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.
curl -O https://raw.githubusercontent.com/kimrejstrom/alpacalyzer-algo-trader/main/.agents/skills/new-agent/SKILL.mdgit clone --depth 1 https://github.com/kimrejstrom/alpacalyzer-algo-traderWrote 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.
[](https://agentmods.dev/skills/kimrejstrom/alpacalyzer-algo-trader/new-agent)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.py → src/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
WarrenBuffettSignalpattern - 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 |
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.
- 11d ago First seen · 75 lines · 34 tokens per session scan A 8d0c085afc25
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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