline wires up without ever running it — the safe, free counterpart to execute/start. Reach for it to "just check" or "dry-run" a bundle.

main_pipe is optional for /validate. A bundle with no main_pipe still validates (200, is_valid: true) — you just get graph_spec: null. --pipe is ignored.
allow_signatures (--allow-signatures) tolerates unimplemented pipe signatures instead of rejecting the bundle (each dry-runs trivially by minting a mock). Useful for in-progress bundles. It parameterizes the dry-run sweep, so it rides validate only — the crate routes run no sweep and do not accept it. Default strict.
render (--render markdown) opts into a server-rendered Markdown view of the verdict, riding the 200 on both arms as rendered_markdown. This is what a skill-driven validate sends; a hook omits it and reads the structured is_valid. The structured fields stay the contract; the Markdown is the view.

Resolve & Codegen (the crate routes)

POST /v1/resolve resolves the library closure into its normalized crate — fully qualified refs, flattened refinement, materialized natives, fingerprint set. It runs no dry-run sweep (runnability is /validate's vocabulary). The valid arm carries the canonical JSON crate — the same bytes pipelex resolve --format json prints, so a fingerprint computed from either surface agrees.

POST /v1/codegen resolves the closure the same way, then projects the crate through two explicit axes: kind (only types is served — an inputs template is projected client-side from the input form POST /v1/pipe-io serves) × target:

ts-zod — zod schemas + inferred TypeScript types
python-pydantic — self-contained pydantic BaseModels
python-structures — Pipelex runtime StructuredContent classes (a Pipelex extension)

The valid arm carries the stamped artifact set plus its codegen.lock: a client that writes them verbatim reproduces a local pipelex codegen types run byte-for-byte and passes the offline pipelex codegen check. There is deliberately no server-side check route (the drift check is offline by design). An unknown kind/target, or a pipe_ref on the concept-set-wide types, is a request-shape 422.

--target is required for codegen — there is no default; ask the user which consumer they're generating for if unclear. The request also accepts a method_ref instead of files (an address-form reference is fetched and resolved; the registry form is a 422 request.unsupported) — this skill always sends files.

Callback URLs (the async `start` endpoint)

/v1/start is fire-and-forget: it returns a run_id immediately and POSTs the finished result to a webhook later, so a start request must carry callback_urls.

Postman push (default mode) always uses the {{callback_url}} collection variable — never a baked-in URL. The pushed body is "callback_urls": ["{{callback_url}}"], so the callback is switchable in the Postman environment right alongside base_url and auth_token (local vs hosted). Nothing to resolve — just tell the user to set callback_url in their Postman environment (a https://webhook.site/... endpoint is the easy way to watch the result land).

--run / --curl (a real HTTP call, or a preview of one) need a concrete URL. Resolution order there:

--callback-url <url> (repeatable — the field is a JSON list; a single URL is the common case).
CALLBACK_URL in the environment or the repo .env. make exports .env; the script also reads .env directly when invoked outside make.
Ask the user. If neither is set, the script stops with a clear error — ask for a callback URL and re-run with --callback-url.

This only affects start — no other endpoint takes callback_urls. The API's SSRF guard rejects loopback/private/metadata hosts, so a localhost callback won't validate — use a public https:// URL.

How to run it

Every command on this page runs from api/, the server's directory in the pipelex repository: cd api first, or prefix the make targets with -C api from the repository root (make -C api bundle-run …), and give BUNDLE and the script's paths relative to api/. The script lists the method's tree with the mthds package and reads the runner's limits from pipelex_api, so it runs with the server's own environment, api/.venv, never a bare python3. The make targets are the convenient wrapper: they install that environment, then run the script with it.

make bundle-run      BUNDLE=<dir|.mthds>                 # POST to a running API, print the response
make bundle-validate BUNDLE=<dir|.mthds>                 # dry-run validate via /v1/validate (free)
make bundle-resolve  BUNDLE=<dir|.mthds>                 # resolve to the normalized crate (free)
make bundle-codegen  BUNDLE=<dir|.mthds> TARGET=ts-zod   # project typed artifacts + lock (free)
make bundle-curl     BUNDLE=<dir|.mthds>                 # emit a ready-to-run curl
make bundle-postman  BUNDLE=<dir|.mthds>                 # push request(s) into the Postman collection
make bundle-dry      BUNDLE=<dir|.mthds>                 # print the request body only
# optional: ENDPOINT=execute|start|validate|resolve|codegen|both
#           PIPE=<code>  INPUTS=<path>  NAME=<folder>  ALLOW_SIGNATURES=1  TARGET=<codegen target>
#           CALLBACK_URL=<url>  BASE_URL=<url>  TOKEN=<bearer>  ARGS=<extra>

bundle-validate / bundle-resolve / bundle-codegen hardcode --run with their endpoint (they're free and safe to run live).

Under the hood it's one script that resolves the bundle once and sends the request to one of four sinks. <bundle-path> is a bundle directory (preferred) or a single .mthds file. Call the script directly for a path outside the repo or finer control, with the server's environment (make install creates it when it is missing):

Push into the Postman collection (default). Source the env first so the Postman key is present (it lives in ~/.zshenv):

source ~/.zshenv 2>/dev/null
.venv/bin/python .claude/skills/postman-bundle/scripts/build_postman_query.py <bundle-path> [--endpoint <ep>] [--target <t>]

After a successful push, tell the user it's in Postman (it auto-syncs), and that they should set base_url and auth_token in their Postman environment, then Send.

Run it yourself from Claude Code — POSTs the exact same body to a running API and prints the response:

.venv/bin/python .claude/skills/postman-bundle/scripts/build_postman_query.py <bundle-path> --run --endpoint <ep>
# defaults to http://127.0.0.1:8081 and no auth (AUTH_MODE=none). Override:
#   --base-url https://api.pipelex.com   --token <bearer>

The API must be up — start it locally with make run first. Heads-up: only execute/start trigger real inference (cost + latency) — confirm the user wants a live run for those. Every other endpoint is free and safe to fire without confirmation:

.venv/bin/python .claude/skills/postman-bundle/scripts/build_postman_query.py <bundle-path> --run --endpoint validate
.venv/bin/python .claude/skills/postman-bundle/scripts/build_postman_query.py <bundle-path> --run --endpoint resolve
.venv/bin/python .claude/skills/postman-bundle/scripts/build_postman_query.py <bundle-path> --run --endpoint codegen --target python-pydantic

Emit a curl command (--curl) — writes the body to a temp file and prints a curl --data @file, so the files' contents never need shell escaping. Preview only (--dry-run) — print the request body, touch nothing. Every mode prints the tree's root and the files it sends first, marking those that travel in Base64.

Useful options (apply across modes):

Option	When to use
`--endpoint <ep>`	Pick the endpoint (see cheat sheet). Default `both` (= execute + start) for Postman/curl; `--run both` runs `execute`.
`--target <t>`	(codegen only, required) `ts-zod`, `python-pydantic`, or `python-structures`.
`--allow-signatures`	(validate only) Tolerate unimplemented pipe signatures. Default strict.
`--render <fmt>`	(validate only) Opt-in server-side view, e.g. `--render markdown` → response carries `rendered_markdown` (what a skill-driven validate sends). Omit for the lean structured body a hook reads. Repeatable.
`--inputs <path>`	Inputs JSON lives elsewhere, or you point at a `.mthds` file directly (file mode does not auto-detect inputs). execute/start only.
`--pipe <pipe_code>`	The bundle has no `main_pipe`, or you want a different pipe. Used by execute/start (as `pipe_code`, exactly as given, so write `domain.pipe_code` when another bundle of the tree declares the same code); ignored by validate/resolve/codegen.
`--callback-url <url>`	(start only) Webhook the async result is POSTed to. Repeatable. Falls back to `CALLBACK_URL` in env/`.env`.
`--base-url` / `--token`	Target server + bearer for `--run`/`--curl`.
`--name <folder>`	Override the per-bundle Postman subfolder name.

Which endpoint/mode should I use?

"Run this bundle" → --run (execute; confirm first — real inference).
"Validate / dry-run / just check it" (no cost) → --run --endpoint validate (or make bundle-validate).
"Resolve it / get the crate / test /v1/resolve" → --run --endpoint resolve.
"Generate types / codegen / zod schemas / pydantic models" → --run --endpoint codegen --target <t> — ask which target if the user didn't say.
"Put it in Postman so I can click around" → default mode (push), with the right --endpoint.
"Give me the curl" → --curl. Just checking resolution → --dry-run.

Resolution rules (mirrors `pipelex run bundle`)

Directory → the main bundle is bundle.mthds if present, else the single *.mthds in the dir. Multiple .mthds and no bundle.mthds is ambiguous → ask the user to pass the file directly. The sibling inputs.json is auto-detected. The directory's whole method tree is sent as files, its bundles in subdirectories included, so multi-file methods resolve.
.mthds file → the main bundle is that file, and the tree it belongs to is sent: the package's tree when a METHODS.toml sits at or above its directory, else that one file. Inputs only come from --inputs.
pipe_code (run routes) is the main bundle's main_pipe, qualified by its domain (override with --pipe, sent as given).
inputs is the inputs.json content copied verbatim — exactly what the API receives, same as the CLI. The file itself is never one of the files.

Important: file/document inputs are out of scope

We are not doing uploads. If inputs.json references a local file (e.g. "url": "inputs/cv.pdf"), the script copies it verbatim and prints a warning listing those URLs. Pass that warning on to the user: those entries must be replaced with real https:// URLs before the request will run. Prefer demoing on a self-contained, text-input bundle.

The method's own files are a different matter: a template, an image or a module the steps read travels in files with the bundles. Python is the one limit: a tree holding a .py file runs only on a sandbox-hosted runner (any other answers a 501), and a Python structure class is refused on every runner, so a method whose concepts rely on a structures/ folder of Python classes, rather than inline [concept.X.structure] blocks, will not run over the API — flag that if you see a structures/ dir doing real work.

If something's missing

No POSTMAN_API_KEY → the script exits asking you to source ~/.zshenv. Do that and retry. If still unset, the user needs to add it (Postman → Settings → API keys → Generate).
No main_pipe and no --pipe (execute/start) → the script exits; ask the user which pipe and pass --pipe.
codegen without --target → the script exits listing the three targets; ask the user which consumer they're generating for.
start selected via --run/--curl and no callback URL → the script exits. Resolve from --callback-url, CALLBACK_URL in .env, or ask the user (e.g. a https://webhook.site/... endpoint). (The Postman push never hits this — it uses the {{callback_url}} variable and needs no resolution.)
No main_pipe, validating/resolving → fine, not an error: those endpoints need no pipe; validate just returns graph_spec: null.
The tree cannot be listed (a symbolic link or a name the standard refuses inside it, a malformed METHODS.toml, a tree past the runner's limits) or the main bundle is outside its own tree (.gitignored, say) → the script exits naming the cause; fix the method's directory, or point at the method's own folder rather than a directory above it.
ModuleNotFoundError: mthds or pipelex_api → the script ran with the wrong interpreter: use .venv/bin/python from api/ (make install creates it) or a make bundle-* target.

Constants

Collection UID: 35082494-559c5753-885c-409a-af63-7647fe28d301 (Pipelex FastAPI). Override with --collection-uid.
Postman API base: https://api.getpostman.com (the script handles GET + PUT).

About this skill

Automatic

QA & Test Engineering pack

SkillSafety

✓ 3 of 3 passed

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0 critical · 0 warning · 0 info — scanner v8 — 10/28/2026

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LLM judge · policy & harmful-content review

Source

Imported

pipelex / pipelex

api/.claude/skills/postman-bundle/SKILL.md

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License NOASSERTIONImported 10/28/2026Version v3Bundled 1 file · scripts

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What it changes

Track record

Use this skill

Postman Bundle

Endpoint cheat sheet

What it produces in Postman

The request body: the method as files

Validate = the API's dry-run

Resolve & Codegen (the crate routes)

Callback URLs (the async start endpoint)

How to run it

Which endpoint/mode should I use?

Resolution rules (mirrors pipelex run bundle)

Important: file/document inputs are out of scope

If something's missing

Constants

SkillSafety

Source

Related skills

More in the registry

The request body: the method as `files`

Callback URLs (the async `start` endpoint)

Resolution rules (mirrors `pipelex run bundle`)