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alphacouncil-agent

Inspectable, source-traceable public-equity research for Codex, Claude Code, OpenCode, Grok Build and ChatGPT Work developer mode. 26 provisional method lenses, 34 MCP tools. Research software, not investment advice.

Install / Use

claude mcp add Zhao73 -- npx -y github:Zhao73/alphacouncil-agent

If the server publishes to npm under a different name, use that package instead — check the repo README.

About this skill
🔌

MCP Server

Model Context Protocol server

Quality Score

79/100

Supported Platforms

Claude Code
Claude Desktop
OpenAI Codex

Tags

Our assessment of alphacouncil-agent

alphacouncil-agent scores 79/100 on our quality scale, 777th of 938 AI & Machine Learning skills we index.

Its MCP Server is 11 KB long, well organised into 12 sections with 3 code examples: a thorough specification that gives an agent plenty to work with.

It has 3 GitHub stars, so there is little community track record yet; judge it on its content.

Substance
29/30
Structure
18/20
Description
15/15
Adoption
3/20
Freshness
15/15

Maintenance, license and trust

  • The repository was last updated 35 days ago, so alphacouncil-agent is actively maintained.
  • It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
  • Its trust signals score 92/100, with 1 caution from licensing, adoption, age or documentation. These come from repository metadata, not a code audit — read the skill file before letting an agent act on it.

alphacouncil-agent compared with similar skills

All 4 of these similar skills score higher than alphacouncil-agent; compare them before choosing.

SkillScoreStarsUpdatedFormat
alphacouncil-agent (this skill)by Zhao7379335d agoMCP Server
Agent-Reachby Panniantong100106.4k5d agoCLAUDE.md
claude-memby thedotmack100105.0ktodayCLAUDE.md
Understand-Anythingby Egonex-AI10088.9k4d agoCLAUDE.md
headroomby headroomlabs-ai10078.3ktodayCLAUDE.md

Frequently asked questions

How do I install alphacouncil-agent?
Run claude mcp add Zhao73 -- npx -y github:Zhao73/alphacouncil-agent. The install tabs above show the steps for each supported agent.
Which AI agents does alphacouncil-agent work with?
It is written for Claude Code, Claude Desktop and OpenAI Codex, as a MCP Server file. Other agents that read the same format can often use it too.
Is alphacouncil-agent safe to use?
It is MIT-licensed and scores 92/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
Is alphacouncil-agent still maintained?
The repository was last updated 35 days ago, so alphacouncil-agent is actively maintained.

<a name="readme-top"></a>

<!-- readme-section:hero --> <div align="center"> <img src="assets/banner.png" alt="AlphaCouncil Agent" width="100%" />

An investment-research council you can inspect

One request becomes sourced evidence, competing method views, a bull/bear challenge, and a portfolio-manager verdict.

English · 中文 · 日本語

<p> <img src="https://img.shields.io/github/actions/workflow/status/Zhao73/alphacouncil-agent/check.yml?style=for-the-badge&label=build&logo=githubactions&logoColor=white&color=1a7a6a" alt="build" /> <img src="https://img.shields.io/badge/License-MIT-c9a227?style=for-the-badge" alt="MIT" /> <img src="https://img.shields.io/badge/Node-%3E%3D18-339933?style=for-the-badge&logo=node.js&logoColor=white" alt="node" /> <img src="https://img.shields.io/github/stars/Zhao73/alphacouncil-agent?style=for-the-badge&logo=github&color=0d4d4d" alt="stars" /> </p> <p> <img src="https://img.shields.io/badge/OpenAI_Codex-412991?style=for-the-badge&logo=openai&logoColor=white" alt="OpenAI Codex" /> <img src="https://img.shields.io/badge/Claude_Code-D97757?style=for-the-badge&logo=anthropic&logoColor=white" alt="Claude Code" /> <img src="https://img.shields.io/badge/OpenCode-1a7a6a?style=for-the-badge" alt="OpenCode" /> <img src="https://img.shields.io/badge/Grok_Build-000000?style=for-the-badge&logo=x&logoColor=white" alt="Grok Build" /> <img src="https://img.shields.io/badge/ChatGPT_Work-dev_mode-412991?style=for-the-badge&logo=openai&logoColor=white" alt="ChatGPT Work developer mode" /> </p> <p> <img src="https://img.shields.io/badge/MCP-compatible-000000?style=for-the-badge" alt="MCP compatible" /> <img src="https://img.shields.io/badge/data_check-no_vendor_key-2ea043?style=for-the-badge" alt="Core data check needs no vendor key" /> <img src="https://img.shields.io/badge/runtime_dependencies-zero-2ea043?style=for-the-badge" alt="Zero runtime dependencies" /> </p>

Install · Try the data layer · See how calls expand · Read a report

</div>

v1.7: concise setup with remembered choices, conclusions first, expanded daily history and full method-dependency checks. Fresh confirmation is still required. Upgrade details and data-source boundaries.

<!-- readme-section:demo --> <div align="center">

Question → sourced evidence → frozen method stances → Bull/Bear challenge → PM decision + saved audit

Historical UI recording (MP4) · Historical report artifact (SOX, Chinese)

<sub>The recording predates the current 26-seat candidate. It demonstrates an earlier interface only—not current timing, method fidelity, data accuracy, four-code-host validation, or ChatGPT Work validation.</sub>

</div> <!-- readme-section:promise -->

One question. An inspectable case.

AlphaCouncil turns a ticker question into a reviewable research process. Parallel evidence workers gather public sources, selected investment-method seats interpret the same dated facts, bull and bear challenge the case, and a portfolio manager records the decision and its invalidation conditions. Missing inputs stay visible instead of being filled with guesses.

The same repository supports Codex, Claude Code, OpenCode, and Grok Build, plus a tool-only ChatGPT Work developer-mode gateway. It classifies companies, ETFs, and market indices before research so a basket is not analyzed as if it were an operating company.

<!-- readme-section:install -->

Install in Codex

Prerequisite: Node.js 18 or newer. Install with these two shell commands:

codex plugin marketplace add Zhao73/alphacouncil-agent
codex plugin add alphacouncil-agent@alphacouncil

Plugins load when Codex starts. Fully quit and restart Codex, open a new session, then enter this in the Codex composer:

@alphacouncil-agent analyze AAPL

For ChatGPT Work developer mode, Claude Code, OpenCode, Grok Build, Windows, troubleshooting, and the optional global npm command, use the complete install guide.

<!-- readme-section:first-run -->

Free first run

Check the keyless public-data layer before starting a council:

# Codex
@alphacouncil-agent AAPL news

# Claude Code, OpenCode, or Grok Build
/alpha AAPL news

This check starts no council workers and requires no data-vendor key. For bounded research in Codex, use @alphacouncil-agent AAPL quick; on the three slash-command hosts, use /alpha AAPL quick.

<!-- readme-section:call-structure -->

Choose the depth before it runs

AlphaCouncil shows the work plan first. Full research asks separately for method seats, evidence breadth, and depth; the user confirms them before workers start. Full tiers use 15 / 30 / 60 minute ceilings—never a hidden token or currency estimate.

| Run | Model-call structure | Time ceiling | |---|---|---:| | Data check | Keyless tools only; no council workers and no additional model fan-out | Outside the council tiers | | Quick research | 4 evidence workers in parallel → 1–4 method seats in parallel → Bull and Bear in parallel → PM | 10 minutes | | Full — fast | 8 core or exactly 11 all-scope evidence workers start together; each selected method stance is frozen deterministically before one isolated explanation worker; 3 debate rounds → PM | 15 minutes | | Full — normal | Same confirmed roster, frozen-stance sequence, 3 debate rounds, and PM, with a larger depth envelope | 30 minutes | | Full — slow | Same confirmed roster and stages with the largest depth envelope | 60 minutes |

These are queue-to-terminal persistence ceilings, not measured completion times. They guarantee an explicit terminal record even when work is incomplete; a successful live fast run within 15 minutes has not yet been demonstrated across the four hosts. Fast keeps the full contract but uses an auditable stage-aware reasoning profile and one shared lifecycle budget per primary/retry/repair chain; retries cannot silently double a seat's cap.

When an instrument classification and typed-fact coverage are already available, the selector also shows an eight-family advisory method match derived from all 26 physical pack manifests. An explicit objective and holding horizon additionally calibrate the match and separate directional, non-voting risk, and context-only contributions; out_of_scope is never a negative vote. For a one-year directional request, the PM maps sourced base-case total return to one published rating rubric instead of counting conservative method seats; the server binds the frozen price/currency and recomputes that return from a same-currency target plus income. An out_of_scope seat remains visible in the method bench but is structurally absent from the PM rating path. It is only a prefill: every pack remains selectable, no run starts without explicit confirmation, and a missing classification produces no guessed default. The output represents AI-generated method simulations—not human experts, independent models, or a promise of profit. See method-panel recommendation and seat evidence.

Only the slow run with all methods and all evidence workers enables the additional verification path; the other full tiers do not claim that extra check.

<!-- readme-section:benefits -->

What you gain

| Benefit | What it changes | |---|---| | A council, not one answer | Evidence specialists, method seats, opposing cases, and a PM expose where agreement comes from. | | A stance before the story | Each selected full-run method stance is fixed from structured inputs before its isolated explanation is written. | | Claims you can trace | Material report claims must point to source IDs; missing evidence remains a stated gap. | | Disagreement that survives synthesis | Three cross-examination rounds and persisted minority or opposing reports keep the losing case available for review. | | The right research path for the asset | Companies use issuer evidence; ETFs use dated holdings look-through; indices use aggregate methodology. The first data check is keyless. |

<!-- readme-section:comparison -->

How the architecture differs

This compares workflow shapes, not named products. A particular tool may implement a different design.

| Review concern | Single-model reply or common shared-context flow | AlphaCouncil | |---|---|---| | Correlated errors | One shared context can carry an early mistake into every later step | Evidence seats and opposing paths run in isolated workers; they may still use the same provider or model and are not independent models | | Position formation | The position can be composed together with its explanation | A structured stance is frozen before explanatory prose | | Source trace | Traceability depends on the prompt and host | Every material claim is required to carry a source ID | | Minority view | Dissent can be folded into the final summary | Minority and opposing reports remain surfaced as review artifacts |

<!-- readme-section:honesty -->

What the seats are—and are not

The method-seat formulas are AI-authored reconstructions of published methods, pending human review. The named practitioners have not reviewed or endorsed these seats. They are not impersonations, independent models, or validated replicas. A stance is a structured argument to check against its inputs and sources—not a validated investment model.

Current source evidence boundary: 26 provisional method seats, 0 validated method models, 0/8 registered-and-completed canonical evaluation runs, and 0/4 live-host end-to-end runs. Passing source tests does not change those zeros.

<!-- readme-section:disclaimer -->

Disclaimer

AlphaCouncil is for education and research only. It is not investment advice, a recommendation, or a solicitation. AI-generated analysis can be incomplete, outdated, or wrong. Verify the evidence yourself and consult a licensed professional before making an investment decision. The authors accept no liability for losses.

<!-- readme-section:reference-fold -->

Go deeper

Runtime outputs are written outside the repository under ~/.alphacouncil-agent/runs/<run_id>/.

<div align="center"> <img src="assets/logo.png" alt="AlphaCouncil" width="120" />

Evidence first. Disagreement visible. Decisions reviewable.

<a href="#readme-top">↑ Back to top</a>

</div>

Related Skills

View on GitHub
GitHub Stars3
CategoryAI
Updated1mo ago
Forks0

Languages

JavaScript

Trust signals

92/100

From repository metadata: license, adoption, age and documentation. Not a code audit — see the Safety scan above for what the skill file itself contains.

1 low