alpacalyzer-algo-trader: Skill for Claude Code

.agents/skills/new-agent/SKILL.md

new-agent is a skill for Claude Code, Codex from kimrejstrom/alpacalyzer-algo-trader. It costs 34 tokens per session (703 once invoked), scanned A, original, MIT.

A coding skill for adding a new investment-analysis agent to a hedge-fund software project. The agent is a LangGraph workflow step that applies an investment philosophy and returns structured results.

In plain words
What is it for?
Use it when creating an agent such as a Ray Dalio or Peter Lynch analyst, including its prompt, code, signal output, configuration, workflow registration, and tests.
Why use it?
It gives the new agent a defined file location, implementation pattern, tests, and registration steps instead of leaving the project structure to guesswork.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: installed under .agents/ (shared by several agents).

This is kimrejstrom/alpacalyzer-algo-trader's own configuration. It tells Claude Code and Codex how to work on alpacalyzer-algo-trader itself, so it is not a mod to install elsewhere. Copy it as a starting point and replace the rules that are about this project. Everything alpacalyzer-algo-trader configures →

Reuse

Borrowing it

Nothing to install: this file belongs to kimrejstrom/alpacalyzer-algo-trader. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.

Copy the file
curl -O https://raw.githubusercontent.com/kimrejstrom/alpacalyzer-algo-trader/main/.agents/skills/new-agent/SKILL.md
Clone the repo
git clone --depth 1 https://github.com/kimrejstrom/alpacalyzer-algo-trader

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for new-agent

README.md
[![agentmods](https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent/github.svg)](https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent)
Your own site
<a href="https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for new-agent

Your own site · 80×15
<a href="https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent"><img src="https://agentmods.dev/badge/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 34 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 703 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00034 $0.00703
Opus 5 $0.00017 $0.00351
Sonnet 5 $0.00007 $0.00141
Haiku 4.5 $0.00003 $0.00070

Measured 11d ago against content hash 8d0c085afc25, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

new-agent scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/new-agent/SKILL.md · 75 lines

How it starts

The opening of the file, as written. The whole thing — 75 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Scope Constraint

  • Agent files go in src/alpacalyzer/agents/{name}_agent.py
  • Tests go in tests/test_{name}_agent.py
  • Agents are LangGraph nodes in the hedge fund workflow

Placeholders

  • <agent> — lowercase with underscores (e.g., ray_dalio)
  • <Agent> — PascalCase (e.g., RayDalio)

Steps

1. Study the reference implementation

Read src/alpacalyzer/agents/warren_buffet_agent.py — it's the canonical example. Also glance at src/alpacalyzer/agents/cathie_wood_agent.py for a second style.

Key patterns: each agent has a system prompt defining its investment philosophy, calls GPT via src/alpacalyzer/llm/, and returns structured output that updates LangGraph state.

2. Create agent file

Copy src/alpacalyzer/agents/warren_buffet_agent.pysrc/alpacalyzer/agents/<agent>_agent.py and modify:

  • SYSTEM_PROMPT — define the agent's unique investment philosophy
  • Function name — <agent>_agent(state: AgentState)
  • Signal model — reuse or extend WarrenBuffettSignal pattern
  • Return key — <agent>_signal

3. Register in agent config and hedge fund workflow

Edit src/alpacalyzer/agents/agents.py — add to ANALYST_CONFIG:

"<agent>": {
    "display_name": "<Agent Display Name>",
    "agent_func": <agent>_agent,
    "order": <next_number>,
},

Import the new agent function at the top of the file. The ANALYST_CONFIG dict is the single source of truth — hedge_fund.py reads from it automatically via get_analyst_nodes().

4. Write tests

Follow the pattern in tests/test_investor_agents.py:

  • Test bullish/bearish/neutral signal generation
  • Test error handling when LLM fails
  • Mock the LLM client (auto-mocked via conftest.py)

5. Run and verify

uv run pytest tests/test_<agent>_agent.py -v
uv run pytest tests/test_hedge_fund.py -v  # integration

Reference files

Purpose File
Reference agent src/alpacalyzer/agents/warren_buffet_agent.py
Second example src/alpacalyzer/agents/cathie_wood_agent.py
Agent registry src/alpacalyzer/agents/agents.py
LLM integration src/alpacalyzer/llm/
Workflow src/alpacalyzer/hedge_fund.py
Graph state src/alpacalyzer/graph/state.py
Prompts src/alpacalyzer/prompts/
Test pattern tests/test_investor_agents.py

Read the full file on GitHub · 75 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 75 lines · 34 tokens per session scan A 8d0c085afc25

Subscribe to this mod's changes

new-agent is a skill published in the GitHub repository kimrejstrom/alpacalyzer-algo-trader (2 stars, last pushed 3mo ago), licensed MIT. It adds 34 tokens to every session and 703 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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