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Deepgram Migration Deep Dive · jeremylongshore | DecimalAI

Codingby jeremylongshore

Deepgram Migration Deep Dive

Deep dive into migrating to Deepgram from other transcription providers.

Maintained

SkillScore

SkillScore74/100

Test results 74

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

57%74%of test cases passed +17 pts

Without skill13 of 23

With skill17 of 23

5 fixed12 passed either way5 still failing1 broke

Measured on gemini-3.6-flash8/14/2026v1 · latest is v222 comparable

This benchmark measured an earlier version of the skill.

Track record

Agent runs: 0; All time

See one task, with and without the skill

One test taskWhen implementing a TypeScript `DeepgramAdapter` class for pre-recorded transcription using `@deepgram/sdk`, what default parameters should be applied when specific options are omitted by the caller?

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 jeremylongshore-deepgram-migration-deep-dive
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("jeremylongshore-deepgram-migration-deep-dive")
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 jeremylongshore-deepgram-migration-deep-dive

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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md https://app.decimal.ai/s/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/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/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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("jeremylongshore-deepgram-migration-deep-dive")
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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/jeremylongshore-deepgram-migration-deep-dive/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 jeremylongshore-deepgram-migration-deep-dive

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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md https://app.decimal.ai/s/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/jeremylongshore-deepgram-migration-deep-dive/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/jeremylongshore-deepgram-migration-deep-dive/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/jeremylongshore-deepgram-migration-deep-dive.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: v2 (v1 tested 8/14/2026)
Source: jeremylongshore/claude-code-plugins-plus-skills ↗
License: MIT

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

Deepgram Migration Deep Dive

Prerequisites

An inventory of existing audio/transcript flows, data retention/consent obligations, evaluation fixtures, and rollback owner.
A staging environment with separate credentials and a compatibility/quality acceptance threshold.

Examples

Run the old and new transcription flow against approved non-sensitive fixtures, compare aggregate accuracy/latency/error metrics, and route a small reversible staging canary only after acceptance passes. Roll back immediately on quality, consent, or operational regression; do not bulk-migrate or reprocess customer recordings before the observation window closes.

Current State

!npm list @deepgram/sdk 2>/dev/null | grep deepgram || echo 'Not installed' !npm list @aws-sdk/client-transcribe 2>/dev/null | grep transcribe || echo 'AWS Transcribe SDK not found' !pip show google-cloud-speech 2>/dev/null | grep Version || echo 'Google STT not found'

Overview

Migrate to Deepgram from AWS Transcribe, Google Cloud Speech-to-Text, Azure Cognitive Services, or OpenAI Whisper. Uses an adapter pattern with a unified interface, parallel running for quality validation, percentage-based traffic shifting, and automated rollback.

Feature Mapping

AWS Transcribe -> Deepgram

AWS Transcribe	Deepgram	Notes
`LanguageCode: 'en-US'`	`language: 'en'`	ISO 639-1 (2-letter)
`ShowSpeakerLabels: true`	`diarize: true`	Same feature, different param
`VocabularyName: 'custom'`	`keywords: ['term:1.5']`	Inline boosting, no pre-upload
`ContentRedactionType: 'PII'`	`redact: ['pci', 'ssn']`	Granular PII categories
`OutputBucketName`	`callback: 'https://...'`	Callback URL, not S3
Job polling model	Sync response or callback	No polling needed

Google Cloud STT -> Deepgram

Google STT	Deepgram	Notes
`RecognitionConfig.encoding`	Auto-detected	Deepgram auto-detects format
`RecognitionConfig.sampleRateHertz`	`sample_rate` (live only)	REST auto-detects
`RecognitionConfig.model: 'latest_long'`	`model: 'nova-3'`	Direct mapping
`SpeakerDiarizationConfig`	`diarize: true`	Simpler configuration
`StreamingRecognize`	`listen.live()`	WebSocket vs gRPC

OpenAI Whisper -> Deepgram

Whisper	Deepgram	Notes
Local GPU processing	API call	No GPU needed
`whisper.transcribe(audio)`	`listen.prerecorded.transcribeFile()`	Similar interface
`model='large-v3'`	`model: 'nova-3'`	10-100x faster
`language='en'`	`language: 'en'`	Same format
No diarization	`diarize: true`	Deepgram advantage
No streaming	`listen.live()`	Deepgram advantage

Instructions

Step 1: Adapter Pattern

interface TranscriptionResult {
  transcript: string;
  confidence: number;
  words: Array<{ word: string; start: number; end: number; speaker?: number }>;
  duration: number;
  provider: string;
}

interface TranscriptionAdapter {
  transcribeUrl(url: string, options: any): Promise<TranscriptionResult>;
  transcribeFile(path: string, options: any): Promise<TranscriptionResult>;
  name: string;
}

Step 2: Deepgram Adapter

import { createClient } from '@deepgram/sdk';
import { readFileSync } from 'fs';

class DeepgramAdapter implements TranscriptionAdapter {
  name = 'deepgram';
  private client: ReturnType<typeof createClient>;

  constructor(apiKey: string) {
    this.client = createClient(apiKey);
  }

  async transcribeUrl(url: string, options: any = {}): Promise<TranscriptionResult> {
    const { result, error } = await this.client.listen.prerecorded.transcribeUrl(
      { url },
      {
        model: options.model ?? 'nova-3',
        smart_format: true,
        diarize: options.diarize ?? false,
        language: options.language ?? 'en',
        keywords: options.keywords,
        redact: options.redact,
      }
    );
    if (error) throw new Error(`Deepgram: ${error.message}`);
    return this.normalize(result);
  }

  async transcribeFile(path: string, options: any = {}): Promise<TranscriptionResult> {
    const audio = readFileSync(path);
    const { result, error } = await this.client.listen.prerecorded.transcribeFile(
      audio,
      {
        model: options.model ?? 'nova-3',
        smart_format: true,
        diarize: options.diarize ?? false,
      }
    );
    if (error) throw new Error(`Deepgram: ${error.message}`);
    return this.normalize(result);
  }

  private normalize(result: any): TranscriptionResult {
    const alt = result.results.channels[0].alternatives[0];
    return {
      transcript: alt.transcript,
      confidence: alt.confidence,
      words: (alt.words ?? []).map((w: any) => ({
        word: w.punctuated_word ?? w.word,
        start: w.start,
        end: w.end,
        speaker: w.speaker,
      })),
      duration: result.metadata.duration,
      provider: 'deepgram',
    };
  }
}

Step 3: AWS Transcribe Adapter (Legacy)

// Legacy adapter — wraps existing AWS Transcribe code for parallel running
import { TranscribeClient, StartTranscriptionJobCommand, GetTranscriptionJobCommand }
  from '@aws-sdk/client-transcribe';

class AWSTranscribeAdapter implements TranscriptionAdapter {
  name = 'aws-transcribe';
  private client: TranscribeClient;

  constructor() {
    this.client = new TranscribeClient({});
  }

  async transcribeUrl(url: string, options: any = {}): Promise<TranscriptionResult> {
    const jobName = `migration-${Date.now()}`;

    await this.client.send(new StartTranscriptionJobCommand({
      TranscriptionJobName: jobName,
      LanguageCode: options.language ?? 'en-US',
      Media: { MediaFileUri: url },
      Settings: {
        ShowSpeakerLabels: options.diarize ?? false,
        MaxSpeakerLabels: options.diarize ? 10 : undefined,
      },
    }));

    // Poll for completion (AWS is async-only)
    let job;
    do {
      await new Promise(r => setTimeout(r, 5000));
      const result = await this.client.send(new GetTranscriptionJobCommand({
        TranscriptionJobName: jobName,
      }));
      job = result.TranscriptionJob;
    } while (job?.TranscriptionJobStatus === 'IN_PROGRESS');

    if (job?.TranscriptionJobStatus !== 'COMPLETED') {
      throw new Error(`AWS Transcribe failed: ${job?.FailureReason}`);
    }

    // Fetch and normalize result
    const response = await fetch(job.Transcript!.TranscriptFileUri!);
    const data = await response.json();

    return {
      transcript: data.results.transcripts[0].transcript,
      confidence: 0, // AWS doesn't provide overall confidence
      words: data.results.items
        .filter((i: any) => i.type === 'pronunciation')
        .map((i: any) => ({
          word: i.alternatives[0].content,
          start: parseFloat(i.start_time),
          end: parseFloat(i.end_time),
          speaker: i.speaker_label ? parseInt(i.speaker_label.replace('spk_', '')) : undefined,
        })),
      duration: 0,
      provider: 'aws-transcribe',
    };
  }

  async transcribeFile(path: string): Promise<TranscriptionResult> {
    throw new Error('Upload to S3 first, then use transcribeUrl');
  }
}

Step 4: Migration Router with Traffic Shifting

class MigrationRouter {
  private adapters: Map<string, TranscriptionAdapter> = new Map();
  private deepgramPercent: number;

  constructor(deepgramPercent = 0) {
    this.deepgramPercent = deepgramPercent;
  }

  register(adapter: TranscriptionAdapter) {
    this.adapters.set(adapter.name, adapter);
  }

  setDeepgramPercent(percent: number) {
    this.deepgramPercent = Math.max(0, Math.min(100, percent));
    console.log(`Traffic split: ${this.deepgramPercent}% Deepgram, ${100 - this.deepgramPercent}% legacy`);
  }

  async transcribe(url: string, options: any = {}): Promise<TranscriptionResult> {
    const useDeepgram = Math.random() * 100 < this.deepgramPercent;
    const primary = useDeepgram ? 'deepgram' : this.getLegacyName();
    const adapter = this.adapters.get(primary);

    if (!adapter) throw new Error(`Adapter not found: ${primary}`);

    const start = Date.now();
    const result = await adapter.transcribeUrl(url, options);
    const elapsed = Date.now() - start;

    console.log(`[${primary}] ${elapsed}ms, confidence: ${result.confidence.toFixed(3)}`);
    return result;
  }

  private getLegacyName(): string {
    for (const [name] of this.adapters) {
      if (name !== 'deepgram') return name;
    }
    throw new Error('No legacy adapter registered');
  }
}

// Migration rollout:
const router = new MigrationRouter(0);
router.register(new AWSTranscribeAdapter());
router.register(new DeepgramAdapter(process.env.DEEPGRAM_API_KEY!));

// Week 1: 5% to Deepgram
router.setDeepgramPercent(5);
// Week 2: 25%
router.setDeepgramPercent(25);
// Week 3: 50%
router.setDeepgramPercent(50);
// Week 4: 100% — migration complete
router.setDeepgramPercent(100);

Step 5: Parallel Running and Quality Validation

async function validateMigration(
  testAudioUrls: string[],
  legacyAdapter: TranscriptionAdapter,
  deepgramAdapter: TranscriptionAdapter,
  minSimilarity = 0.85
) {
  console.log(`Validating ${testAudioUrls.length} files (min similarity: ${minSimilarity})`);

  const results: Array<{
    url: string;
    similarity: number;
    legacyConfidence: number;
    deepgramConfidence: number;
    legacyTime: number;
    deepgramTime: number;
    pass: boolean;
  }> = [];

  for (const url of testAudioUrls) {
    const legacyStart = Date.now();
    const legacy = await legacyAdapter.transcribeUrl(url);
    const legacyTime = Date.now() - legacyStart;

    const dgStart = Date.now();
    const dg = await deepgramAdapter.transcribeUrl(url);
    const dgTime = Date.now() - dgStart;

    // Jaccard similarity
    const words1 = new Set(legacy.transcript.toLowerCase().split(/\s+/));
    const words2 = new Set(dg.transcript.toLowerCase().split(/\s+/));
    const intersection = new Set([...words1].filter(w => words2.has(w)));
    const union = new Set([...words1, ...words2]);
    const similarity = intersection.size / union.size;

    results.push({
      url: url.substring(url.lastIndexOf('/') + 1),
      similarity,
      legacyConfidence: legacy.confidence,
      deepgramConfidence: dg.confidence,
      legacyTime,
      deepgramTime,
      pass: similarity >= minSimilarity,
    });
  }

  // Report
  const passCount = results.filter(r => r.pass).length;
  console.log(`\n=== Validation Results ===`);
  for (const r of results) {
    console.log(`${r.pass ? 'PASS' : 'FAIL'} ${r.url}: similarity=${(r.similarity * 100).toFixed(1)}% ` +
      `(legacy: ${r.legacyTime}ms, deepgram: ${r.deepgramTime}ms)`);
  }
  console.log(`\n${passCount}/${results.length} passed (${(passCount / results.length * 100).toFixed(0)}%)`);

  return { results, allPassed: passCount === results.length };
}

Step 6: Migration Checklist

Phase	Actions	Duration
Assessment	Audit current usage, map features, estimate costs	1 week
Setup	Install SDK, implement adapter pattern, create test suite	1 week
Validation	Parallel run on test corpus, measure similarity	1 week
Rollout 5%	Enable for 5% of traffic, monitor closely	1 week
Rollout 25%	Increase if no issues, monitor error rate	1 week
Rollout 50%	Continue monitoring, compare costs	1 week
Rollout 100%	Full cutover, decommission legacy	1 week
Cleanup	Remove legacy adapter, update docs	1 week

Output

Unified TranscriptionAdapter interface
Deepgram and legacy (AWS/Google) adapter implementations
Migration router with percentage-based traffic shifting
Parallel running with Jaccard similarity validation
Migration timeline and checklist

Error Handling

Issue	Cause	Solution
Low similarity	Feature mapping incomplete	Check options mapping (language, diarize)
Deepgram slower than expected	First request cold start	Pre-warm with test request
Missing features	No direct equivalent	Use `keywords` for custom vocab
Rollback needed	Quality regression	`router.setDeepgramPercent(0)` immediately

Resources

When it activates

Automatically, when a task matches. Use when migrating from AWS Transcribe, Google Cloud STT, Azure Speech, OpenAI Whisper, AssemblyAI, or Rev.ai to Deepgram. Trigger: "deepgram migration", "switch to deepgram", "migrate transcription", "deepgram from AWS", "deepgram from Google", "replace whisper with deepgram".

About this skill

Automatic

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Source

Imported

jeremylongshore / claude-code-plugins-plus-skills

skills/.curated/deepgram-migration-deep-dive/SKILL.md

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License MITImported 10/23/2026Version v2Bundled 1 file

Illustrative Want to see what jeremylongshore/deepgram-migration-deep-dive would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

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What it changes

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Use this skill

Deepgram Migration Deep Dive

Prerequisites

Examples

Current State

Overview

Feature Mapping

AWS Transcribe -> Deepgram

Google Cloud STT -> Deepgram

OpenAI Whisper -> Deepgram

Instructions

Step 1: Adapter Pattern

Step 2: Deepgram Adapter

Step 3: AWS Transcribe Adapter (Legacy)

Step 4: Migration Router with Traffic Shifting

Step 5: Parallel Running and Quality Validation

Step 6: Migration Checklist

Output

Error Handling

Resources

When it activates

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Source

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