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Fixed Income Credit Analysis Engine v0.3.2 · tywinlu1988 | DecimalAI

Financeby tywinlu1988

Fixed Income Credit Analysis Engine v0.3.2

Use when analyzing industries or companies for credit decisions in fixed income markets, building industry analysis frameworks for lending or bond investment, evaluating credit quality via dual-track methodology, constructing investment dashboards from public data, validating frameworks against historical defaults, or assessing cross-industry contagion, portfolio concentration, and systemic risk via the system-intelligence layer. Route vague needs to the credit-analysis-router skill.

Never made a case worseMaintained

SkillScore

SkillScore96/100

Test results 100 · Live runs 91

Safety

3/3 safety checks passed

Code scan
Intent
Content

What it changes

14%100%of test cases passed +86 pts

Without skill3 of 22

With skill22 of 22

19 fixed3 passed either way0 still failing0 broke

Measured on gemini-3.6-flash8/10/2026v221 comparable

Track record

Agent runs: 68; 57 in the last 30 days

See one task, with and without the skill

One test taskHow many analytical paradigms (P1-P6) does the engine recognize for mapping industries, and which paradigm governs sovereign-linked or government-supported credits?

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 tywinlu1988-fixed-income-credit-analysis
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("tywinlu1988-fixed-income-credit-analysis")
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 tywinlu1988-fixed-income-credit-analysis

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/tywinlu1988-fixed-income-credit-analysis/SKILL.md https://app.decimal.ai/s/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Copy SKILL.md

Save it in your project, as

.claude/skills/tywinlu1988-fixed-income-credit-analysis/SKILL.md

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Claude Code docs

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

Install

pip install "decimalai[claude-agent-sdk]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Claude Agent SDK docs

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

Install

pip install "decimalai[openai-agents]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
from agents import Agent, Runner

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

OpenAI Agents docs

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

Install

pip install "decimalai[openai]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
from openai import OpenAI

client = OpenAI()
skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

OpenAI SDK docs

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

Install

pip install "decimalai[anthropic]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
import anthropic

client = anthropic.Anthropic()
skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Anthropic docs

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

Install

pip install "decimalai[google-genai]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/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/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Gemini API docs

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

Install

pip install "decimalai[langchain]" langchain-openai
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
from langchain.agents import create_agent

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

LangChain docs

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

Install

pip install "decimalai>=0.16" "crewai>=1.15.27"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

CrewAI docs

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

Install

pip install "decimalai[adk]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

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

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Google ADK docs

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

Install

pip install "decimalai[pydantic-ai]"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
from pydantic_ai import Agent

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Pydantic AI docs

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

Install

pip install "decimalai>=0.16" deepagents
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
from deepagents import create_deep_agent

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Deep Agents docs

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

Install

pip install "decimalai>=0.16" agent-framework
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

import asyncio
from pathlib import Path
from agent_framework import Agent

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Microsoft Agent Framework docs

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

Install

pip install "decimalai>=0.16" strands-agents
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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("tywinlu1988-fixed-income-credit-analysis")
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/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Give it to your agent

from pathlib import Path
from strands import Agent

skill = Path(".agents/skills/tywinlu1988-fixed-income-credit-analysis/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 4 other files are not included.

Other ways to add it

Strands docs

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

pip install "decimalai>=0.15"
decimalai skills add tywinlu1988-fixed-income-credit-analysis

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/tywinlu1988-fixed-income-credit-analysis/SKILL.md https://app.decimal.ai/s/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Copy SKILL.md

Save it in your project, as

.agents/skills/tywinlu1988-fixed-income-credit-analysis/SKILL.md

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Cursor and other editors docs

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

Get the file

curl -fsSL https://app.decimal.ai/s/tywinlu1988-fixed-income-credit-analysis/SKILL.md

Read the summary as JSON

curl -fsSL https://app.decimal.ai/s/tywinlu1988-fixed-income-credit-analysis.json

This gets SKILL.md only. The skill's 4 other files are not included.

Other ways to add it

Other setups docs

You don't need a DecimalAI account.

Version: v2, tested 8/10/2026
Source: tywinlu1988/credence-global ↗
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

Path Resolution

See dev/engine/agent-protocol.md §1 — engine/ and templates/ paths resolve to the package root in both plugin-install and open-as-project modes.

Invocation Protocol

Non-Negotiables (see AGENTS.md): no analysis without a Path Sheet · no numbers without a doc §section citation (engine_undefined otherwise) · no report outside dev/templates/ · no delivery without a QA Verdict · no invented dimensions or vocabulary · follow the path's Playbook (dev/engine/path-playbooks/<path_id>.md).

When this Skill is invoked:

Path-sheet-driven (preferred). If the user message carries a Path Sheet produced by the credit-analysis-router skill, read dev/engine/path-playbooks/<path_id>.md first, then the engine documents in the sheet's engine_reading_order order, and validate against its quality_gates.
Explicit registered path_id. If the user explicitly names a registered path (e.g., "run WP-RO-01"), treat that as the path selection: load the Playbook, then the core set — dev/engine/engine-overview.md + dev/engine/dual-track-methodology.md — plus any topic-specific doc the request names.
No Path Sheet and no explicit path_id (strict). If the request would produce a credit conclusion/rating/score, STOP — do not analyze. Route through the credit-analysis-router skill first, or ask the Q1–Q4 questions (role / object / depth / data) yourself to pick a path from dev/engine/work-path-registry.md. Exception: pure knowledge questions (e.g., "what is the SRI formula?", "explain the veto mechanism") may be answered directly from engine documents with citations.
Use only thresholds, weights, rating mappings, and veto rules found in those documents.
For every quantitative judgment, cite the source document and section.
If a required threshold, weight, or mapping is missing from the engine documents, output engine_undefined and do not invent a value.
Do not invoke Mode B or generate external-data values unless the user has explicitly provided a CSV upload, API endpoint, or MCP server. Treat Mode B fields as data gaps until then.
Before delivering: hand the analysis artifact to credit-report-builder (if a report is requested) and obtain a passing QA Verdict from credit-qa-verifier. No verdict, no delivery.

Fixed Income Credit Analysis Engine v0.3.2

Overview

A systematic methodology for evaluating corporate credit quality in fixed income markets. The engine operates in three layers: (1) a Mosaic Engine that assembles fragmented public data into coherent signals; (2) a Dual-Track Engine combining industry-specific multi-layer analysis pyramids with market-based pricing signals; and (3) a System-Intelligence Layer that models cross-industry contagion, portfolio concentration, and a market-wide Systemic Risk Index (SRI). Combines multi-stakeholder perspectives into a unified assessment framework.

Core principles:

Traditional financial analysis systematically fails in policy-driven, technology-barrier, and asset-lease industries. The heaviest credit factor is rarely on the balance sheet.
External credit ratings consistently lag true credit deterioration by 17+ months.
Mosaic theory: Individual public data fragments are meaningless alone; assembled together they form coherent signals.
Information completeness theory: Data gaps are not defects — they are risk signals. "We don't have this data" itself tells the user something meaningful.

When to Use

Building an industry credit analysis framework from scratch
Evaluating a specific company for lending or bond investment decisions
Constructing a multi-dimensional investment dashboard (relative value + sector allocation fit + curve positioning + event calendar)
Assembling fragmented public data into a coherent credit assessment using mosaic theory
Retroactively validating analytical frameworks against historical defaults
Evaluating sovereign-linked or government-supported credit → read dev/engine/external-support-framework.md
Conducting ESG/governance risk scans and fraud detection → read dev/engine/esg-framework.md and dev/engine/governance-fraud-risk.md
Performing LGD/recovery rate analysis for default scenarios → read dev/engine/lgd-recovery-framework.md
Assessing external support (government, parent company) impact on creditworthiness → read dev/engine/external-support-framework.md
Evaluating financial bonds → read dev/engine/financial-bond-framework.md
Analyzing holding companies → read dev/engine/holding-company-framework.md
Assessing cross-industry contagion risk from a stressed issuer or sector
Evaluating portfolio concentration across industry, region, rating, tenor, and funding-channel dimensions
Computing the Systemic Risk Index (SRI) and interpreting the four-level thermometer
Mapping an industry to one of the six analytical paradigms (cyclical, defensive, growth, regulated-utility, financial, sovereign-linked)

Mandatory Density Rules (mandatory)

Critical dimension signal density <20% → MUST NOT output a numeric score for that dimension; state insufficient information to evaluate and list the missing signals.
Weighted-average density across scored dimensions <50% → MUST NOT output a final letter rating; output a qualitative directional assessment plus a prioritized gap list.
Density 50–80% → MAY rate but MUST label medium confidence and widen the implied interval by ±1 notch.
The completeness report is mandatory for every analysis; omitting it is a protocol violation.

Full confidence/density model and gap-to-risk mapping: references/mosaic-engine-architecture.md (threshold single source: dev/engine/mosaic-engine.md).

Mode B: External Data Source Adapter (Placeholder)

Mode B guardrail: Unless the user explicitly provides a CSV upload, API endpoint, or MCP server, do not invoke Mode B interfaces or generate external data values. When Mode B is not active, all Mode B fields must be treated as data gaps.

Defined but not implemented. Adapter contract (query_bond_analytics / query_market_data / query_industry_benchmark) and connection priority (CSV > REST API > MCP > DB): dev/engine/mosaic-engine.md.

Veto & Rating Ceiling (mandatory)

One-vote veto: When any analysis layer triggers a one-vote veto condition, the issuer's rating ceiling is locked at CCC and may not be raised. Veto conditions for each layer: dev/engine/industry-framework.md §5.
Rating mapping must use the official 12-notch table (dev/engine/dual-track-methodology.md §6); do not create custom notches.
For sovereign-linked entities (P6): fiscal capacity and institutional strength govern, and support willingness determines the final notch (dev/engine/external-support-framework.md).

Two-Track Parallel Structure (Core)

Track A (fundamental, qualitative+scoring, L1 heaviest → L4 lightest) and Track B (market pricing: credit spreads / volatility / fund flows / rating migration) run in parallel, then feed a Cross-Comparison Matrix. Consensus reinforces; divergence is the most valuable insight.

When tracks diverge, prioritize Track A (auditable financial facts) over Track B (external ratings).

Full pyramid weights, Track-B thresholds, and the cross-comparison matrix: references/industry-scoring.md and dev/engine/dual-track-methodology.md.

System-Intelligence Layer

Aggregates issuer assessments into portfolio/market signals: cross-industry contagion (19×19 matrix), five-dimensional concentration, and the Systemic Risk Index SRI = Σ(industry_risk_score × industry_weight_pct) (scale 0–3+).

SRI thermometer: 🟢 normal (<0.5), 🟡 watch (0.5–1.0), 🟠 alert (1.0–1.8), 🔴 danger (≥1.8).

Full specification: references/system-intelligence.md and dev/engine/systemic-warning-framework.md.

Key Design Principles

Financial analysis is NEVER the heaviest layer. The heaviest factor is structural/external.
Each industry has a different heaviest factor determined by 10-dimension scoring.
Don't jump layers. L1 must pass before L2 is meaningful.
L4 validates, never overrules. Poor financials with strong upper layers = may be investing through cycle. Strong financials with weak upper layers = MORE dangerous (peak cycle or fraud).
Public data is sufficient across the 13 covered industries.
Track B is independent, not subordinate. Divergence generates the most valuable questions.
When tracks clash, prioritize auditable financial facts over external ratings.
Data gaps are not defects — they are risk signals. Every analysis includes a completeness report.
The framework identifies structural unsustainability but cannot predict default timing or specific triggers.

Chaining

Upstream: credit-analysis-router — consumes its Path Sheet (see Invocation Protocol).
Output: After analysis completes, output the Analysis Artifact (schema at dev/engine/pipeline-contract.md §2.2); path_id is inherited from the Path Sheet.
Downstream (REQUIRED NEXT SUB-SKILL): credit-report-builder — hand the Analysis Artifact to this skill for report assembly; this skill does not perform report assembly.

References

Details have been moved to references/ (single source of truth remains dev/engine/ engine documents):

references/mosaic-engine-architecture.md — Mosaic Engine Mode A: signal confidence / density assessment / gap mapping / completeness output
references/industry-scoring.md — Track A industry pyramid · six paradigms (P1-P6) · D1-D10
references/system-intelligence.md — Cross-industry contagion · five-dimensional concentration · SRI thermometer
references/stakeholder-paths.md — Multi-stakeholder views · portfolio manager dashboard · Path Sheet consumption guide
dev/.claude/skills/credit-analysis-router/SKILL.md — Requirement interpretation / routing layer, outputs Path Sheet
dev/engine/work-path-registry.md — Work path registry (path sheet single source of truth)

Version History

Version	Date	Changes
0.1.0	2026-07-07	Initial release. 10-dim scoring, 4-layer pyramid, dual-track, 7 industries, solar forward-validated
0.3.0	2026-07-08	Mosaic engine architecture (Mode A+B). Multi-stakeholder coverage map. P0 bond investment dashboard. Signal confidence + density metrics. Completeness reporting. Mode B adapter interface (placeholder).
0.4.0-alpha	2026-07-08	LGD/recovery, external support, outlook/monitoring, LGFV, ESG + governance/fraud, non-credit overlay, financial bond, holding company frameworks. Layered output. Multi-stakeholder coverage completed.
0.7.0-alpha	2026-07-13	System-intelligence layer: contagion theory/matrix, five-dimensional concentration, systemic warning (SRI), 13-industry coverage, six analytical paradigms.
0.7.1-release	2026-07-15	Dev-stack reorganization finalized; validation artifacts separated (root validation/, never in snapshots). Version headers promoted.
0.7.4	2026-07-15	SKILL.md slimmed to a navigator (≤150 lines); detail sunk to `references/` (mosaic / industry / system-intelligence / stakeholder). Invocation Protocol is now path-sheet-driven (`engine_reading_order`). LGV→LGFV naming unified.
v0.0.1	2026-07-18	International release: 19-industry GICS contagion matrix, six international paradigms (P1-P6), six buy-side roles, S&P/Moody's/Fitch rating alignment, IFRS/US GAAP framework. LGFV framework retired (China-specific).
v0.0.2	2026-07-21	Paradigm taxonomy unified on industry-framework P1-P6; references rebuilt (ghost LGFV/Corporate-Financing references removed); outlook migration matrix completed for all 18 tiers.

About this skill

Automatic

Finance & Accounting pack

SkillSafety

✓ 3 of 3 passed

Code scan15 of 15 checks clean

0 critical · 0 warning · 0 info — scanner v8 — 10/22/2026

View 15 scanner checks

live_secretremote_code_executionreverse_shelldata_exfiltrationssrf_metadataagent_snoopinganti_refusaldestructive_commandsobfuscated_unicodeunicode_homoglyphopaque_blobsuspicious_urltool_overreachtrigger_abuseprompt_injection_phrasing

IntentSafe

LLM judge · does the skill do what it claims

ContentClean

LLM judge · policy & harmful-content review

Source

Imported

tywinlu1988 / credence-global

dev/.claude/skills/fixed-income-credit-analysis/SKILL.md

View on GitHub ↗

License MITImported 8/8/2026Version v2Bundled 4 files

Illustrative Want to see what tywinlu1988/fixed-income-credit-analysis would put in front of your model? Preview the rendered body and check its trigger phrases — no LLM call, no signup.

Other skills for the same job

Published skills whose descriptions closely match this one, with their headline benchmark lift.

tywinlu1988/credit-analysis-routerIntake router for vague or compound fixed-income credit-analysis requests such as 'evaluate this company', 'check this portfolio for issues', 'what analysis should I run', 'where to start'. Use when the need is ambiguous, spans multiple roles (Credit Selector/Portfolio Manager/Advisor/Trader/Risk Officer/Individual Investor), or the user asks which analysis to run or where to start. If the user already names a concrete methodology task or engine path, use the fixed-income-credit-analysis skill iClose match+78 pts pass-rate lift

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From the same source and nearby in the catalogue.

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SkillScore

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What it changes

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Use this skill

Path Resolution

Invocation Protocol

Fixed Income Credit Analysis Engine v0.3.2

Overview

When to Use

Mandatory Density Rules (mandatory)

Mode B: External Data Source Adapter (Placeholder)

Veto & Rating Ceiling (mandatory)

Two-Track Parallel Structure (Core)

System-Intelligence Layer

Key Design Principles

Chaining

References

Version History

SkillSafety

Source

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